diff --git a/application_example/maskrcnn/src/dataset/__init__.py b/application_example/maskrcnn/src/dataset/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/application_example/maskrcnn/src/datasets.md b/application_example/maskrcnn/src/datasets.md new file mode 100644 index 0000000000000000000000000000000000000000..e80ec7fa758bb4e5eb54ca14ea7f330b1040dae0 --- /dev/null +++ b/application_example/maskrcnn/src/datasets.md @@ -0,0 +1,7 @@ +. +└─cocodataset + ├─annotations + ├─instance_train2017.json + └─instance_val2017.json + ├─val2017 + └─train2017 \ No newline at end of file diff --git a/application_example/maskrcnn/src/eval.py b/application_example/maskrcnn/src/eval.py index e5a7f9a7b0e53e8f28c53e5f3fe4d9b013de0b07..1bd47f05576747fdd843e483d070f024d51a9102 100644 --- a/application_example/maskrcnn/src/eval.py +++ b/application_example/maskrcnn/src/eval.py @@ -23,10 +23,10 @@ from mindspore import context, Tensor from mindspore.train.serialization import load_checkpoint, load_param_into_net from mindspore.common import set_seed +from utils.config import config from model.mask_rcnn_r50 import MaskRcnnResnet50 # when use maskrcnn mobilenetv1, just change the following backbone # from mask_rcnn_mobilenetv1 -from utils.config import config from utils.util import coco_eval, bbox2result_1image, results2json, get_seg_masks from dataset.dataset import data_to_mindrecord_byte_image, create_coco_dataset @@ -94,7 +94,6 @@ def maskrcnn_eval(dataset_path, ckpt_path, ann_file): segm_results = get_seg_masks(all_mask_fb_tmp_mask, all_bboxes_tmp_mask, all_labels_tmp_mask, img_metas[j], True, config.num_classes) outputs.append((bbox_results, segm_results)) - break eval_types = ["bbox", "segm"] result_files = results2json(dataset_coco, outputs, "./results.pkl") diff --git a/application_example/maskrcnn/src/images/framework.png b/application_example/maskrcnn/src/images/framework.png new file mode 100644 index 0000000000000000000000000000000000000000..c3cd10ba7b68be5a85d6fe16059c4df733ecb204 Binary files /dev/null and b/application_example/maskrcnn/src/images/framework.png differ diff --git a/application_example/maskrcnn/src/images/infer.png b/application_example/maskrcnn/src/images/infer.png new file mode 100644 index 0000000000000000000000000000000000000000..6d4a7e758ca9e84366f1e68eca8730c9d70d5e0d Binary files /dev/null and b/application_example/maskrcnn/src/images/infer.png differ diff --git a/application_example/maskrcnn/src/images/mobilenetv1.png b/application_example/maskrcnn/src/images/mobilenetv1.png new file mode 100644 index 0000000000000000000000000000000000000000..dccdafac99d892149bcf481f1d204f0e9daafefa Binary files /dev/null and b/application_example/maskrcnn/src/images/mobilenetv1.png differ diff --git a/application_example/maskrcnn/src/images/resnet_block.png b/application_example/maskrcnn/src/images/resnet_block.png new file mode 100644 index 0000000000000000000000000000000000000000..35d8ba8bdac9431e7f78e252979c6e822eea4522 Binary files /dev/null and b/application_example/maskrcnn/src/images/resnet_block.png differ diff --git a/application_example/maskrcnn/src/images/roi_align.png b/application_example/maskrcnn/src/images/roi_align.png new file mode 100644 index 0000000000000000000000000000000000000000..9a1ae73bb2feac22c9dc5c43c9392f4a8e4492ff Binary files /dev/null and b/application_example/maskrcnn/src/images/roi_align.png differ diff --git a/application_example/maskrcnn/src/infer.py b/application_example/maskrcnn/src/infer.py index 3f4fc70660e8edadf5f099a83b70512636b02294..3b9ef5055a73683f037f060163d4e7bcac046419 100644 --- a/application_example/maskrcnn/src/infer.py +++ b/application_example/maskrcnn/src/infer.py @@ -27,10 +27,10 @@ from mindspore import context, Tensor from mindspore.train.serialization import load_checkpoint, load_param_into_net from mindspore.common import set_seed +from utils.config import config # when use maskrcnn mobilenetv1, just change the following backbone # from mask_rcnn_mobilenetv1 from model.mask_rcnn_r50 import MaskRcnnResnet50 -from utils.config import config from dataset.dataset import create_coco_dataset set_seed(1) @@ -87,7 +87,7 @@ def random_colors(num, bright=True): List, a list of different colors. """ brightness = 1.0 if bright else 0.7 - hsv = [(i / num, 1, brightness) for i in range(N)] + hsv = [(i / num, 1, brightness) for i in range(num)] colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv)) random.shuffle(colors) return colors @@ -191,7 +191,7 @@ def detection(output, img, img_metas): color = colors[j] i = type_ids[j] # Bounding box - x1, y1, x2, y2 = all_bbox[i]*ratio + x1, y1, x2, y2, _ = all_bbox[i]*ratio score = all_bbox[i, 4] p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2, alpha=0.7, diff --git a/application_example/maskrcnn/src/maskrcnn.ipynb b/application_example/maskrcnn/src/maskrcnn.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..0f86386fceb340094bbe6a4b406b9ed6f41bb31c --- /dev/null +++ b/application_example/maskrcnn/src/maskrcnn.ipynb @@ -0,0 +1,6827 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Mask R-CNN\n", + "\n", + "MaskRCNN是一种概念简单、灵活、通用的目标实例分割框架，在检测出图像中目标的同时，还为每一个实例生成高质量掩码。这种称为Mask R-CNN的方法，通过添加与现有边框检测分支平行的预测目标掩码分支，达到扩展Faster R-CNN的目的。Mask R-CNN训练简单，运行速度达5fps，与Faster R-CNN相比，开销只有小幅上涨。此外，Mask R-CNN易于推广到其他任务。例如，允许在同一框架中预测人体姿势。 Mask R-CNN在COCO挑战赛的三个关键难点上都表现不俗，包括实例分割、边框目标检测和人物关键点检测。Mask R-CNN没有什么华而不实的附加功能，各任务的表现都优于现存所有单模型，包括COCO 2016挑战赛的胜出模型。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 模型简介\n", + "\n", + "MaskRCNN是一个两级目标检测网络，作为FasterRCNN的扩展模型，在现有的边框检测分支的基础上增加了一个预测目标掩码的分支。该网络采用区域候选网络（RPN），可与检测网络共享整个图像\n", + "的卷积特征，无需任何代价就可轻松计算候选区域。整个网络通过共享卷积特征，将RPN和掩码分支合并为一个网络。其模型骨干还可以选择轻量级网络Mobilenet。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 官方库和第三方库的导入\n", + "\n", + "我们首先导入案例依赖的官方库和第三方库。" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "import os\n", + "\n", + "import numpy as np\n", + "import mindspore.nn as nn\n", + "import mindspore.common.dtype as mstype\n", + "from mindspore.ops import operations as P\n", + "from mindspore.ops import functional as F\n", + "from mindspore.ops import composite as C\n", + "from mindspore.nn import layer as L\n", + "from mindspore.common.initializer import initializer\n", + "from mindspore import context, Tensor, Parameter\n", + "from mindspore import ParameterTuple\n", + "from mindspore.train.callback import Callback\n", + "from mindspore.nn.wrap.grad_reducer import DistributedGradReducer\n", + "from mindspore.train.callback import CheckpointConfig, ModelCheckpoint, TimeMonitor\n", + "from mindspore.train import Model\n", + "from mindspore.train.serialization import load_checkpoint, load_param_into_net\n", + "from mindspore.nn import Momentum\n", + "from mindspore.common import set_seed\n", + "\n", + "from utils.config import config" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 数据处理\n", + "\n", + "开始实验之前，请确保本地已经安装了Python环境并安装了MindSpore Vision套件。\n", + "\n", + "### 数据准备\n", + "\n", + "COCO2017是一个广泛应用的数据集，带有边框和像素级背景注释。这些注释可用于场景理解任务，如语义分割，目标检测和图像字幕制作。训练和评估的图像大小为118K和5K。\n", + "\n", + "数据集大小：19G\n", + "\n", + "训练：18G，118,000个图像\n", + "\n", + "评估：1G，5000个图像\n", + "\n", + "注释：241M；包括实例、字幕、人物关键点等\n", + "\n", + "数据格式：图像及JSON文件\n", + "\n", + "注：数据在dataset.py中处理。\n", + "\n", + "首先，你需要下载 coco2017 数据集。\n", + "\n", + "下载完成后，确保你的数据集存放符合如下路径。" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".\n", + "└─cocodataset\n", + " ├─annotations\n", + " ├─instance_train2017.json\n", + " └─instance_val2017.json\n", + " ├─val2017\n", + " └─train2017" + ] + } + ], + "source": [ + "!cat datasets.md" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 数据预处理\n", + "\n", + "原始数据集中图像大小不一致，不方便统一读取和检测。我们首先统一图像大小。数据的注释信息保存在json文件中，我们需要读取出来给图像数据加label。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 数据增强\n", + "\n", + "在你开始训练模型之前。数据增强对于您的数据集以及创建训练数据和测试数据是必要的。对于coco数据集，你可以使用dataset.py为图像添加label，并将它们转换到MindRecord。MindRecord是一种MindSpore指定的数据格式，可以在某些场景下优化MindSpore的性能。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "首先，我们创建MindRecord数据集保存和读取的地址。" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from dataset.dataset import create_coco_dataset, data_to_mindrecord_byte_image\n", + "\n", + "def create_mindrecord_dir(prefix, mindrecord_dir):\n", + " \"\"\"Create MindRecord Direction.\"\"\"\n", + " if not os.path.isdir(mindrecord_dir):\n", + " os.makedirs(mindrecord_dir)\n", + " if config.dataset == \"coco\":\n", + " if os.path.isdir(config.data_root):\n", + " print(\"Create Mindrecord.\")\n", + " data_to_mindrecord_byte_image(\"coco\", True, prefix)\n", + " print(\"Create Mindrecord Done, at {}\".format(mindrecord_dir))\n", + " else:\n", + " raise Exception(\"coco_root not exits.\")\n", + " else:\n", + " if os.path.isdir(config.IMAGE_DIR) and os.path.exists(config.ANNO_PATH):\n", + " print(\"Create Mindrecord.\")\n", + " data_to_mindrecord_byte_image(\"other\", True, prefix)\n", + " print(\"Create Mindrecord Done, at {}\".format(mindrecord_dir))\n", + " else:\n", + " raise Exception(\"IMAGE_DIR or ANNO_PATH not exits.\")\n", + " while not os.path.exists(mindrecord_file+\".db\"):\n", + " time.sleep(5)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "然后，加载数据集，调用dataset.py中的create_coco_dataset函数完成数据预处理和数据增强。" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Start create dataset!\n", + "total images num: 51790\n", + "Create dataset done!\n" + ] + } + ], + "source": [ + "# Allocating memory Environment\n", + "device_target = config.device_target\n", + "rank = 0\n", + "device_num = 1\n", + "context.set_context(mode=context.GRAPH_MODE, device_target=device_target)\n", + "\n", + "print(\"Start create dataset!\")\n", + "# Call the interface for data processing\n", + "# It will generate mindrecord file in config.mindrecord_dir,\n", + "# and the file name is MaskRcnn.mindrecord0, 1, ... file_num.\n", + "prefix = \"MaskRcnn.mindrecord\"\n", + "mindrecord_dir = os.path.join(config.data_root, config.mindrecord_dir)\n", + "mindrecord_file = os.path.join(mindrecord_dir, prefix + \"0\")\n", + "if rank == 0 and not os.path.exists(mindrecord_file):\n", + " create_mindrecord_dir(prefix, mindrecord_dir)\n", + "# When create MindDataset, using the fitst mindrecord file,\n", + "# such as MaskRcnn.mindrecord0.\n", + "dataset = create_coco_dataset(mindrecord_file, batch_size=config.batch_size, device_num=device_num, rank_id=rank)\n", + "dataset_size = dataset.get_dataset_size()\n", + "print(\"total images num: \", dataset_size)\n", + "print(\"Create dataset done!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 数据集可视化\n", + "\n", + "运行以下代码观察数据增强后的图片。可以发现图片经过了旋转处理，并且图片的shape也已经转换为待输入网络的（N，C，H，W）格式，其中N代表样本数量，C代表图片通道，H和W代表图片的高和宽。" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Image shape: (2, 3, 768, 1280)\n" + ] + }, + { + "ename": "ValueError", + "evalue": "Unsupported dtype", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m~/anaconda3/envs/MindSpore/lib/python3.7/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 339\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 340\u001b[0m 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*args, **kwargs)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 39\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/envs/MindSpore/lib/python3.7/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 617\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 618\u001b[0m im, l, b, trans = self.make_image(\n\u001b[0;32m--> 619\u001b[0;31m renderer, renderer.get_image_magnification())\n\u001b[0m\u001b[1;32m 620\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mim\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 621\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/envs/MindSpore/lib/python3.7/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36mmake_image\u001b[0;34m(self, renderer, magnification, unsampled)\u001b[0m\n\u001b[1;32m 879\u001b[0m return self._make_image(\n\u001b[1;32m 880\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_A\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransformed_bbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagnification\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 881\u001b[0;31m unsampled=unsampled)\n\u001b[0m\u001b[1;32m 882\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 883\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_check_unsampled_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/envs/MindSpore/lib/python3.7/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_make_image\u001b[0;34m(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border)\u001b[0m\n\u001b[1;32m 502\u001b[0m \u001b[0m_interpd_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_interpolation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 503\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_resample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 504\u001b[0;31m self.get_filternorm(), self.get_filterrad())\n\u001b[0m\u001b[1;32m 505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 506\u001b[0m \u001b[0;31m#resample rgb channels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: Unsupported dtype" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "show_data = next(dataset.create_dict_iterator())\n", + "\n", + "show_images = show_data[\"image\"].asnumpy()\n", + "print(f'Image shape: {show_images.shape}')\n", + "\n", + "plt.figure()\n", + "\n", + "# 展示2张图片供参考\n", + "for i in range(1, 3):\n", + " plt.subplot(1, 2, i)\n", + "\n", + " # 将图片转换HWC格式\n", + " image_trans = np.transpose(show_images[i - 1], (1, 2, 0))\n", + " image_trans = np.clip(image_trans, 0, 1)\n", + "\n", + " plt.imshow(image_trans[:, :], cmap=None)\n", + " plt.xticks(rotation=180)\n", + " plt.axis(\"off\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 构建网络\n", + "\n", + "![image1](images/framework.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "前文提到Mask RCNN的模型骨干采用ResNet50（原文），通过添加与现有边框检测分支平行的预测目标掩模分支实现扩展Faster R-CNN，完成目标检测。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 骨干网络\n", + "\n", + "Mask R-CNN骨干网络的选择：ResNet, VGG, Mobilenet等。本项目中，使用了对ResNet为骨干的Mask RCNN进行了框架迁移。以及扩展了Mobilenet这种轻量级网络。\n", + "\n", + "骨干网络：" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Resnet（Deep residual network, ResNet），深度残差神经网络，卷积神经网络历史在具有划时代意义的神经网络。与Alexnet和VGG不同的是，网络结构上就有很大的改变，在大家为了提升卷积神经网络的性能在不断提升网络深度的时候，大家发现随着网络深度的提升，网络的效果变得越来越差，甚至出现了网络的退化问题，80层的网络比30层的效果还差，深度网络存在的梯度消失和爆炸问题越来越严重，这使得训练一个优异的深度学习模型变得更加艰难，在这种情况下，网络残差模块可以有效消除梯度消失和梯度爆炸问题。\n", + "\n", + "![image2](images/resnet_block.png)\n", + "\n", + "2. Mobilenetv1是一种轻量级的深度卷积网络，MobileNet的基本单元是深度级可分离卷积（depthwise separable convolution），将标准卷积分成两步。第一步 Depthwise convolution（DW）,也即逐通道的卷积，一个卷积核负责一个通道，一个通道只被一个卷积核“滤波”，则卷积核个数和通道数个数相同；第二步，Pointwise convolution（PW），将depthwise convolution得到的结果通过1x1卷积，再“串”起来。这样其实整体效果和一个标准卷积是差不多的，但是会大大减少计算量和模型参数量。其网络结构如下。\n", + "\n", + "![image3](images/mobilenetv1.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "原文中，使用Resnet为骨干网络。这里，我们也选择Resnet50作为骨干网络执行案例。" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import mindspore.nn as nn\n", + "import mindspore.common.dtype as mstype\n", + "from mindspore.ops import operations as P\n", + "from mindspore.common.tensor import Tensor\n", + "from mindspore.ops import functional as F\n", + "from mindspore import context\n", + "\n", + "if context.get_context(\"device_target\") == \"Ascend\":\n", + " ms_cast_type = mstype.float16\n", + "else:\n", + " ms_cast_type = mstype.float32\n", + "\n", + "\n", + "def weight_init_ones(shape):\n", + " \"\"\"\n", + " Weight init.\n", + "\n", + " Args:\n", + " shape(List): weights shape.\n", + "\n", + " Returns:\n", + " Tensor, weights, default float32.\n", + " \"\"\"\n", + " return Tensor(np.array(np.ones(shape).astype(np.float32) * 0.01).astype(np.float32))\n", + "\n", + "\n", + "def _conv(in_channels, out_channels, kernel_size=3, stride=1, padding=0, pad_mode='pad'):\n", + " \"\"\"\n", + " Conv2D wrapper.\n", + "\n", + " Args:\n", + " in_channels (int): The channel number of the input tensor of the Conv2d layer.\n", + " out_channels (int): The channel number of the output tensor of the Conv2d layer.\n", + " kernel_size (Union[int, tuple[int]]): Specifies the height and width of the 2D convolution kernel.\n", + " The data type is an integer or a tuple of two integers. An integer represents the height\n", + " and width of the convolution kernel. A tuple of two integers represents the height\n", + " and width of the convolution kernel respectively. Default: 3.\n", + " stride (Union[int, tuple[int]]): The movement stride of the 2D convolution kernel.\n", + " The data type is an integer or a tuple of two integers. An integer represents the movement step size\n", + " in both height and width directions. A tuple of two integers represents the movement step size in the height\n", + " and width directions respectively. Default: 1.\n", + " padding (Union[int, tuple[int]]): The number of padding on the height and width directions of the input.\n", + " The data type is an integer or a tuple of four integers. If `padding` is an integer,\n", + " then the top, bottom, left, and right padding are all equal to `padding`.\n", + " If `padding` is a tuple of 4 integers, then the top, bottom, left, and right padding\n", + " is equal to `padding[0]`, `padding[1]`, `padding[2]`, and `padding[3]` respectively.\n", + " The value should be greater than or equal to 0. Default: 0.\n", + " pad_mode (str): Specifies padding mode. The optional values are\n", + " \"same\", \"valid\", \"pad\". Default: \"pad\".\n", + "\n", + " Outputs:\n", + " Tensor, math '(N, C_{out}, H_{out}, W_{out})' or math '(N, H_{out}, W_{out}, C_{out})'.\n", + " \"\"\"\n", + " shape = (out_channels, in_channels, kernel_size, kernel_size)\n", + " weights = weight_init_ones(shape)\n", + " return nn.Conv2d(in_channels, out_channels,\n", + " kernel_size=kernel_size, stride=stride, padding=padding,\n", + " pad_mode=pad_mode, weight_init=weights, has_bias=False).to_float(mstype.float32)\n", + "\n", + "\n", + "def _batch_norm2d_init(out_chls, momentum=0.1, affine=True, use_batch_statistics=True):\n", + " \"\"\"\n", + " Batchnorm2D wrapper.\n", + "\n", + " Args:\n", + " out_cls (int): The number of channels of the input tensor. Expected input size is (N, C, H, W),\n", + " `C` represents the number of channels\n", + " momentum (float): A floating hyperparameter of the momentum for the\n", + " running_mean and running_var computation. Default: 0.1.\n", + " affine (bool): A bool value. When set to True, gamma and beta can be learned. Default: True.\n", + " use_batch_statistics (bool):\n", + "\n", + " - If true, use the mean value and variance value of current batch data and track running mean\n", + " and running variance. Default: True.\n", + " - If false, use the mean value and variance value of specified value, and not track statistical value.\n", + " - If None, the use_batch_statistics is automatically set to true or false according to the training\n", + " and evaluation mode. During training, the parameter is set to true, and during evaluation, the\n", + " parameter is set to false.\n", + " Outputs:\n", + " Tensor, the normalized, scaled, offset tensor, of shape :math:'(N, C_{out}, H_{out}, W_{out})'.\n", + " \"\"\"\n", + " gamma_init = Tensor(np.array(np.ones(out_chls)).astype(np.float32))\n", + " beta_init = Tensor(np.array(np.ones(out_chls) * 0).astype(np.float32))\n", + " moving_mean_init = Tensor(np.array(np.ones(out_chls) * 0).astype(np.float32))\n", + " moving_var_init = Tensor(np.array(np.ones(out_chls)).astype(np.float32))\n", + "\n", + " return nn.BatchNorm2d(out_chls, momentum=momentum, affine=affine, gamma_init=gamma_init,\n", + " beta_init=beta_init, moving_mean_init=moving_mean_init,\n", + " moving_var_init=moving_var_init,\n", + " use_batch_statistics=use_batch_statistics)\n", + "\n", + "\n", + "class ResNetFea(nn.Cell):\n", + " \"\"\"\n", + " ResNet architecture.\n", + "\n", + " Args:\n", + " block (Tensor): Block for network.\n", + " layer_nums (list): Numbers of block in different layers.\n", + " in_channels (list): Input channel in each layer.\n", + " out_channels (list): Output channel in each layer.\n", + " weights_update (bool): Weight update flag.\n", + "\n", + " Inputs:\n", + " - **x** (Tensor) - Input block.\n", + "\n", + " Outputs:\n", + " Tensor, output block.\n", + "\n", + " Support Plarforms:\n", + " ``Ascend`` ``CPU`` ``GPU``\n", + "\n", + " Examples:\n", + " >>> ResNetFea(ResidualBlockUsing, [3, 4, 6, 3], [64, 256, 512, 1024], [256, 512, 1024, 2048], False)\n", + " \"\"\"\n", + " def __init__(self, block, layer_nums, in_channels, out_channels, weights_update=False):\n", + " super(ResNetFea, self).__init__()\n", + "\n", + " if not len(layer_nums) == len(in_channels) == len(out_channels) == 4:\n", + " raise ValueError(\"the length of \"\n", + " \"layer_num, inchannel, outchannel list must be 4!\")\n", + "\n", + " bn_training = False\n", + " self.conv1 = _conv(3, 64, kernel_size=7, stride=2, padding=3, pad_mode='pad')\n", + " self.bn1 = _batch_norm2d_init(64, affine=bn_training, use_batch_statistics=bn_training)\n", + " self.relu = P.ReLU()\n", + " self.maxpool = P.MaxPool(kernel_size=3, strides=2, pad_mode=\"SAME\")\n", + " self.weights_update = weights_update\n", + "\n", + " if not self.weights_update:\n", + " self.conv1.weight.requires_grad = False\n", + "\n", + " self.layer1 = self._make_layer(block, layer_nums[0], in_channel=in_channels[0],\n", + " out_channel=out_channels[0], stride=1, training=bn_training,\n", + " weights_update=self.weights_update)\n", + " self.layer2 = self._make_layer(block, layer_nums[1], in_channel=in_channels[1],\n", + " out_channel=out_channels[1], stride=2,\n", + " training=bn_training, weights_update=True)\n", + " self.layer3 = self._make_layer(block, layer_nums[2], in_channel=in_channels[2],\n", + " out_channel=out_channels[2], stride=2,\n", + " training=bn_training, weights_update=True)\n", + " self.layer4 = self._make_layer(block, layer_nums[3], in_channel=in_channels[3],\n", + " out_channel=out_channels[3], stride=2,\n", + " training=bn_training, weights_update=True)\n", + "\n", + " def _make_layer(self, block, layer_num, in_channel, out_channel, stride, training=False, weights_update=False):\n", + " \"\"\"\n", + " Make layer for resnet backbone.\n", + "\n", + " Args:\n", + " block (Tensor): ResNet block.\n", + " layer_num (int): Layer number.\n", + " in_channel (int): Input channel.\n", + " out_channel (int): Output channel.\n", + " stride (int): Stride size for convolutional layer.\n", + " training(bool): Whether to do training. Default: False.\n", + " weights_update(bool): Whether to update weights. Default: False.\n", + "\n", + " Returns:\n", + " SequentialCell, Combine several layers toghter.\n", + "\n", + " Examples:\n", + " >>> _make_layer(InvertedResidual, 4, 64, 64, 1)\n", + " \"\"\"\n", + " layers = []\n", + " down_sample = False\n", + " if stride != 1 or in_channel != out_channel:\n", + " down_sample = True\n", + " resblk = block(in_channel, out_channel, stride=stride, down_sample=down_sample,\n", + " training=training, weights_update=weights_update)\n", + " layers.append(resblk)\n", + "\n", + " for _ in range(1, layer_num):\n", + " resblk = block(out_channel, out_channel, stride=1, training=training, weights_update=weights_update)\n", + " layers.append(resblk)\n", + "\n", + " return nn.SequentialCell(layers)\n", + "\n", + " def construct(self, x):\n", + " \"\"\"Construct ResNet architecture.\"\"\"\n", + " x = self.conv1(x)\n", + " x = self.bn1(x)\n", + " x = self.relu(x)\n", + " c1 = self.maxpool(x)\n", + "\n", + " c2 = self.layer1(c1)\n", + " identity = c2\n", + " if not self.weights_update:\n", + " identity = F.stop_gradient(c2)\n", + " c3 = self.layer2(identity)\n", + " c4 = self.layer3(c3)\n", + " c5 = self.layer4(c4)\n", + "\n", + " return identity, c3, c4, c5\n", + "\n", + "\n", + "class ResidualBlockUsing(nn.Cell):\n", + " \"\"\"\n", + " ResNet V1 residual block definition.\n", + "\n", + " Args:\n", + " in_channels (int): Input channel.\n", + " out_channels (int): Output channel.\n", + " stride (int): Stride size for the initial convolutional layer. Default: 1.\n", + " down_sample (bool): If to do the downsample in block. Default: False.\n", + " momentum (float): Momentum for batchnorm layer. Default: 0.1.\n", + " training (bool): Training flag. Default: False.\n", + " weights_updata (bool): Weights update flag. Default: False.\n", + "\n", + " Inputs:\n", + " - **x** (Tensor) - Input block.\n", + "\n", + " Outputs:\n", + " Tensor, output block.\n", + "\n", + " Support Plarforms:\n", + " ``Ascend`` ``CPU`` ``GPU``\n", + "\n", + " Examples:\n", + " ResidualBlockUsing(3, 256, stride=2, down_sample=True)\n", + " \"\"\"\n", + " expansion = 4\n", + "\n", + " def __init__(self, in_channels, out_channels, stride=1, down_sample=False,\n", + " momentum=0.1, training=False, weights_update=False):\n", + " super(ResidualBlockUsing, self).__init__()\n", + "\n", + " self.affine = weights_update\n", + "\n", + " out_chls = out_channels // self.expansion\n", + " self.conv1 = _conv(in_channels, out_chls, kernel_size=1, stride=1, padding=0)\n", + " self.bn1 = _batch_norm2d_init(out_chls, momentum=momentum, affine=self.affine, use_batch_statistics=training)\n", + "\n", + " self.conv2 = _conv(out_chls, out_chls, kernel_size=3, stride=stride, padding=1)\n", + " self.bn2 = _batch_norm2d_init(out_chls, momentum=momentum, affine=self.affine, use_batch_statistics=training)\n", + "\n", + " self.conv3 = _conv(out_chls, out_channels, kernel_size=1, stride=1, padding=0)\n", + " self.bn3 = _batch_norm2d_init(out_channels, momentum=momentum, affine=self.affine,\n", + " use_batch_statistics=training)\n", + "\n", + " if training:\n", + " self.bn1 = self.bn1.set_train()\n", + " self.bn2 = self.bn2.set_train()\n", + " self.bn3 = self.bn3.set_train()\n", + "\n", + " if not weights_update:\n", + " self.conv1.weight.requires_grad = False\n", + " self.conv2.weight.requires_grad = False\n", + " self.conv3.weight.requires_grad = False\n", + "\n", + " self.relu = P.ReLU()\n", + " self.downsample = down_sample\n", + " if self.downsample:\n", + " self.conv_down_sample = _conv(in_channels, out_channels, kernel_size=1, stride=stride, padding=0)\n", + " self.bn_down_sample = _batch_norm2d_init(out_channels, momentum=momentum, affine=self.affine,\n", + " use_batch_statistics=training)\n", + " if training:\n", + " self.bn_down_sample = self.bn_down_sample.set_train()\n", + " if not weights_update:\n", + " self.conv_down_sample.weight.requires_grad = False\n", + " self.add = P.Add()\n", + "\n", + " def construct(self, x):\n", + " \"\"\"Construct ResNet V1 residual block.\"\"\"\n", + " identity = x\n", + "\n", + " out = self.conv1(x)\n", + " out = self.bn1(out)\n", + " out = self.relu(out)\n", + "\n", + " out = self.conv2(out)\n", + " out = self.bn2(out)\n", + " out = self.relu(out)\n", + "\n", + " out = self.conv3(out)\n", + " out = self.bn3(out)\n", + "\n", + " if self.downsample:\n", + " identity = self.conv_down_sample(identity)\n", + " identity = self.bn_down_sample(identity)\n", + "\n", + " out = self.add(out, identity)\n", + " out = self.relu(out)\n", + "\n", + " return out" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### FPN网络\n", + "\n", + "FPN网络（Feature Pyramid Network）同时利用低层特征高分辨率和高层特征的高语义信息，通过融合这些不同层的特征达到预测的效果。并且预测是在每个融合后的特征层上单独进行的，这和常规的特征融合方式不同。\n", + "\n", + "骨干网络和FPN网络结合构成了Mask RCNN网络的卷积层。" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def bias_init_zeros(shape):\n", + " \"\"\"Bias init method.\"\"\"\n", + " result = Tensor(np.array(np.zeros(shape).astype(np.float32)), dtype=mstype.float32)\n", + " return result\n", + "\n", + "\n", + "def _conv(in_channels, out_channels, kernel_size=3, stride=1, padding=0, pad_mode='pad'):\n", + " \"\"\"\n", + " Conv2D wrapper.\n", + "\n", + " Args:\n", + " in_channels(int): Input channel num.\n", + " out_channels(int): Output channel num.\n", + " kernel_size(int): Kernel size. Default: 1.\n", + " stride(int): Stride. Default: 1.\n", + " padding(int): Padding range. Default: 0.\n", + " pad_mode(bool): Padding model. Default: 'pad'.\n", + " gain(int): Gain. Default: 1.\n", + "\n", + " Returns:\n", + " Tensor, Convoluted result.\n", + " \"\"\"\n", + " shape = (out_channels, in_channels, kernel_size, kernel_size)\n", + " weights = initializer(\"XavierUniform\", shape=shape, dtype=mstype.float32)\n", + " shape_bias = (out_channels,)\n", + " biass = bias_init_zeros(shape_bias)\n", + " return nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding,\n", + " pad_mode=pad_mode, weight_init=weights, has_bias=True, bias_init=biass)\n", + "\n", + "\n", + "class FeatPyramidNeck(nn.Cell):\n", + " \"\"\"\n", + " Feature pyramid network cell, usually uses as network neck.\n", + "\n", + " Applies the convolution on multiple, input feature maps\n", + " and output feature map with same channel size. if required num of\n", + " output larger then num of inputs, add extra maxpooling for further\n", + " downsampling;\n", + "\n", + " Args:\n", + " in_channels (tuple): Channel size of input feature maps.\n", + " out_channels (int): Channel size output.\n", + " num_outs (int): Num of output features.\n", + " Inputs:\n", + " - **x** (Tensor) - Input variant\n", + "\n", + " Outputs:\n", + " Tuple, with tensors of same channel size.\n", + "\n", + " Support Platform:\n", + " ``Ascend`` ``CPU`` ``GPU``\n", + "\n", + " Examples:\n", + " >>> neck = FeatPyramidNeck([100,200,300], 50, 4)\n", + " >>> input_data = (normal(0,0.1,(1,c,1280//(4*2**i), 768//(4*2**i)),\n", + " ... dtype=np.float32) for i, c in enumerate(config.fpn_in_channels))\n", + " >>> out = neck(input_data)\n", + " \"\"\"\n", + "\n", + " def __init__(self,\n", + " in_channels,\n", + " out_channels,\n", + " num_outs):\n", + " super(FeatPyramidNeck, self).__init__()\n", + "\n", + " if context.get_context(\"device_target\") == \"Ascend\":\n", + " self.cast_type = mstype.float16\n", + " else:\n", + " self.cast_type = mstype.float32\n", + "\n", + " self.num_outs = num_outs\n", + " self.in_channels = in_channels\n", + " self.fpn_layer = len(self.in_channels)\n", + "\n", + " assert not self.num_outs < len(in_channels)\n", + "\n", + " self.lateral_convs_list_ = []\n", + " self.fpn_convs_ = []\n", + "\n", + " for _, channel in enumerate(in_channels):\n", + " l_conv = _conv(channel, out_channels, kernel_size=1, stride=1, padding=0,\n", + " pad_mode='valid').to_float(self.cast_type)\n", + " fpn_conv = _conv(out_channels, out_channels, kernel_size=3, stride=1, padding=0,\n", + " pad_mode='same').to_float(self.cast_type)\n", + " self.lateral_convs_list_.append(l_conv)\n", + " self.fpn_convs_.append(fpn_conv)\n", + " self.lateral_convs_list = nn.layer.CellList(self.lateral_convs_list_)\n", + " self.fpn_convs_list = nn.layer.CellList(self.fpn_convs_)\n", + " self.interpolate1 = P.ResizeBilinear((48, 80))\n", + " self.interpolate2 = P.ResizeBilinear((96, 160))\n", + " self.interpolate3 = P.ResizeBilinear((192, 320))\n", + " self.cast = P.Cast()\n", + " self.maxpool = P.MaxPool(kernel_size=1, strides=2, pad_mode=\"same\")\n", + "\n", + " def construct(self, inputs):\n", + " \"\"\"construction of Feature Pyramid Neck.\"\"\"\n", + " layers = ()\n", + " for i in range(self.fpn_layer):\n", + " layers += (self.lateral_convs_list[i](inputs[i]),)\n", + "\n", + " cast_layers = (layers[3],)\n", + " cast_layers = \\\n", + " cast_layers + (layers[2] + self.cast(self.interpolate1(cast_layers[self.fpn_layer - 4]), self.cast_type),)\n", + " cast_layers = \\\n", + " cast_layers + (layers[1] + self.cast(self.interpolate2(cast_layers[self.fpn_layer - 3]), self.cast_type),)\n", + " cast_layers = \\\n", + " cast_layers + (layers[0] + self.cast(self.interpolate3(cast_layers[self.fpn_layer - 2]), self.cast_type),)\n", + "\n", + " layers_arranged = ()\n", + " for i in range(self.fpn_layer - 1, -1, -1):\n", + " layers_arranged = layers_arranged + (cast_layers[i],)\n", + "\n", + " outs = ()\n", + " for i in range(self.fpn_layer):\n", + " outs = outs + (self.fpn_convs_list[i](layers_arranged[i]),)\n", + "\n", + " for i in range(self.num_outs - self.fpn_layer):\n", + " outs = outs + (self.maxpool(outs[3]),)\n", + " return outs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### RPN网络\n", + "\n", + "RPN第一次出现在世人眼中是在Faster RCNN这个结构中，专门用来提取候选框，在RCNN和Fast RCNN等物体检测架构中，用来提取候选框的方法通常是Selective Search，是比较传统的方法，而且比较耗时，在CPU上要2s一张图。所以作者提出RPN，专门用来提取候选框，一方面RPN耗时少，另一方面RPN可以很容易结合到Fast RCNN中，称为一个整体。\n", + "\n", + "RPN网络主要输出项：\n", + "\n", + "1. ROI：对应在特征层每个特征点产生4k个变量，其中4表示[dy, dx, dh, dw]四个边框平移缩放量。其中k表示4个边框，k=4。\n", + "\n", + "2. scores：对应在特征层每个特征点产生2k个变量，其中2表示前景和北京概率。其中k表示3个边框，k=3。" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from model.bbox_assign_sample import BboxAssignSample\n", + "\n", + "\n", + "class RpnRegClsBlock(nn.Cell):\n", + " \"\"\"\n", + " Rpn reg cls block for rpn layer\n", + "\n", + " Args:\n", + " in_channels (int): Input channels of shared convolution.\n", + " feat_channels (int): Output channels of shared convolution.\n", + " num_anchors (int): The anchor number.\n", + " cls_out_channels (int): Output channels of classification convolution.\n", + " weight_conv (Tensor): Weight init for rpn conv.\n", + " bias_conv (Tensor): Bias init for rpn conv.\n", + " weight_cls (Tensor): Weight init for rpn cls conv.\n", + " bias_cls (Tensor): Bias init for rpn cls conv.\n", + " weight_reg (Tensor): Weight init for rpn reg conv.\n", + " bias_reg (Tensor): Bias init for rpn reg conv.\n", + "\n", + " Inputs:\n", + " - **x** (Tensor) - input variant\n", + "\n", + " Outputs:\n", + " Tensor, output tensor.\n", + "\n", + " Support Platform:\n", + " ``Ascend`` ``CPU`` ``GPU``\n", + "\n", + " Examples:\n", + " >>> x = Tensor(np.array([[[[1., 2.], [3., 4.]]]]), mindspore.float32)\n", + " >>> weight_conv = Tensor(np.array([[[[0.2, 0.3], [0.4, 0.1]]]]), mindspore.float32)\n", + " >>> bias_conv = Tensor(np.array([[[[0., 0.], [0., 0.]]]]), mindspore.float32)\n", + " >>> weight_cls = Tensor(np.array([[[[0.2, 0.3], [0.4, 0.1]]]]), mindspore.float32)\n", + " >>> bias_cls = Tensor(np.array([[[[0., 0.], [0., 0.]]]]), mindspore.float32)\n", + " >>> weight_reg = Tensor(np.array([[[[0.2, 0.3], [0.4, 0.1]]]]), mindspore.float32)\n", + " >>> bias_reg = Tensor(np.array([[[[0., 0.], [0., 0.]]]]), mindspore.float32)\n", + " >>> rpn = RpnRegClsBlock(2, 2, 4, 4, )\n", + " >>> rpn = ops.SingleRoIExtractor(2, 2, 0.5, 2, weight_conv, bias_conv,\n", + " ... weight_cls, bias_cls, weight_reg, bias_reg)\n", + " >>> output = rpn(x)\n", + " \"\"\"\n", + " def __init__(self, in_channels, feat_channels, num_anchors, cls_out_channels, weight_conv,\n", + " bias_conv, weight_cls, bias_cls, weight_reg, bias_reg):\n", + " super(RpnRegClsBlock, self).__init__()\n", + " self.rpn_conv = nn.Conv2d(in_channels, feat_channels, kernel_size=3,\n", + " stride=1, pad_mode='same',\n", + " has_bias=True, weight_init=weight_conv,\n", + " bias_init=bias_conv)\n", + " self.relu = nn.ReLU()\n", + "\n", + " self.rpn_cls = nn.Conv2d(feat_channels, num_anchors * cls_out_channels,\n", + " kernel_size=1, pad_mode='valid',\n", + " has_bias=True, weight_init=weight_cls,\n", + " bias_init=bias_cls)\n", + " self.rpn_reg = nn.Conv2d(feat_channels, num_anchors * 4,\n", + " kernel_size=1, pad_mode='valid',\n", + " has_bias=True, weight_init=weight_reg,\n", + " bias_init=bias_reg)\n", + "\n", + " def construct(self, x):\n", + " \"\"\"Construct Rpn reg cls block for rpn layer.\"\"\"\n", + " x = self.relu(self.rpn_conv(x))\n", + "\n", + " x1 = self.rpn_cls(x)\n", + " x2 = self.rpn_reg(x)\n", + "\n", + " return x1, x2\n", + "\n", + "\n", + "class RPN(nn.Cell):\n", + " \"\"\"\n", + " ROI proposal network..\n", + "\n", + " Args:\n", + " config (dict): Config.\n", + " batch_size (int): Batchsize.\n", + " in_channels (int): Input channels of shared convolution.\n", + " feat_channels (int): Output channels of shared convolution.\n", + " num_anchors (int): The anchor number.\n", + " cls_out_channels (int): Output channels of classification convolution.\n", + "\n", + " Inputs:\n", + " - **inputs** (Tensor) - Input variant.\n", + " - **img_metas** (Tensor) - Img shape.\n", + " - **anchor_list** (Tensor) - A list of anchors.\n", + " - **gt_bboxes** (Tensor) - Ground truth bounding boxes.\n", + " - **gt_labels** (Tensor) - Ground truth labels.\n", + " - **gt_valids** (Tensor) - Ground truth validations.\n", + "\n", + " Outputs:\n", + " Tuple, tuple of output tensor.\n", + "\n", + " Support Platform:\n", + " ``Ascend`` ``CPU`` ``GPU``\n", + "\n", + " Examples:\n", + " >>> RPN(config=config, batch_size=2, in_channels=256, feat_channels=1024,\n", + " ... num_anchors=3, cls_out_channels=512)\n", + " \"\"\"\n", + " def __init__(self, config, batch_size, in_channels, feat_channels, num_anchors, cls_out_channels):\n", + " super(RPN, self).__init__()\n", + " cfg_rpn = config\n", + "\n", + " if context.get_context(\"device_target\") == \"Ascend\":\n", + " self.cast_type = mstype.float16\n", + " self.np_cast_type = np.float16\n", + " else:\n", + " self.cast_type = mstype.float32\n", + " self.np_cast_type = np.float32\n", + "\n", + " self.num_bboxes = cfg_rpn.num_bboxes\n", + " self.slice_index = ()\n", + " self.feature_anchor_shape = ()\n", + " self.slice_index += (0,)\n", + " index = 0\n", + " for shape in cfg_rpn.feature_shapes:\n", + " self.slice_index += (self.slice_index[index] + shape[0] * shape[1] * num_anchors,)\n", + " self.feature_anchor_shape += (shape[0] * shape[1] * num_anchors * batch_size,)\n", + " index += 1\n", + "\n", + " self.num_anchors = num_anchors\n", + " self.batch_size = batch_size\n", + " self.test_batch_size = cfg_rpn.test_batch_size\n", + " self.num_layers = 5\n", + " self.real_ratio = Tensor(np.ones((1, 1)).astype(self.np_cast_type))\n", + "\n", + " self.rpn_convs_list = nn.layer.CellList(self._make_rpn_layer(self.num_layers, in_channels, feat_channels,\n", + " num_anchors, cls_out_channels))\n", + "\n", + " self.transpose = P.Transpose()\n", + " self.reshape = P.Reshape()\n", + " self.concat = P.Concat(axis=0)\n", + " self.fill = P.Fill()\n", + " self.placeh1 = Tensor(np.ones((1,)).astype(self.np_cast_type))\n", + "\n", + " self.trans_shape = (0, 2, 3, 1)\n", + "\n", + " self.reshape_shape_reg = (-1, 4)\n", + " self.reshape_shape_cls = (-1,)\n", + " self.rpn_loss_reg_weight = Tensor(np.array(cfg_rpn.rpn_loss_reg_weight).astype(self.np_cast_type))\n", + " self.rpn_loss_cls_weight = Tensor(np.array(cfg_rpn.rpn_loss_cls_weight).astype(self.np_cast_type))\n", + " expected_total_size = cfg_rpn.num_expected_neg * self.batch_size\n", + " self.num_expected_total = Tensor(np.array(expected_total_size).astype(self.np_cast_type))\n", + " self.num_bboxes = cfg_rpn.num_bboxes\n", + " self.get_targets = BboxAssignSample(cfg_rpn, self.batch_size, self.num_bboxes, False)\n", + " self.check_valid = P.CheckValid()\n", + " self.sum_loss = P.ReduceSum()\n", + " self.loss_cls = P.SigmoidCrossEntropyWithLogits()\n", + " self.loss_bbox = P.SmoothL1Loss(beta=1.0/9.0)\n", + " self.squeeze = P.Squeeze()\n", + " self.cast = P.Cast()\n", + " self.tile = P.Tile()\n", + " self.zeros_like = P.ZerosLike()\n", + " self.loss = Tensor(np.zeros((1,)).astype(self.np_cast_type))\n", + " self.clsloss = Tensor(np.zeros((1,)).astype(self.np_cast_type))\n", + " self.regloss = Tensor(np.zeros((1,)).astype(self.np_cast_type))\n", + "\n", + " def _make_rpn_layer(self, num_layers, in_channels,\n", + " feat_channels, num_anchors, cls_out_channels):\n", + " \"\"\"\n", + " Make rpn layer for rpn proposal network\n", + "\n", + " Args:\n", + " num_layers (int): layer num.\n", + " in_channels (int): Input channels of shared convolution.\n", + " feat_channels (int): Output channels of shared convolution.\n", + " num_anchors (int): The anchor number.\n", + " cls_out_channels (int): Output channels of classification convolution.\n", + "\n", + " Returns:\n", + " List, list of RpnRegClsBlock cells.\n", + " \"\"\"\n", + " rpn_layer = []\n", + "\n", + " shp_weight_conv = (feat_channels, in_channels, 3, 3)\n", + " shp_bias_conv = (feat_channels,)\n", + " weight_conv = initializer('Normal', shape=shp_weight_conv, dtype=mstype.float32)\n", + " bias_conv = initializer(0, shape=shp_bias_conv, dtype=mstype.float32)\n", + "\n", + " shp_weight_cls = (num_anchors * cls_out_channels, feat_channels, 1, 1)\n", + " shp_bias_cls = (num_anchors * cls_out_channels,)\n", + " weight_cls = initializer('Normal', shape=shp_weight_cls, dtype=mstype.float32)\n", + " bias_cls = initializer(0, shape=shp_bias_cls, dtype=mstype.float32)\n", + "\n", + " shp_weight_reg = (num_anchors * 4, feat_channels, 1, 1)\n", + " shp_bias_reg = (num_anchors * 4,)\n", + " weight_reg = initializer('Normal', shape=shp_weight_reg, dtype=mstype.float32)\n", + " bias_reg = initializer(0, shape=shp_bias_reg, dtype=mstype.float32)\n", + "\n", + " for i in range(num_layers):\n", + " rpn_layer.append(RpnRegClsBlock(in_channels, feat_channels, num_anchors, cls_out_channels, weight_conv,\n", + " bias_conv, weight_cls, bias_cls, weight_reg,\n", + " bias_reg).to_float(self.cast_type))\n", + "\n", + " for i in range(1, num_layers):\n", + " rpn_layer[i].rpn_conv.weight = rpn_layer[0].rpn_conv.weight\n", + " rpn_layer[i].rpn_cls.weight = rpn_layer[0].rpn_cls.weight\n", + " rpn_layer[i].rpn_reg.weight = rpn_layer[0].rpn_reg.weight\n", + "\n", + " rpn_layer[i].rpn_conv.bias = rpn_layer[0].rpn_conv.bias\n", + " rpn_layer[i].rpn_cls.bias = rpn_layer[0].rpn_cls.bias\n", + " rpn_layer[i].rpn_reg.bias = rpn_layer[0].rpn_reg.bias\n", + "\n", + " return rpn_layer\n", + "\n", + " def construct(self, inputs, img_metas, anchor_list, gt_bboxes, gt_labels, gt_valids):\n", + " \"\"\"Construct ROI Proposal Network.\"\"\"\n", + " loss_print = ()\n", + " rpn_cls_score = ()\n", + " rpn_bbox_pred = ()\n", + " rpn_cls_score_total = ()\n", + " rpn_bbox_pred_total = ()\n", + "\n", + " for i in range(self.num_layers):\n", + " x1, x2 = self.rpn_convs_list[i](inputs[i])\n", + "\n", + " rpn_cls_score_total = rpn_cls_score_total + (x1,)\n", + " rpn_bbox_pred_total = rpn_bbox_pred_total + (x2,)\n", + "\n", + " x1 = self.transpose(x1, self.trans_shape)\n", + " x1 = self.reshape(x1, self.reshape_shape_cls)\n", + "\n", + " x2 = self.transpose(x2, self.trans_shape)\n", + " x2 = self.reshape(x2, self.reshape_shape_reg)\n", + "\n", + " rpn_cls_score = rpn_cls_score + (x1,)\n", + " rpn_bbox_pred = rpn_bbox_pred + (x2,)\n", + "\n", + " loss = self.loss\n", + " clsloss = self.clsloss\n", + " regloss = self.regloss\n", + " bbox_targets = ()\n", + " bbox_weights = ()\n", + " labels = ()\n", + " label_weights = ()\n", + "\n", + " output = ()\n", + " if self.training:\n", + " for i in range(self.batch_size):\n", + " multi_level_flags = ()\n", + " anchor_list_tuple = ()\n", + "\n", + " for j in range(self.num_layers):\n", + " res = self.cast(self.check_valid(anchor_list[j], self.squeeze(img_metas[i:i + 1:1, ::])),\n", + " mstype.int32)\n", + " multi_level_flags = multi_level_flags + (res,)\n", + " anchor_list_tuple = anchor_list_tuple + (anchor_list[j],)\n", + "\n", + " valid_flag_list = self.concat(multi_level_flags)\n", + " anchor_using_list = self.concat(anchor_list_tuple)\n", + "\n", + " gt_bboxes_i = self.squeeze(gt_bboxes[i:i + 1:1, ::])\n", + " gt_labels_i = self.squeeze(gt_labels[i:i + 1:1, ::])\n", + " gt_valids_i = self.squeeze(gt_valids[i:i + 1:1, ::])\n", + "\n", + " bbox_target, bbox_weight, label, label_weight = \\\n", + " self.get_targets(gt_bboxes_i, gt_labels_i, self.cast(valid_flag_list, mstype.bool_),\n", + " anchor_using_list, gt_valids_i)\n", + "\n", + " bbox_weight = self.cast(bbox_weight, self.cast_type)\n", + " label = self.cast(label, self.cast_type)\n", + " label_weight = self.cast(label_weight, self.cast_type)\n", + "\n", + " for j in range(self.num_layers):\n", + " begin = self.slice_index[j]\n", + " end = self.slice_index[j + 1]\n", + " stride = 1\n", + " bbox_targets += (bbox_target[begin:end:stride, ::],)\n", + " bbox_weights += (bbox_weight[begin:end:stride],)\n", + " labels += (label[begin:end:stride],)\n", + " label_weights += (label_weight[begin:end:stride],)\n", + "\n", + " for i in range(self.num_layers):\n", + " bbox_target_using = ()\n", + " bbox_weight_using = ()\n", + " label_using = ()\n", + " label_weight_using = ()\n", + "\n", + " for j in range(self.batch_size):\n", + " bbox_target_using += (bbox_targets[i + (self.num_layers * j)],)\n", + " bbox_weight_using += (bbox_weights[i + (self.num_layers * j)],)\n", + " label_using += (labels[i + (self.num_layers * j)],)\n", + " label_weight_using += (label_weights[i + (self.num_layers * j)],)\n", + "\n", + " bbox_target_with_batchsize = self.concat(bbox_target_using)\n", + " bbox_weight_with_batchsize = self.concat(bbox_weight_using)\n", + " label_with_batchsize = self.concat(label_using)\n", + " label_weight_with_batchsize = self.concat(label_weight_using)\n", + "\n", + " # stop\n", + " bbox_target_ = F.stop_gradient(bbox_target_with_batchsize)\n", + " bbox_weight_ = F.stop_gradient(bbox_weight_with_batchsize)\n", + " label_ = F.stop_gradient(label_with_batchsize)\n", + " label_weight_ = F.stop_gradient(label_weight_with_batchsize)\n", + "\n", + " cls_score_i = rpn_cls_score[i]\n", + " reg_score_i = rpn_bbox_pred[i]\n", + "\n", + " loss_cls = self.loss_cls(cls_score_i, label_)\n", + " loss_cls_item = loss_cls * label_weight_\n", + " loss_cls_item = self.sum_loss(loss_cls_item, (0,)) / self.num_expected_total\n", + "\n", + " loss_reg = self.loss_bbox(reg_score_i, bbox_target_)\n", + " bbox_weight_ = self.tile(self.reshape(bbox_weight_, (self.feature_anchor_shape[i], 1)), (1, 4))\n", + " loss_reg = loss_reg * bbox_weight_\n", + " loss_reg_item = self.sum_loss(loss_reg, (1,))\n", + " loss_reg_item = self.sum_loss(loss_reg_item, (0,)) / self.num_expected_total\n", + "\n", + " loss_total = self.rpn_loss_cls_weight * loss_cls_item + self.rpn_loss_reg_weight * loss_reg_item\n", + "\n", + " loss += loss_total\n", + " loss_print += (loss_total, loss_cls_item, loss_reg_item)\n", + " clsloss += loss_cls_item\n", + " regloss += loss_reg_item\n", + "\n", + " output = (loss, rpn_cls_score_total, rpn_bbox_pred_total,\n", + " clsloss, regloss, loss_print)\n", + " else:\n", + " output = (self.placeh1, rpn_cls_score_total, rpn_bbox_pred_total,\n", + " self.placeh1, self.placeh1, self.placeh1)\n", + " return output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ROI Align\n", + "\n", + "ROI Align可以计算不同proposal对应到不同尺度下的特征，利用proposal对该特征进行剪裁、resize、pooling提取特征。\n", + "\n", + "Mask-RCNN中使用的ROI Level校准:\n", + "\n", + "$$\n", + "k=[k_0+\\log_2{(\\frac{\\sqrt{wh}}{224/\\sqrt{image\\; area}})}]\n", + "$$\n", + "\n", + "#### 解释\n", + "\n", + "1. 由于Mask R-CNN训练数据的box和anchor都做了调整，所以ROI Level的计算部分也需要 $224/\\sqrt{image\\; area}$。其中，224应为输入图像尺寸的一半。\n", + "\n", + "2. 计算得到的k即为ROI对应的level，level一共4个:\n", + "\n", + " 1. $level=2$表示映射回特征 $P_{2}$，大小为原输入图像的 $1/4$。\n", + "\n", + " 2. $level=3$表示映射回特征 $P_{3}$，大小为原输入图像的 $1/8$。\n", + "\n", + " 3. $level=4$表示映射回特征 $P_{4}$，大小为原输入图像的 $1/16$。\n", + "\n", + " 4. $level=5$表示映射回特征 $P_{5}$，大小为原输入图像的 $1/32$。\n", + "\n", + "![image4](images/roi_align.png)\n", + "\n", + "虚线网格表示特征图，实线表示RoI(在本例中为2×2个bin)，点表示每个容器中的4个采样点。RoIAlign通过双线性插值从特征图上附近的网格点(最近的4个)计算每个采样点的值。在ROI、4个bin或采样点中涉及的任何坐标上都不进行量化。" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "class ROIAlign(nn.Cell):\n", + " \"\"\"\n", + " Extract RoI features from mulitiple feature map.\n", + "\n", + " Args:\n", + " out_size_h (int): RoI height.\n", + " out_size_w (int): RoI width.\n", + " spatial_scale (int): RoI spatial scale.\n", + " sample_num (int): RoI sample number. Default: 0.\n", + " roi_align_mode (int): RoI align mode. Default: 1.\n", + "\n", + " Inputs:\n", + " - **features** (Tensor) - The input features, whose shape must be :math:'(N, C, H, W)'.\n", + " - **rois** (Tensor) - The shape is :math:'(rois_n, 5)'. With data type of float16 or float32.\n", + "\n", + " Outputs:\n", + " Tensor, the shape is :math: '(rois_n, C, pooled_height, pooled_width)'.\n", + "\n", + " Support Platform:\n", + " ``Ascend`` ``CPU`` ``GPU``\n", + "\n", + " Examples:\n", + " >>> features = Tensor(np.array([[[[1., 2.], [3., 4.]]]]), mindspore.float32)\n", + " >>> rois = Tensor(np.array([[0, 0.2, 0.3, 0.2, 0.3]]), mindspore.float32)\n", + " >>> roi_align = ops.ROIAlign(2, 2, 0.5, 2)\n", + " >>> output = roi_align(features, rois)\n", + " >>> print(output)\n", + " [[[[1.775 2.025]\n", + " [2.275 2.525]]]]\n", + " \"\"\"\n", + " def __init__(self, out_size_h, out_size_w, spatial_scale, sample_num=0, roi_align_mode=1):\n", + " super(ROIAlign, self).__init__()\n", + "\n", + " self.out_size = (out_size_h, out_size_w)\n", + " self.spatial_scale = float(spatial_scale)\n", + " self.sample_num = int(sample_num)\n", + " self.align_op = P.ROIAlign(self.out_size[0], self.out_size[1],\n", + " self.spatial_scale, self.sample_num,\n", + " roi_align_mode)\n", + "\n", + " def construct(self, features, rois):\n", + " \"\"\"Construct ROI Align\"\"\"\n", + " return self.align_op(features, rois)\n", + "\n", + " def __repr__(self):\n", + " format_str = self.__class__.__name__\n", + " format_str += \\\n", + " '(out_size={}, spatial_scale={}, sample_num={}'.format(self.out_size, self.spatial_scale, self.sample_num)\n", + " return format_str\n", + "\n", + "\n", + "class SingleRoIExtractor(nn.Cell):\n", + " \"\"\"\n", + " Extract RoI features from a single level feature map.\n", + "\n", + " If there are multiple input feature levels, each RoI is mapped to a level according to its scale.\n", + "\n", + " Args:\n", + " config (dict): Config\n", + " out_channels (int): Output channels of RoI layers.\n", + " featmap_strides (int): Strides of input feature maps.\n", + " batch_size (int): Batchsize. Default: 1.\n", + " finest_scale (int): Scale threshold of mapping to level 0. Default: 56.\n", + " mask (bool): Specify ROIAlign for cls or mask branch. Default: False.\n", + "\n", + " Inputs:\n", + " - **rois** (Tensor) - The shape is :math:'(rois_n, 5)'. With data type of float16 or float32.\n", + " - **feat1** (Tensor) - The input features, whose shape must be :math:'(N, C, H, W)'.\n", + " - **feat2** (Tensor) - The input features, whose shape must be :math:'(N, C, H, W)'.\n", + " - **feat3** (Tensor) - The input features, whose shape must be :math:'(N, C, H, W)'.\n", + " - **feat4** (Tensor) - The input features, whose shape must be :math:'(N, C, H, W)'.\n", + "\n", + " Outputs:\n", + " Tensor, the shape is :math:'(rois_n, C, pooled_height, pooled_width)'.\n", + "\n", + " Support Platform:\n", + " ``Ascend`` ``CPU`` ``GPU``\n", + "\n", + " Examples:\n", + " >>> fea1 = Tensor(np.array([[[[1., 2.], [3., 4.]]]]), mindspore.float32)\n", + " >>> fea2 = Tensor(np.array([[[[1., 2.], [3., 4.]]]]), mindspore.float32)\n", + " >>> fea3 = Tensor(np.array([[[[1., 2.], [3., 4.]]]]), mindspore.float32)\n", + " >>> fea4 = Tensor(np.array([[[[1., 2.], [3., 4.]]]]), mindspore.float32)\n", + " >>> rois = Tensor(np.array([[0, 0.2, 0.3, 0.2, 0.3]]), mindspore.float32)\n", + " >>> single_roi = ops.SingleRoIExtractor(conifg, 2, 1, 2, 2, mask)\n", + " >>> output = single_roi(rois, fea1, fea2, fea3, fea4)\n", + " \"\"\"\n", + "\n", + " def __init__(self, config, roi_layer, out_channels, featmap_strides, batch_size=1, finest_scale=56, mask=False):\n", + " super(SingleRoIExtractor, self).__init__()\n", + " cfg = config\n", + " self.train_batch_size = batch_size\n", + " self.out_channels = out_channels\n", + " self.featmap_strides = featmap_strides\n", + " self.num_levels = len(self.featmap_strides)\n", + " self.out_size = roi_layer.mask_out_size if mask else roi_layer.out_size\n", + " self.mask = mask\n", + " self.sample_num = roi_layer.sample_num\n", + " self.roi_layers = self.build_roi_layers(self.featmap_strides)\n", + " self.roi_layers = L.CellList(self.roi_layers)\n", + "\n", + " self.sqrt = P.Sqrt()\n", + " self.log = P.Log()\n", + " self.finest_scale_ = finest_scale\n", + " self.clamp = C.clip_by_value\n", + "\n", + " self.cast = P.Cast()\n", + " self.equal = P.Equal()\n", + " self.select = P.Select()\n", + "\n", + " in_mode_16 = False\n", + " self.dtype = np.float16 if in_mode_16 else np.float32\n", + " self.ms_dtype = mstype.float16 if in_mode_16 else mstype.float32\n", + " self.set_train_local(cfg, training=True)\n", + "\n", + " def set_train_local(self, config, training=True):\n", + " \"\"\"Set training flag.\"\"\"\n", + " self.training_local = training\n", + "\n", + " cfg = config\n", + " # Init tensor\n", + " roi_sample_num = cfg.num_expected_pos_stage2 if self.mask else cfg.roi_sample_num\n", + " self.batch_size = roi_sample_num if self.training_local else cfg.rpn_max_num\n", + " self.batch_size = self.train_batch_size*self.batch_size \\\n", + " if self.training_local else cfg.test_batch_size*self.batch_size\n", + " self.ones = Tensor(np.array(np.ones((self.batch_size, 1)), dtype=self.dtype))\n", + " finest_scale = np.array(np.ones((self.batch_size, 1)), dtype=self.dtype) * self.finest_scale_\n", + " self.finest_scale = Tensor(finest_scale)\n", + " self.epslion = Tensor(np.array(np.ones((self.batch_size, 1)), dtype=self.dtype)*self.dtype(1e-6))\n", + " self.zeros = Tensor(np.array(np.zeros((self.batch_size, 1)), dtype=np.int32))\n", + " self.max_levels = Tensor(np.array(np.ones((self.batch_size, 1)), dtype=np.int32)*(self.num_levels-1))\n", + " self.twos = Tensor(np.array(np.ones((self.batch_size, 1)), dtype=self.dtype) * 2)\n", + " self.res_ = Tensor(np.array(np.zeros((self.batch_size, self.out_channels, self.out_size, self.out_size)),\n", + " dtype=self.dtype))\n", + "\n", + " def num_inputs(self):\n", + " \"\"\"input number.\"\"\"\n", + " return len(self.featmap_strides)\n", + "\n", + " def log2(self, value):\n", + " \"\"\"calculate log2.\"\"\"\n", + " return self.log(value) / self.log(self.twos)\n", + "\n", + " def build_roi_layers(self, featmap_strides):\n", + " \"\"\"build ROI layers.\"\"\"\n", + " roi_layers = []\n", + " for s in featmap_strides:\n", + " layer_cls = ROIAlign(self.out_size, self.out_size, spatial_scale=1 / s,\n", + " sample_num=self.sample_num, roi_align_mode=0)\n", + " roi_layers.append(layer_cls)\n", + " return roi_layers\n", + "\n", + " def _c_map_roi_levels(self, rois):\n", + " \"\"\"Map rois to corresponding feature levels by scales.\n", + "\n", + " - scale < finest_scale * 2: level 0\n", + " - finest_scale * 2 <= scale < finest_scale * 4: level 1\n", + " - finest_scale * 4 <= scale < finest_scale * 8: level 2\n", + " - scale >= finest_scale * 8: level 3\n", + "\n", + " Args:\n", + " rois (Tensor): Input RoIs, shape (k, 5).\n", + " num_levels (int): Total level number.\n", + "\n", + " Returns:\n", + " Tensor, Level index (0-based) of each RoI, shape (k, )\n", + " \"\"\"\n", + " scale = self.sqrt(rois[::, 3:4:1] - rois[::, 1:2:1] + self.ones) * \\\n", + " self.sqrt(rois[::, 4:5:1] - rois[::, 2:3:1] + self.ones)\n", + "\n", + " target_lvls = self.log2(scale / self.finest_scale + self.epslion)\n", + " target_lvls = P.Floor()(target_lvls)\n", + " target_lvls = self.cast(target_lvls, mstype.int32)\n", + " target_lvls = self.clamp(target_lvls, self.zeros, self.max_levels)\n", + "\n", + " return target_lvls\n", + "\n", + " def construct(self, rois, feat1, feat2, feat3, feat4):\n", + " \"\"\"Construct Single RoI Extractor\"\"\"\n", + " feats = (feat1, feat2, feat3, feat4)\n", + " res = self.res_\n", + " target_lvls = self._c_map_roi_levels(rois)\n", + " for i in range(self.num_levels):\n", + " mask = self.equal(target_lvls, P.ScalarToArray()(i))\n", + " mask = P.Reshape()(mask, (-1, 1, 1, 1))\n", + " roi_feats_t = self.roi_layers[i](feats[i], rois)\n", + " mask = \\\n", + " self.cast(P.Tile()(self.cast(mask, mstype.int32), (1, 256, self.out_size, self.out_size)), mstype.bool_)\n", + " res = self.select(mask, roi_feats_t, res)\n", + "\n", + " return res" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Class/Bounding Box预测\n", + "\n", + "Class/bounding box预测时，RPN输出一系列ROI，RoIAlign将ROI逐个对应会Resnet输出的5个特征层中的一个。再对该特征做相应的裁剪，resize操作得到对应的特征。再对该特征做进一步卷积，全连接最终输出预测。" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "class DenseNoTranpose(nn.Cell):\n", + " \"\"\"\n", + " Dense method\n", + "\n", + " Args:\n", + " input_channels (int): Channel size of input feature maps.\n", + " output_channels (int): Channel size output.\n", + " weight_init (tuple): Initialized values of weights.\n", + "\n", + " Inputs:\n", + " - **x** (Tensor) - Input from the upper layer.\n", + "\n", + " Outputs:\n", + " Tensor, dense result.\n", + "\n", + " Support Platforms:\n", + " ``Ascend`` ``CPU`` ``GPU``\n", + "\n", + " Examples:\n", + " >>> out_channels = 128\n", + " >>> dense_notranspose = DenseNoTranpose(input_channels, output_channels, weights)\n", + " \"\"\"\n", + " def __init__(self, input_channels, output_channels, weight_init):\n", + " super(DenseNoTranpose, self).__init__()\n", + " self.weight = Parameter(initializer(weight_init, [input_channels, output_channels], mstype.float32))\n", + " self.bias = Parameter(initializer(\"zeros\", [output_channels], mstype.float32))\n", + " self.matmul = P.MatMul(transpose_b=False)\n", + " self.bias_add = P.BiasAdd()\n", + "\n", + " def construct(self, x):\n", + " \"\"\"Construct Dense No Transpose.\"\"\"\n", + " output = self.bias_add(self.matmul(x, self.weight), self.bias)\n", + " return output\n", + "\n", + "\n", + "class FpnCls(nn.Cell):\n", + " \"\"\"\n", + " Dense layer of classification and box head\n", + "\n", + " Args:\n", + " input_channels (int): Channel size of input feature maps.\n", + " output_channels (int): Channel size output\n", + " num_classes (int): Number of classes.\n", + " pool_size (int): Pooling size.\n", + "\n", + " Inputs:\n", + " - **x** (Tensor) - Input from the upper layer.\n", + "\n", + " Outputs:\n", + " Tensor, dense result.\n", + "\n", + " Support Platforms:\n", + " ``Ascend`` ``CPU`` ``GPU``\n", + "\n", + " Examples:\n", + " >>> fpn_cls = FpnCls(256,128,81,2)\n", + " \"\"\"\n", + " def __init__(self, input_channels, output_channels, num_classes, pool_size):\n", + " super(FpnCls, self).__init__()\n", + "\n", + " if context.get_context(\"device_target\") == \"Ascend\":\n", + " self.cast_type = mstype.float16\n", + " else:\n", + " self.cast_type = mstype.float32\n", + "\n", + " representation_size = input_channels * pool_size * pool_size\n", + " shape_0 = (output_channels, representation_size)\n", + " weights_0 = initializer(\"XavierUniform\", shape=shape_0[::-1], dtype=mstype.float32)\n", + " shape_1 = (output_channels, output_channels)\n", + " weights_1 = initializer(\"XavierUniform\", shape=shape_1[::-1], dtype=mstype.float32)\n", + " self.shared_fc_0 = DenseNoTranpose(representation_size, output_channels, weights_0).to_float(self.cast_type)\n", + " self.shared_fc_1 = DenseNoTranpose(output_channels, output_channels, weights_1).to_float(self.cast_type)\n", + "\n", + " cls_weight = initializer('Normal', shape=[num_classes, output_channels][::-1], dtype=mstype.float32)\n", + " reg_weight = initializer('Normal', shape=[num_classes * 4, output_channels][::-1], dtype=mstype.float32)\n", + " self.cls_scores = DenseNoTranpose(output_channels, num_classes, cls_weight).to_float(self.cast_type)\n", + " self.reg_scores = DenseNoTranpose(output_channels, num_classes * 4, reg_weight).to_float(self.cast_type)\n", + "\n", + " self.relu = P.ReLU()\n", + " self.flatten = P.Flatten()\n", + "\n", + " def construct(self, x):\n", + " \"\"\"Construct FPNCls\"\"\"\n", + " # two share fc layer\n", + " x = self.flatten(x)\n", + "\n", + " x = self.relu(self.shared_fc_0(x))\n", + " x = self.relu(self.shared_fc_1(x))\n", + "\n", + " # classifier head\n", + " cls_scores = self.cls_scores(x)\n", + " # bbox head\n", + " reg_scores = self.reg_scores(x)\n", + "\n", + " return cls_scores, reg_scores\n", + "\n", + "\n", + "class RcnnCls(nn.Cell):\n", + " \"\"\"\n", + " Rcnn for classification and box regression subnet.\n", + "\n", + " Args:\n", + " config (dict): Config.\n", + " batch_size (int): Batchsize.\n", + " num_classes (int): Class number.\n", + " target_means (list): Means for encode function. Default: (.0, .0, .0, .0]).\n", + " target_stds (list): Stds for encode function. Default: (0.1, 0.1, 0.2, 0.2).\n", + "\n", + " Inputs:\n", + " - **featuremap** (tuple) - Feature map.\n", + " - **bbox_targets** (tuple) - A set of bounding box targets.\n", + " - **labels** (tuple) - Ground truth labels.\n", + " - **mask** (tuple) - Mask array.\n", + "\n", + " Outputs:\n", + " Tuple, tuple of output tensor.\n", + "\n", + " Support Platforms:\n", + " ``Ascend`` ``CPU`` ``GPU``\n", + "\n", + " Examples:\n", + " >>> RcnnCls(config=config, representation_size = 1024,\n", + " ... batch_size=2, num_classes = 81,\n", + " ... target_means=(0., 0., 0., 0.),\n", + " ... target_stds=(0.1, 0.1, 0.2, 0.2))\n", + " \"\"\"\n", + "\n", + " def __init__(self, config, batch_size, num_classes, target_means=(0., 0., 0., 0.),\n", + " target_stds=(0.1, 0.1, 0.2, 0.2)):\n", + " super(RcnnCls, self).__init__()\n", + " cfg = config\n", + "\n", + " if context.get_context(\"device_target\") == \"Ascend\":\n", + " self.cast_type = mstype.float16\n", + " self.np_cast_type = np.float16\n", + " else:\n", + " self.cast_type = mstype.float32\n", + " self.np_cast_type = np.float32\n", + " self.eps = 1e-5\n", + "\n", + " self.rcnn_loss_cls_weight = Tensor(np.array(cfg.rcnn_loss_cls_weight).astype(self.np_cast_type))\n", + " self.rcnn_loss_reg_weight = Tensor(np.array(cfg.rcnn_loss_reg_weight).astype(self.np_cast_type))\n", + " self.rcnn_fc_out_channels = cfg.rcnn_fc_out_channels\n", + " self.target_means = target_means\n", + " self.target_stds = target_stds\n", + " self.num_classes = num_classes\n", + " self.in_channels = cfg.rcnn_in_channels\n", + " self.train_batch_size = batch_size\n", + " self.test_batch_size = cfg.test_batch_size\n", + "\n", + " self.fpn_cls = FpnCls(self.in_channels, self.rcnn_fc_out_channels, self.num_classes, cfg.roi_layer.out_size)\n", + " self.relu = P.ReLU()\n", + " self.logicaland = P.LogicalAnd()\n", + " self.loss_cls = P.SoftmaxCrossEntropyWithLogits()\n", + " self.loss_bbox = P.SmoothL1Loss(beta=1.0)\n", + " self.loss_mask = P.SigmoidCrossEntropyWithLogits()\n", + " self.reshape = P.Reshape()\n", + " self.onehot = P.OneHot()\n", + " self.greater = P.Greater()\n", + " self.cast = P.Cast()\n", + " self.sum_loss = P.ReduceSum()\n", + " self.tile = P.Tile()\n", + " self.expandims = P.ExpandDims()\n", + "\n", + " self.gather = P.GatherNd()\n", + " self.argmax = P.ArgMaxWithValue(axis=1)\n", + "\n", + " self.on_value = Tensor(1.0, mstype.float32)\n", + " self.off_value = Tensor(0.0, mstype.float32)\n", + " self.value = Tensor(1.0, self.cast_type)\n", + "\n", + " self.num_bboxes = (cfg.num_expected_pos_stage2 + cfg.num_expected_neg_stage2) * batch_size\n", + "\n", + " rmv_first = np.ones((self.num_bboxes, self.num_classes))\n", + " rmv_first[:, 0] = np.zeros((self.num_bboxes,))\n", + " self.rmv_first_tensor = Tensor(rmv_first.astype(self.np_cast_type))\n", + "\n", + " self.num_bboxes_test = cfg.rpn_max_num * cfg.test_batch_size\n", + "\n", + " def construct(self, featuremap, bbox_targets, labels, mask):\n", + " \"\"\"Construct Rcnn for classification\"\"\"\n", + " x_cls, x_reg = self.fpn_cls(featuremap)\n", + "\n", + " if self.training:\n", + " bbox_weights = self.cast(self.logicaland(self.greater(labels, 0), mask), mstype.int32) * labels\n", + " labels = self.cast(self.onehot(labels, self.num_classes, self.on_value, self.off_value), self.cast_type)\n", + " bbox_targets = self.tile(self.expandims(bbox_targets, 1), (1, self.num_classes, 1))\n", + "\n", + " loss_cls, loss_reg = self.loss(x_cls, x_reg, bbox_targets, bbox_weights, labels, mask)\n", + " out = (loss_cls, loss_reg)\n", + " else:\n", + " out = (x_cls, x_reg)\n", + "\n", + " return out\n", + "\n", + " def loss(self, cls_score, bbox_pred, bbox_targets, bbox_weights, labels, weights):\n", + " \"\"\"\n", + " Loss method.\n", + " Args:\n", + " cls_score(Array): Classificaiton scores.\n", + " bbox_pred(Array): Bounding box prediction.\n", + " bbox_targets(Array): Bounding box GT target.\n", + " bbox_weights(Array): Bounding box weights.\n", + " labels(Array): GT labels.\n", + " weights(Array): GT wieghts.\n", + "\n", + " Returns:\n", + " loss_cls, float, classification loss.\n", + " loss_reg, float, regression loss.\n", + " \"\"\"\n", + " # loss_cls\n", + " loss_cls, _ = self.loss_cls(cls_score, labels)\n", + " weights = self.cast(weights, self.cast_type)\n", + " loss_cls = loss_cls * weights\n", + " loss_cls = self.sum_loss(loss_cls, (0,)) / (self.sum_loss(weights, (0,)) + self.eps)\n", + "\n", + " # loss_reg\n", + " bbox_weights = self.cast(self.onehot(bbox_weights, self.num_classes, self.on_value, self.off_value),\n", + " self.cast_type)\n", + " bbox_weights = bbox_weights * self.rmv_first_tensor\n", + " pos_bbox_pred = self.reshape(bbox_pred, (self.num_bboxes, -1, 4))\n", + " loss_reg = self.loss_bbox(pos_bbox_pred, bbox_targets)\n", + " loss_reg = self.sum_loss(loss_reg, (2,))\n", + " loss_reg = loss_reg * bbox_weights\n", + " loss_reg = loss_reg / (self.sum_loss(weights, (0,)) + self.eps)\n", + " loss_reg = self.sum_loss(loss_reg, (0, 1))\n", + "\n", + " return loss_cls, loss_reg" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Mask预测\n", + "\n", + "对RoIAlign输出的特征进行一系列的卷积，转置卷积，最后输出mask的预测结果。" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def _conv(in_channels, out_channels, kernel_size=1, stride=1, padding=0, pad_mode='pad', gain=1):\n", + " \"\"\"\n", + " Conv2D wrapper.\n", + "\n", + " Args:\n", + " in_channels(int): Input channel num.\n", + " out_channels(int): Output channel num.\n", + " kernel_size(int): Kernel size. Default: 1\n", + " stride(int): Stride. Default: 1\n", + " padding(int): Padding range. Default: 0\n", + " pad_mode(bool): Padding model. Default: 'pad'\n", + " gain(int): Gain. Default: 1\n", + "\n", + " Returns:\n", + " Tensor, Convoluted result.\n", + " \"\"\"\n", + " shape = (out_channels, in_channels, kernel_size, kernel_size)\n", + " # xavier_normal\n", + " fan_in = in_channels * kernel_size * kernel_size\n", + " fan_out = out_channels * kernel_size * kernel_size\n", + " std = gain * (2 / (fan_in + fan_out)) ** 0.5\n", + " weights = Tensor(np.random.normal(loc=0.0, scale=std, size=shape).astype(np.float32))\n", + " shape_bias = (out_channels,)\n", + " bias = Tensor(np.array(np.zeros(shape_bias)).astype(np.float32))\n", + " return nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding,\n", + " pad_mode=pad_mode, weight_init=weights, has_bias=True, bias_init=bias)\n", + "\n", + "\n", + "def _conv_transpose(in_channels, out_channels, kernel_size=1, stride=1, padding=0, pad_mode='pad', gain=1):\n", + " \"\"\"\n", + " ConvTranspose wrapper.\n", + "\n", + " Args:\n", + " in_channels(int): Input channel num.\n", + " out_channels(int): Output channel num.\n", + " kernel_size(int): Kernel size. Default: 1\n", + " stride(int): Stride. Default: 1\n", + " padding(int): Padding range. Default: 0\n", + " pad_mode(bool): Padding model. Default: 'pad'\n", + " gain(int): Gain. Default: 1\n", + "\n", + " Returns:\n", + " Tensor, Convoluted Transposed result.\n", + " \"\"\"\n", + " shape = (out_channels, in_channels, kernel_size, kernel_size)\n", + " # xavier_normal\n", + " fan_in = in_channels * kernel_size * kernel_size\n", + " fan_out = out_channels * kernel_size * kernel_size\n", + " std = gain * (2 / (fan_in + fan_out)) ** 0.5\n", + " weights = Tensor(np.random.normal(loc=0.0, scale=std, size=shape).astype(np.float32))\n", + " shape_bias = (out_channels,)\n", + " bias = Tensor(np.array(np.zeros(shape_bias)).astype(np.float32))\n", + " return nn.Conv2dTranspose(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding,\n", + " pad_mode=pad_mode, weight_init=weights, has_bias=True, bias_init=bias)\n", + "\n", + "\n", + "class FpnMask(nn.Cell):\n", + " \"\"\"\n", + " Conv layers of mask head\n", + "\n", + " Args:\n", + " input_channels (int): Channel size of input feature maps.\n", + " output_channels (int): Channel size output\n", + " num_classes (int): Number of classes.\n", + "\n", + " Inputs:\n", + " - **x** (Tensor) - Input from the upper layer.\n", + "\n", + " Outputs:\n", + " Tuple, tuple of output tensor.\n", + "\n", + " Support Platforms:\n", + " ``Ascend`` ``CPU`` ``GPU``\n", + "\n", + " Examples:\n", + " >>> FpnMask(input_channels=256, output_channels=256, num_classes=81)\n", + " \"\"\"\n", + " def __init__(self, input_channels, output_channels, num_classes):\n", + " super(FpnMask, self).__init__()\n", + "\n", + " if context.get_context(\"device_target\") == \"Ascend\":\n", + " self.cast_type = mstype.float16\n", + " else:\n", + " self.cast_type = mstype.float32\n", + "\n", + " self.mask_conv1 = _conv(input_channels, output_channels,\n", + " kernel_size=3, gain=2 ** 0.5,\n", + " pad_mode=\"same\").to_float(self.cast_type)\n", + " self.mask_relu1 = P.ReLU()\n", + "\n", + " self.mask_conv2 = _conv(output_channels, output_channels,\n", + " kernel_size=3, gain=2 ** 0.5,\n", + " pad_mode=\"same\").to_float(self.cast_type)\n", + " self.mask_relu2 = P.ReLU()\n", + "\n", + " self.mask_conv3 = _conv(output_channels, output_channels,\n", + " kernel_size=3, gain=2 ** 0.5,\n", + " pad_mode=\"same\").to_float(self.cast_type)\n", + " self.mask_relu3 = P.ReLU()\n", + "\n", + " self.mask_conv4 = _conv(output_channels, output_channels,\n", + " kernel_size=3, gain=2 ** 0.5,\n", + " pad_mode=\"same\").to_float(self.cast_type)\n", + " self.mask_relu4 = P.ReLU()\n", + "\n", + " self.mask_deconv5 = _conv_transpose(output_channels, output_channels, kernel_size=2, gain=2 ** 0.5,\n", + " stride=2, pad_mode=\"valid\").to_float(self.cast_type)\n", + " self.mask_relu5 = P.ReLU()\n", + " self.mask_conv6 = _conv(output_channels, num_classes, kernel_size=1, stride=1, gain=2,\n", + " pad_mode=\"valid\").to_float(self.cast_type)\n", + "\n", + " def construct(self, x):\n", + " \"\"\"Construct convolutional layers of mask heads. \"\"\"\n", + " x = self.mask_conv1(x)\n", + " x = self.mask_relu1(x)\n", + "\n", + " x = self.mask_conv2(x)\n", + " x = self.mask_relu2(x)\n", + "\n", + " x = self.mask_conv3(x)\n", + " x = self.mask_relu3(x)\n", + "\n", + " x = self.mask_conv4(x)\n", + " x = self.mask_relu4(x)\n", + "\n", + " x = self.mask_deconv5(x)\n", + " x = self.mask_relu5(x)\n", + "\n", + " x = self.mask_conv6(x)\n", + "\n", + " return x\n", + "\n", + "\n", + "class RcnnMask(nn.Cell):\n", + " \"\"\"\n", + " Rcnn for mask subnet.\n", + "\n", + " Args:\n", + " config (dict): Config.\n", + " batch_size (int): Batchsize.\n", + " num_classes (int): Class number.\n", + " target_means (list): Means for encode function. Default: (.0, .0, .0, .0]).\n", + " target_stds (list): Stds for encode function. Default: (0.1, 0.1, 0.2, 0.2).\n", + "\n", + " Inputs:\n", + " - **mask_featuremap** (tuple) - Masked feature map\n", + " - **labels** (tuple) - Ground truth labels. Default: None\n", + " - **mask** (tuple) - Mask map. Default: None\n", + " - **mask_fb_targets** (tuple) - Masked targets. Default: None\n", + "\n", + " Outputs:\n", + " Tuple, tuple of output tensor.\n", + "\n", + " Examples:\n", + " >>> RcnnMask(config=config, representation_size = 1024,\n", + " ... batch_size=2, num_classes = 81,\n", + " ... target_means=(0., 0., 0., 0.),\n", + " ... target_stds=(0.1, 0.1, 0.2, 0.2))\n", + " \"\"\"\n", + "\n", + " def __init__(self, config, batch_size, num_classes, target_means=(0., 0., 0., 0.),\n", + " target_stds=(0.1, 0.1, 0.2, 0.2)):\n", + " super(RcnnMask, self).__init__()\n", + " cfg = config\n", + "\n", + " if context.get_context(\"device_target\") == \"Ascend\":\n", + " self.cast_type = mstype.float16\n", + " self.np_cast_type = np.float16\n", + " else:\n", + " self.cast_type = mstype.float32\n", + " self.np_cast_type = np.float32\n", + " self.eps = 1e-5\n", + "\n", + " self.rcnn_loss_mask_fb_weight = Tensor(np.array(cfg.rcnn_loss_mask_fb_weight).astype(self.np_cast_type))\n", + " self.rcnn_mask_out_channels = cfg.rcnn_mask_out_channels\n", + " self.target_means = target_means\n", + " self.target_stds = target_stds\n", + " self.num_classes = num_classes\n", + " self.in_channels = cfg.rcnn_in_channels\n", + "\n", + " self.fpn_mask = FpnMask(self.in_channels, self.rcnn_mask_out_channels, self.num_classes)\n", + "\n", + " self.logicaland = P.LogicalAnd()\n", + " self.loss_mask = P.SigmoidCrossEntropyWithLogits()\n", + " self.onehot = P.OneHot()\n", + " self.greater = P.Greater()\n", + " self.cast = P.Cast()\n", + " self.sum_loss = P.ReduceSum()\n", + " self.tile = P.Tile()\n", + " self.expandims = P.ExpandDims()\n", + "\n", + " self.on_value = Tensor(1.0, mstype.float32)\n", + " self.off_value = Tensor(0.0, mstype.float32)\n", + "\n", + " self.num_bboxes = cfg.num_expected_pos_stage2 * batch_size\n", + " rmv_first = np.ones((self.num_bboxes, self.num_classes))\n", + " rmv_first[:, 0] = np.zeros((self.num_bboxes,))\n", + " self.rmv_first_tensor = Tensor(rmv_first.astype(self.np_cast_type))\n", + " self.mean_loss = P.ReduceMean()\n", + "\n", + " def construct(self, mask_featuremap, labels=None, mask=None, mask_fb_targets=None):\n", + " \"\"\"Construct Rcnn Mask.\"\"\"\n", + " x_mask_fb = self.fpn_mask(mask_featuremap)\n", + "\n", + " if self.training:\n", + " bbox_weights = self.cast(self.logicaland(self.greater(labels, 0), mask), mstype.int32) * labels\n", + " mask_fb_targets = self.tile(self.expandims(mask_fb_targets, 1), (1, self.num_classes, 1, 1))\n", + "\n", + " loss_mask_fb = self.loss(x_mask_fb, bbox_weights, mask, mask_fb_targets)\n", + " out = loss_mask_fb\n", + " else:\n", + " out = x_mask_fb\n", + "\n", + " return out\n", + "\n", + " def loss(self, masks_fb_pred, bbox_weights, weights, masks_fb_targets):\n", + " \"\"\"\n", + " Loss method.\n", + "\n", + " Args:\n", + " mask_fb_pred (Tensor): Mask feedback prediction.\n", + " bbox_weights (Tensor): Bounding box weights.\n", + " weights (Tensor): GT weights.\n", + " masks_fb_targets (Tensor): Mask feedback targets.\n", + "\n", + " Returns:\n", + " Tensor, loss mask feedback result.\n", + " \"\"\"\n", + " weights = self.cast(weights, self.cast_type)\n", + " bbox_weights = \\\n", + " self.cast(self.onehot(bbox_weights, self.num_classes, self.on_value, self.off_value), self.cast_type)\n", + " bbox_weights = bbox_weights * self.rmv_first_tensor\n", + "\n", + " # loss_mask_fb\n", + " masks_fb_targets = self.cast(masks_fb_targets, self.cast_type)\n", + " loss_mask_fb = self.loss_mask(masks_fb_pred, masks_fb_targets)\n", + " loss_mask_fb = self.mean_loss(loss_mask_fb, (2, 3))\n", + " loss_mask_fb = loss_mask_fb * bbox_weights\n", + " loss_mask_fb = loss_mask_fb / (self.sum_loss(weights, (0,)) + self.eps)\n", + " loss_mask_fb = self.sum_loss(loss_mask_fb, (0, 1))\n", + "\n", + " return loss_mask_fb" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Mask RCNN模型\n", + "\n", + "我们将卷积层，RPN层，RoIAlign层，Bbox预测层和Mask预测层连接起来，构建Mask RCNN网络。" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "from model.bbox_assign_sample_stage2 import BboxAssignSampleForRcnn\n", + "from model.fpn_neck import FeatPyramidNeck\n", + "from model.proposal_generator import Proposal\n", + "from model.rcnn_cls import RcnnCls\n", + "from model.rcnn_mask import RcnnMask\n", + "from model.rpn import RPN\n", + "from model.roi_align import SingleRoIExtractor\n", + "from model.anchor_generator import AnchorGenerator\n", + "from model.resnet50 import ResNetFea, ResidualBlockUsing\n", + "\n", + "\n", + "class MaskRcnnResnet50(nn.Cell):\n", + " \"\"\"\n", + " MaskRcnn Network.\n", + "\n", + " Note:\n", + " backbone = resnet50\n", + "\n", + " Args:\n", + " config (dict): Config.\n", + "\n", + " Inputs:\n", + " - **img_data** (Tensor) - Image data.\n", + " - **img_metas** (Tensor) - Image shapes.\n", + " - **gt_bboxes** (Tensor) - GT boudning boxes.\n", + " - **gt_labels** (Tensor) - GT labels.\n", + " - **gt_valids** (Tensor) - GT validations.\n", + " - **gt_masks** (Tensor) - GT masks.\n", + "\n", + " Outputs:\n", + " Function, return a tuple of output tensor.\n", + "\n", + " Support Plarforms:\n", + " ``Ascend`` ``CPU`` ``GPU``\n", + "\n", + " Examples:\n", + " >>> net = MaskRcnnResnet50(config)\n", + " \"\"\"\n", + " def __init__(self, config):\n", + " super(MaskRcnnResnet50, self).__init__()\n", + "\n", + " if context.get_context(\"device_target\") == \"Ascend\":\n", + " self.cast_type = mstype.float16\n", + " self.np_cast_type = np.float16\n", + " else:\n", + " self.cast_type = mstype.float32\n", + " self.np_cast_type = np.float32\n", + "\n", + " self.train_batch_size = config.batch_size\n", + " self.num_classes = config.num_classes\n", + " self.anchor_scales = config.anchor_scales\n", + " self.anchor_ratios = config.anchor_ratios\n", + " self.anchor_strides = config.anchor_strides\n", + " self.target_means = tuple(config.rcnn_target_means)\n", + " self.target_stds = tuple(config.rcnn_target_stds)\n", + "\n", + " # Anchor generator\n", + " anchor_base_sizes = None\n", + " self.anchor_base_sizes = list(\n", + " self.anchor_strides) if anchor_base_sizes is None else anchor_base_sizes\n", + "\n", + " self.anchor_generators = []\n", + " for anchor_base in self.anchor_base_sizes:\n", + " self.anchor_generators.append(AnchorGenerator(anchor_base, self.anchor_scales, self.anchor_ratios))\n", + "\n", + " self.num_anchors = len(self.anchor_ratios) * len(self.anchor_scales)\n", + "\n", + " featmap_sizes = config.feature_shapes\n", + " assert len(featmap_sizes) == len(self.anchor_generators)\n", + "\n", + " self.anchor_list = self.get_anchors(featmap_sizes)\n", + "\n", + " # Backbone resnet50\n", + " self.backbone = ResNetFea(ResidualBlockUsing, config.resnet_block, config.resnet_in_channels,\n", + " config.resnet_out_channels, False)\n", + " # Fpn\n", + " self.fpn_ncek = FeatPyramidNeck(config.fpn_in_channels, config.fpn_out_channels, config.fpn_num_outs)\n", + "\n", + " # Rpn and rpn loss\n", + " self.gt_labels_stage1 = Tensor(np.ones((self.train_batch_size, config.num_gts)).astype(np.uint8))\n", + " self.rpn_with_loss = RPN(config, self.train_batch_size, config.rpn_in_channels,\n", + " config.rpn_feat_channels, config.num_anchors, config.rpn_cls_out_channels)\n", + "\n", + " # Proposal\n", + " self.proposal_generator = Proposal(config, self.train_batch_size,\n", + " config.activate_num_classes,\n", + " config.use_sigmoid_cls)\n", + " self.proposal_generator.set_train_local(config, True)\n", + " self.proposal_generator_test = Proposal(config, config.test_batch_size,\n", + " config.activate_num_classes,\n", + " config.use_sigmoid_cls)\n", + " self.proposal_generator_test.set_train_local(config, False)\n", + "\n", + " # Assign and sampler stage two\n", + " self.bbox_assigner_sampler_for_rcnn = \\\n", + " BboxAssignSampleForRcnn(config, self.train_batch_size, config.num_bboxes_stage2, True)\n", + " self.decode = P.BoundingBoxDecode(max_shape=(768, 1280), means=self.target_means, stds=self.target_stds)\n", + "\n", + " # Roi\n", + " self.init_roi(config)\n", + "\n", + " # Rcnn\n", + " self.rcnn_cls = RcnnCls(config, self.train_batch_size, self.num_classes)\n", + " self.rcnn_mask = RcnnMask(config, self.train_batch_size, self.num_classes)\n", + "\n", + " # Op declare\n", + " self.squeeze = P.Squeeze()\n", + " self.cast = P.Cast()\n", + "\n", + " self.concat = P.Concat(axis=0)\n", + " self.concat_1 = P.Concat(axis=1)\n", + " self.concat_2 = P.Concat(axis=2)\n", + " self.reshape = P.Reshape()\n", + " self.select = P.Select()\n", + " self.greater = P.Greater()\n", + " self.transpose = P.Transpose()\n", + "\n", + " # Test mode\n", + " self.init_test_mode(config)\n", + "\n", + " # Improve speed\n", + " self.concat_start = min(self.num_classes - 2, 55)\n", + " self.concat_end = (self.num_classes - 1)\n", + "\n", + " # Init tensor\n", + " self.init_tensor(config)\n", + "\n", + " def init_roi(self, config):\n", + " \"\"\"initialize roi aligners.\"\"\"\n", + " self.roi_align = SingleRoIExtractor(config, config.roi_layer, config.roi_align_out_channels,\n", + " config.roi_align_featmap_strides, self.train_batch_size,\n", + " config.roi_align_finest_scale, mask=False)\n", + " self.roi_align.set_train_local(config, True)\n", + "\n", + " self.roi_align_mask = SingleRoIExtractor(config, config.roi_layer, config.roi_align_out_channels,\n", + " config.roi_align_featmap_strides, self.train_batch_size,\n", + " config.roi_align_finest_scale, mask=True)\n", + " self.roi_align_mask.set_train_local(config, True)\n", + "\n", + " self.roi_align_test = SingleRoIExtractor(config, config.roi_layer, config.roi_align_out_channels,\n", + " config.roi_align_featmap_strides, 1,\n", + " config.roi_align_finest_scale, mask=False)\n", + " self.roi_align_test.set_train_local(config, False)\n", + "\n", + " self.roi_align_mask_test = SingleRoIExtractor(config, config.roi_layer, config.roi_align_out_channels,\n", + " config.roi_align_featmap_strides, 1,\n", + " config.roi_align_finest_scale, mask=True)\n", + " self.roi_align_mask_test.set_train_local(config, False)\n", + "\n", + " def init_test_mode(self, config):\n", + " \"\"\"\"initialize the test mode.\"\"\"\n", + " self.test_batch_size = config.test_batch_size\n", + " self.split = P.Split(axis=0, output_num=self.test_batch_size)\n", + " self.split_shape = P.Split(axis=0, output_num=4)\n", + " self.split_scores = P.Split(axis=1, output_num=self.num_classes)\n", + " self.split_fb_mask = P.Split(axis=1, output_num=self.num_classes)\n", + " self.split_cls = P.Split(axis=0, output_num=self.num_classes-1)\n", + " self.tile = P.Tile()\n", + " self.gather = P.GatherNd()\n", + "\n", + " self.rpn_max_num = config.rpn_max_num\n", + "\n", + " self.zeros_for_nms = Tensor(np.zeros((self.rpn_max_num, 3)).astype(self.np_cast_type))\n", + " self.ones_mask = np.ones((self.rpn_max_num, 1)).astype(np.bool)\n", + " self.zeros_mask = np.zeros((self.rpn_max_num, 1)).astype(np.bool)\n", + " self.bbox_mask = Tensor(np.concatenate((self.ones_mask, self.zeros_mask,\n", + " self.ones_mask, self.zeros_mask), axis=1))\n", + " self.nms_pad_mask = Tensor(np.concatenate((self.ones_mask, self.ones_mask,\n", + " self.ones_mask, self.ones_mask,\n", + " self.zeros_mask), axis=1))\n", + "\n", + " self.test_score_thresh = Tensor(np.ones((self.rpn_max_num, 1)).astype(self.np_cast_type) * \\\n", + " config.test_score_thr)\n", + " self.test_score_zeros = Tensor(np.ones((self.rpn_max_num, 1)).astype(self.np_cast_type) * 0)\n", + " self.test_box_zeros = Tensor(np.ones((self.rpn_max_num, 4)).astype(self.np_cast_type) * -1)\n", + " self.test_iou_thr = Tensor(np.ones((self.rpn_max_num, 1)).astype(self.np_cast_type) * config.test_iou_thr)\n", + " self.test_max_per_img = config.test_max_per_img\n", + " self.nms_test = P.NMSWithMask(config.test_iou_thr)\n", + " self.softmax = P.Softmax(axis=1)\n", + " self.logicand = P.LogicalAnd()\n", + " self.oneslike = P.OnesLike()\n", + " self.test_topk = P.TopK(sorted=True)\n", + " self.test_num_proposal = self.test_batch_size * self.rpn_max_num\n", + "\n", + " def init_tensor(self, config):\n", + " \"\"\"initialize the tensors.\"\"\"\n", + " roi_align_index = [np.array(np.ones((config.num_expected_pos_stage2 + \\\n", + " config.num_expected_neg_stage2, 1)) * i,\n", + " dtype=self.np_cast_type) for i in range(self.train_batch_size)]\n", + "\n", + " roi_align_index_test = [np.array(np.ones((config.rpn_max_num, 1)) * i,\n", + " dtype=self.np_cast_type) for i in range(self.test_batch_size)]\n", + "\n", + " self.roi_align_index_tensor = Tensor(np.concatenate(roi_align_index))\n", + " self.roi_align_index_test_tensor = Tensor(np.concatenate(roi_align_index_test))\n", + "\n", + " roi_align_index_pos = [np.array(np.ones((config.num_expected_pos_stage2, 1)) * i,\n", + " dtype=self.np_cast_type) for i in range(self.train_batch_size)]\n", + " self.roi_align_index_tensor_pos = Tensor(np.concatenate(roi_align_index_pos))\n", + "\n", + " self.rcnn_loss_cls_weight = Tensor(np.array(config.rcnn_loss_cls_weight).astype(self.np_cast_type))\n", + " self.rcnn_loss_reg_weight = Tensor(np.array(config.rcnn_loss_reg_weight).astype(self.np_cast_type))\n", + " self.rcnn_loss_mask_fb_weight = Tensor(np.array(config.rcnn_loss_mask_fb_weight).astype(self.np_cast_type))\n", + "\n", + " self.argmax_with_value = P.ArgMaxWithValue(axis=1)\n", + " self.on_value = Tensor(1.0, mstype.float32)\n", + " self.off_value = Tensor(0.0, mstype.float32)\n", + " self.onehot = P.OneHot()\n", + " self.reducesum = P.ReduceSum()\n", + " self.sigmoid = P.Sigmoid()\n", + " self.expand_dims = P.ExpandDims()\n", + " self.test_mask_fb_zeros = Tensor(np.zeros((self.rpn_max_num, 28, 28)).astype(self.np_cast_type))\n", + " self.value = Tensor(1.0, self.cast_type)\n", + "\n", + " def construct(self, img_data, img_metas, gt_bboxes, gt_labels, gt_valids, gt_masks):\n", + " \"\"\"Construct for Mask R-CNN net.\"\"\"\n", + " x = self.backbone(img_data)\n", + " x = self.fpn_ncek(x)\n", + "\n", + " rpn_loss, cls_score, bbox_pred, rpn_cls_loss, rpn_reg_loss, _ = self.rpn_with_loss(x, img_metas,\n", + " self.anchor_list,\n", + " gt_bboxes,\n", + " self.gt_labels_stage1,\n", + " gt_valids)\n", + "\n", + " if self.training:\n", + " proposal, proposal_mask = self.proposal_generator(cls_score, bbox_pred, self.anchor_list)\n", + " else:\n", + " proposal, proposal_mask = self.proposal_generator_test(cls_score, bbox_pred, self.anchor_list)\n", + "\n", + " gt_labels = self.cast(gt_labels, mstype.int32)\n", + " gt_valids = self.cast(gt_valids, mstype.int32)\n", + " bboxes_tuple = ()\n", + " deltas_tuple = ()\n", + " labels_tuple = ()\n", + " mask_tuple = ()\n", + "\n", + " pos_bboxes_tuple = ()\n", + " pos_mask_fb_tuple = ()\n", + " pos_labels_tuple = ()\n", + " pos_mask_tuple = ()\n", + "\n", + " if self.training:\n", + " for i in range(self.train_batch_size):\n", + " gt_bboxes_i = self.squeeze(gt_bboxes[i:i + 1:1, ::])\n", + "\n", + " gt_labels_i = self.squeeze(gt_labels[i:i + 1:1, ::])\n", + " gt_labels_i = self.cast(gt_labels_i, mstype.uint8)\n", + "\n", + " gt_valids_i = self.squeeze(gt_valids[i:i + 1:1, ::])\n", + " gt_valids_i = self.cast(gt_valids_i, mstype.bool_)\n", + "\n", + " gt_masks_i = self.squeeze(gt_masks[i:i + 1:1, ::])\n", + " gt_masks_i = self.cast(gt_masks_i, mstype.bool_)\n", + "\n", + " bboxes, deltas, labels, mask, pos_bboxes, pos_mask_fb, pos_labels, pos_mask = \\\n", + " self.bbox_assigner_sampler_for_rcnn(gt_bboxes_i, gt_labels_i, proposal_mask[i],\n", + " proposal[i][::, 0:4:1], gt_valids_i, gt_masks_i)\n", + " bboxes_tuple += (bboxes,)\n", + " deltas_tuple += (deltas,)\n", + " labels_tuple += (labels,)\n", + " mask_tuple += (mask,)\n", + "\n", + " pos_bboxes_tuple += (pos_bboxes,)\n", + " pos_mask_fb_tuple += (pos_mask_fb,)\n", + " pos_labels_tuple += (pos_labels,)\n", + " pos_mask_tuple += (pos_mask,)\n", + "\n", + " bbox_targets = self.concat(deltas_tuple)\n", + " rcnn_labels = self.concat(labels_tuple)\n", + " bbox_targets = F.stop_gradient(bbox_targets)\n", + " rcnn_labels = F.stop_gradient(rcnn_labels)\n", + " rcnn_labels = self.cast(rcnn_labels, mstype.int32)\n", + "\n", + " rcnn_pos_masks_fb = self.concat(pos_mask_fb_tuple)\n", + " rcnn_pos_masks_fb = F.stop_gradient(rcnn_pos_masks_fb)\n", + " rcnn_pos_labels = self.concat(pos_labels_tuple)\n", + " rcnn_pos_labels = F.stop_gradient(rcnn_pos_labels)\n", + " rcnn_pos_labels = self.cast(rcnn_pos_labels, mstype.int32)\n", + " else:\n", + " mask_tuple += proposal_mask\n", + " bbox_targets = proposal_mask\n", + " rcnn_labels = proposal_mask\n", + "\n", + " rcnn_pos_masks_fb = proposal_mask\n", + " rcnn_pos_labels = proposal_mask\n", + " for p_i in proposal:\n", + " bboxes_tuple += (p_i[::, 0:4:1],)\n", + "\n", + " bboxes_all, rois, pos_rois = self.rois(bboxes_tuple, pos_bboxes_tuple)\n", + "\n", + " if self.training:\n", + " roi_feats = self.roi_align(rois,\n", + " self.cast(x[0], mstype.float32),\n", + " self.cast(x[1], mstype.float32),\n", + " self.cast(x[2], mstype.float32),\n", + " self.cast(x[3], mstype.float32))\n", + " else:\n", + " roi_feats = self.roi_align_test(rois,\n", + " self.cast(x[0], mstype.float32),\n", + " self.cast(x[1], mstype.float32),\n", + " self.cast(x[2], mstype.float32),\n", + " self.cast(x[3], mstype.float32))\n", + "\n", + "\n", + " roi_feats = self.cast(roi_feats, self.cast_type)\n", + " rcnn_masks = self.concat(mask_tuple)\n", + " rcnn_masks = F.stop_gradient(rcnn_masks)\n", + " rcnn_mask_squeeze = self.squeeze(self.cast(rcnn_masks, mstype.bool_))\n", + "\n", + " rcnn_pos_masks = self.concat(pos_mask_tuple)\n", + " rcnn_pos_masks = F.stop_gradient(rcnn_pos_masks)\n", + " rcnn_pos_mask_squeeze = self.squeeze(self.cast(rcnn_pos_masks, mstype.bool_))\n", + "\n", + " rcnn_cls_loss, rcnn_reg_loss = self.rcnn_cls(roi_feats, bbox_targets, rcnn_labels, rcnn_mask_squeeze)\n", + "\n", + " if self.training:\n", + " return self.get_output_train(pos_rois, x, rcnn_pos_labels, rcnn_pos_mask_squeeze, rcnn_pos_masks_fb,\n", + " rpn_loss, rpn_cls_loss, rpn_reg_loss, rcnn_cls_loss, rcnn_reg_loss)\n", + "\n", + " return self.get_output_eval(x, bboxes_all, rcnn_cls_loss, rcnn_reg_loss, rcnn_masks, img_metas)\n", + "\n", + " def rois(self, bboxes_tuple, pos_bboxes_tuple):\n", + " \"\"\"\"initialize the rois.\"\"\"\n", + " pos_rois = None\n", + " if self.training:\n", + " if self.train_batch_size > 1:\n", + " bboxes_all = self.concat(bboxes_tuple)\n", + " pos_bboxes_all = self.concat(pos_bboxes_tuple)\n", + " else:\n", + " bboxes_all = bboxes_tuple[0]\n", + " pos_bboxes_all = pos_bboxes_tuple[0]\n", + " rois = self.concat_1((self.roi_align_index_tensor, bboxes_all))\n", + " pos_rois = self.concat_1((self.roi_align_index_tensor_pos, pos_bboxes_all))\n", + " pos_rois = self.cast(pos_rois, mstype.float32)\n", + " pos_rois = F.stop_gradient(pos_rois)\n", + " else:\n", + " if self.test_batch_size > 1:\n", + " bboxes_all = self.concat(bboxes_tuple)\n", + " else:\n", + " bboxes_all = bboxes_tuple[0]\n", + " rois = self.concat_1((self.roi_align_index_test_tensor, bboxes_all))\n", + "\n", + " rois = self.cast(rois, mstype.float32)\n", + " rois = F.stop_gradient(rois)\n", + "\n", + " return bboxes_all, rois, pos_rois\n", + "\n", + " def get_output_train(self, pos_rois, x, rcnn_pos_labels, rcnn_pos_mask_squeeze, rcnn_pos_masks_fb,\n", + " rpn_loss, rpn_cls_loss, rpn_reg_loss, rcnn_cls_loss, rcnn_reg_loss):\n", + " \"\"\"get the training outputs.\"\"\"\n", + " output = ()\n", + " roi_feats_mask = self.roi_align_mask(pos_rois,\n", + " self.cast(x[0], mstype.float32),\n", + " self.cast(x[1], mstype.float32),\n", + " self.cast(x[2], mstype.float32),\n", + " self.cast(x[3], mstype.float32))\n", + " roi_feats_mask = self.cast(roi_feats_mask, self.cast_type)\n", + " rcnn_mask_fb_loss = self.rcnn_mask(roi_feats_mask, rcnn_pos_labels, rcnn_pos_mask_squeeze, rcnn_pos_masks_fb)\n", + "\n", + " rcnn_loss = self.rcnn_loss_cls_weight * rcnn_cls_loss + self.rcnn_loss_reg_weight * rcnn_reg_loss + \\\n", + " self.rcnn_loss_mask_fb_weight * rcnn_mask_fb_loss\n", + " output += (rpn_loss, rcnn_loss, rpn_cls_loss, rpn_reg_loss, rcnn_cls_loss, rcnn_reg_loss, rcnn_mask_fb_loss)\n", + " return output\n", + "\n", + " def get_output_eval(self, x, bboxes_all, rcnn_cls_loss, rcnn_reg_loss, rcnn_masks, img_metas):\n", + " \"\"\"get the evaluation results.\"\"\"\n", + " mask_fb_pred_all = self.rcnn_mask_test(x, bboxes_all, rcnn_cls_loss, rcnn_reg_loss)\n", + " output = self.get_det_bboxes(rcnn_cls_loss, rcnn_reg_loss, rcnn_masks, bboxes_all, img_metas, mask_fb_pred_all)\n", + " return output\n", + "\n", + " def get_det_bboxes(self, cls_logits, reg_logits, mask_logits, rois, img_metas, mask_fb_pred_all):\n", + " \"\"\"Get the actual detection box.\"\"\"\n", + " scores = self.softmax(cls_logits / self.value)\n", + " mask_fb_logits = self.sigmoid(mask_fb_pred_all)\n", + "\n", + " boxes_all = ()\n", + " for i in range(self.num_classes):\n", + " k = i * 4\n", + " reg_logits_i = self.squeeze(reg_logits[::, k:k+4:1])\n", + " out_boxes_i = self.decode(rois, reg_logits_i)\n", + " boxes_all += (out_boxes_i,)\n", + "\n", + " img_metas_all = self.split(img_metas)\n", + " scores_all = self.split(scores)\n", + " mask_all = self.split(self.cast(mask_logits, mstype.int32))\n", + " mask_fb_all = self.split(mask_fb_logits)\n", + "\n", + " boxes_all_with_batchsize = ()\n", + " for i in range(self.test_batch_size):\n", + " scale = self.split_shape(self.squeeze(img_metas_all[i]))\n", + " scale_h = scale[2]\n", + " scale_w = scale[3]\n", + " boxes_tuple = ()\n", + " for j in range(self.num_classes):\n", + " boxes_tmp = self.split(boxes_all[j])\n", + " out_boxes_h = boxes_tmp[i] / scale_h\n", + " out_boxes_w = boxes_tmp[i] / scale_w\n", + " boxes_tuple += (self.select(self.bbox_mask, out_boxes_w, out_boxes_h),)\n", + " boxes_all_with_batchsize += (boxes_tuple,)\n", + "\n", + " output = self.multiclass_nms(boxes_all_with_batchsize, scores_all, mask_all, mask_fb_all)\n", + "\n", + " return output\n", + "\n", + " def multiclass_nms(self, boxes_all, scores_all, mask_all, mask_fb_all):\n", + " \"\"\"\n", + " Multiscale postprocessing.\n", + "\n", + " Args:\n", + " boxes_all (tuple): All bounding boxes.\n", + " scores_all (tuple): All scores.\n", + " mask_all (tuple): All masks.\n", + " mask_fb_all (tuple): All feedback masks.\n", + "\n", + " Returns:\n", + " - all_bboxes, tuple, output bounding boxes with the same shape of boxes_all.\n", + " - all_labels, tuple, output labels with the same shape of scores_all.\n", + " - all_masks, tuple, output masks with the same shape of mask_all.\n", + " - all_masks_fb, tuple, output feedback masks with the same shape of mask_fb_all.\n", + " \"\"\"\n", + " all_bboxes = ()\n", + " all_labels = ()\n", + " all_masks = ()\n", + " all_masks_fb = ()\n", + "\n", + " for i in range(self.test_batch_size):\n", + " bboxes = boxes_all[i]\n", + " scores = scores_all[i]\n", + " masks = self.cast(mask_all[i], mstype.bool_)\n", + " masks_fb = mask_fb_all[i]\n", + " mask_fb_all_x = self.split_fb_mask(masks_fb)\n", + "\n", + " res_boxes_tuple = ()\n", + " res_labels_tuple = ()\n", + " res_masks_tuple = ()\n", + " res_masks_fb_tuple = ()\n", + "\n", + " for j in range(self.num_classes - 1):\n", + " k = j + 1\n", + " cls_scores_x = scores[::, k:k + 1:1]\n", + " bboxes_x = self.squeeze(bboxes[k])\n", + " mask_ox = self.reshape(masks, (self.rpn_max_num, 1))\n", + " masks_fb_x = self.squeeze(mask_fb_all_x[k])\n", + "\n", + " cls_mask = self.greater(cls_scores_x, self.test_score_thresh)\n", + " mask_x = self.logicand(mask_ox, cls_mask)\n", + "\n", + " reg_mask_x = self.cast(self.tile(self.cast(mask_x, mstype.int32), (1, 4)), mstype.bool_)\n", + "\n", + " bboxes_x = self.select(reg_mask_x, bboxes_x, self.test_box_zeros)\n", + " fb_mask_x = self.expand_dims(mask_x, -1)\n", + " mask_fb_mask_x = self.cast(self.tile(self.cast(fb_mask_x, mstype.int32), (1, 28, 28)), mstype.bool_)\n", + " masks_fb_x = self.select(mask_fb_mask_x, masks_fb_x, self.test_mask_fb_zeros)\n", + " cls_scores_x = self.select(mask_x, cls_scores_x, self.test_score_zeros)\n", + " cls_scores_x_next = self.squeeze(cls_scores_x)\n", + " scores_sorted, topk_inds = self.test_topk(cls_scores_x_next, self.rpn_max_num)\n", + " topk_inds = self.reshape(topk_inds, (self.rpn_max_num, 1))\n", + " scores_sorted = self.reshape(scores_sorted, (self.rpn_max_num, 1))\n", + " bboxes_x_sorted = self.gather(bboxes_x, topk_inds)\n", + " mask_fb_sorted_x = self.gather(masks_fb_x, topk_inds)\n", + " mask_sorted_x = self.gather(mask_x, topk_inds)\n", + "\n", + " scores_sorted = self.tile(scores_sorted, (1, 4))\n", + " cls_dets = self.concat_1((bboxes_x_sorted, scores_sorted))\n", + " cls_dets = P.Slice()(cls_dets, (0, 0), (self.rpn_max_num, 5))\n", + "\n", + " cls_dets, index_x, mask_nms_x = self.nms_test(cls_dets)\n", + " index_x = self.reshape(index_x, (self.rpn_max_num, 1))\n", + " mask_nms_x = self.reshape(mask_nms_x, (self.rpn_max_num, 1))\n", + "\n", + " mask_n_x = self.gather(mask_sorted_x, index_x)\n", + " mask_n_x = self.logicand(mask_n_x, mask_nms_x)\n", + "\n", + " mask_fb_x = self.gather(mask_fb_sorted_x, index_x)\n", + "\n", + " cls_labels = self.oneslike(index_x) * j\n", + " res_boxes_tuple += (cls_dets,)\n", + " res_labels_tuple += (cls_labels,)\n", + " res_masks_tuple += (mask_n_x,)\n", + " res_masks_fb_tuple += (mask_fb_x,)\n", + "\n", + " res_boxes_start = self.concat(res_boxes_tuple[:self.concat_start])\n", + " res_labels_start = self.concat(res_labels_tuple[:self.concat_start])\n", + " res_masks_start = self.concat(res_masks_tuple[:self.concat_start])\n", + " res_masks_fb_start = self.concat(res_masks_fb_tuple[:self.concat_start])\n", + "\n", + " res_boxes_end = self.concat(res_boxes_tuple[self.concat_start:self.concat_end])\n", + " res_labels_end = self.concat(res_labels_tuple[self.concat_start:self.concat_end])\n", + " res_masks_end = self.concat(res_masks_tuple[self.concat_start:self.concat_end])\n", + " res_masks_fb_end = self.concat(res_masks_fb_tuple[self.concat_start:self.concat_end])\n", + "\n", + " res_boxes = self.concat((res_boxes_start, res_boxes_end))\n", + " res_labels = self.concat((res_labels_start, res_labels_end))\n", + " res_masks = self.concat((res_masks_start, res_masks_end))\n", + " res_masks_fb = self.concat((res_masks_fb_start, res_masks_fb_end))\n", + "\n", + " reshape_size = (self.num_classes - 1) * self.rpn_max_num\n", + " res_boxes = self.reshape(res_boxes, (1, reshape_size, 5))\n", + " res_labels = self.reshape(res_labels, (1, reshape_size, 1))\n", + " res_masks = self.reshape(res_masks, (1, reshape_size, 1))\n", + " res_masks_fb = self.reshape(res_masks_fb, (1, reshape_size, 28, 28))\n", + "\n", + " all_bboxes += (res_boxes,)\n", + " all_labels += (res_labels,)\n", + " all_masks += (res_masks,)\n", + " all_masks_fb += (res_masks_fb,)\n", + "\n", + " all_bboxes = self.concat(all_bboxes)\n", + " all_labels = self.concat(all_labels)\n", + " all_masks = self.concat(all_masks)\n", + " all_masks_fb = self.concat(all_masks_fb)\n", + " return all_bboxes, all_labels, all_masks, all_masks_fb\n", + "\n", + " def get_anchors(self, featmap_sizes):\n", + " \"\"\"Get anchors according to feature map sizes.\n", + "\n", + " Args:\n", + " featmap_sizes (list[tuple]): Multi-level feature map sizes.\n", + " img_metas (list[dict]): Image meta info.\n", + "\n", + " Returns:\n", + " Tuple, anchors of each image, valid flags of each image\n", + " \"\"\"\n", + " num_levels = len(featmap_sizes)\n", + "\n", + " # since feature map sizes of all images are the same, we only compute\n", + " # anchors for one time\n", + " multi_level_anchors = ()\n", + " for i in range(num_levels):\n", + " anchors = self.anchor_generators[i].grid_anchors(featmap_sizes[i], self.anchor_strides[i])\n", + " multi_level_anchors += (Tensor(anchors.astype(self.np_cast_type)),)\n", + "\n", + " return multi_level_anchors\n", + "\n", + " def rcnn_mask_test(self, x, rois, cls_pred, reg_pred):\n", + " \"\"\"\n", + " Prediction masks in an images by the bounding boxes.\n", + "\n", + " Args:\n", + " x (Cell): Input layer.\n", + " rois (List): Region of Interest.\n", + " cls_pred (float): Classification prediction.\n", + " reg_pred (float): Regression prediction.\n", + "\n", + " Returns:\n", + " Cell, masked rcnn layer.\n", + " \"\"\"\n", + " cls_scores = self.softmax(cls_pred / self.value)\n", + "\n", + " cls_scores_all = self.split(cls_scores)\n", + " reg_pred = self.reshape(reg_pred, (-1, self.num_classes, 4))\n", + " reg_pred_all = self.split(reg_pred)\n", + " rois_all = self.split(rois)\n", + " boxes_tuple = ()\n", + " for i in range(self.test_batch_size):\n", + " cls_score_max_index, _ = self.argmax_with_value(cls_scores_all[i])\n", + " cls_score_max_index = self.cast(self.onehot(cls_score_max_index, self.num_classes,\n", + " self.on_value, self.off_value), self.cast_type)\n", + " cls_score_max_index = self.expand_dims(cls_score_max_index, -1)\n", + " cls_score_max_index = self.tile(cls_score_max_index, (1, 1, 4))\n", + " reg_pred_max = reg_pred_all[i] * cls_score_max_index\n", + " reg_pred_max = self.reducesum(reg_pred_max, 1)\n", + " out_boxes_i = self.decode(rois_all[i], reg_pred_max)\n", + " boxes_tuple += (out_boxes_i,)\n", + "\n", + " boxes_all = self.concat(boxes_tuple)\n", + " boxes_rois = self.concat_1((self.roi_align_index_test_tensor, boxes_all))\n", + " boxes_rois = self.cast(boxes_rois, self.cast_type)\n", + " roi_feats_mask_test = self.roi_align_mask_test(boxes_rois,\n", + " self.cast(x[0], mstype.float32),\n", + " self.cast(x[1], mstype.float32),\n", + " self.cast(x[2], mstype.float32),\n", + " self.cast(x[3], mstype.float32))\n", + " roi_feats_mask_test = self.cast(roi_feats_mask_test, self.cast_type)\n", + " mask_fb_pred_all = self.rcnn_mask(roi_feats_mask_test)\n", + " return mask_fb_pred_all" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 连接网络和损失函数\n", + "\n", + "MindSpore将损失函数、优化器等操作都封装到了Cell中，我们需要自定义WithLossCell类，将网络和Loss连接起来。\n", + "\n", + "Mask RCNN的损失函数被定义为：\n", + "\n", + "$$\n", + "L=L_{c l s}+L_{b o x}+L_{\\text {mask }}\n", + "$$\n", + "\n", + "$L_{c l s}$类别损失：rpn class和rcnn_cls的类别损失都是交叉熵损失。\n", + "\n", + "$L_{b o x}$边框损失：\n", + "\n", + "$$\n", + "\\operatorname{smooth}_{L_{1}}(x)= \\begin{cases}0.5 x^{2} & \\text { if }|x|<1 \\\\ |x|-0.5 & \\text { otherwise }\\end{cases}\n", + "$$\n", + "\n", + "$L_{mask}$掩膜损失：\n", + "\n", + "只对rcnn_mask计算1/0交叉熵损失。" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "TIME_STAMP_INIT = False\n", + "TIME_STAMP_FIRST = 0\n", + "\n", + "GRADIENT_CLIP_TYPE = 1\n", + "GRADIENT_CLIP_VALUE = 1.0\n", + "\n", + "clip_grad = C.MultitypeFuncGraph(\"clip_grad\")\n", + "\n", + "\n", + "@clip_grad.register(\"Number\", \"Number\", \"Tensor\")\n", + "def _clip_grad(clip_type, clip_value, grad):\n", + " \"\"\"\n", + " Clip gradients.\n", + "\n", + " Args:\n", + " clip_type (int): The way to clip, 0 for 'value', 1 for 'norm'.\n", + " clip_value (float): Specifies how much to clip.\n", + " grad (tuple[Tensor]): Gradients.\n", + "\n", + " Returns:\n", + " tuple[Tensor], clipped gradients.\n", + " \"\"\"\n", + " if clip_type not in (0, 1):\n", + " return grad\n", + " dt = F.dtype(grad)\n", + " mf_cast = F.cast(F.tuple_to_array((-clip_value,)), dt)\n", + " pf_cast = F.cast(F.tuple_to_array((clip_value,)), dt)\n", + " if clip_type == 0:\n", + " new_grad = C.clip_by_value(grad, mf_cast, pf_cast)\n", + " else:\n", + " new_grad = nn.ClipByNorm()(grad, pf_cast)\n", + " return F.cast(new_grad, dt)\n", + "\n", + "\n", + "class LossCallBack(Callback):\n", + " \"\"\"\n", + " Monitor the loss in training.\n", + "\n", + " If the loss is NAN or INF terminating training.\n", + "\n", + " Note:\n", + " If per_print_times is 0 do not print loss.\n", + "\n", + " Args:\n", + " per_print_times (int): Print loss every times. Default: 1.\n", + " \"\"\"\n", + "\n", + " def __init__(self, per_print_times=1, rank_id=0):\n", + " super(LossCallBack, self).__init__()\n", + " if not isinstance(per_print_times, int) or per_print_times < 0:\n", + " raise ValueError(\"print_step must be int and >= 0.\")\n", + " self._per_print_times = per_print_times\n", + " self.count = 0\n", + " self.loss_sum = 0\n", + " self.rank_id = rank_id\n", + "\n", + " global TIME_STAMP_INIT, TIME_STAMP_FIRST\n", + " if not TIME_STAMP_INIT:\n", + " TIME_STAMP_FIRST = time.time()\n", + " TIME_STAMP_INIT = True\n", + "\n", + " def step_end(self, run_context):\n", + " \"\"\"set the end of step\"\"\"\n", + " cb_params = run_context.original_args()\n", + " loss = cb_params.net_outputs.asnumpy()\n", + " cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1\n", + " cur_time = time.time()\n", + " self.count += 1\n", + " self.loss_sum += float(loss)\n", + "\n", + " if self.count >= 1:\n", + " global TIME_STAMP_FIRST\n", + " time_stamp_current = time.time()\n", + " total_loss = self.loss_sum/self.count\n", + "\n", + " print(\"%lu epoch: %s step: %s total_loss: %.5f\" %\n", + " (time_stamp_current - TIME_STAMP_FIRST,\n", + " cb_params.cur_epoch_num, cur_step_in_epoch, total_loss))\n", + " loss_file = open(\"./loss_{}.log\".format(self.rank_id), \"a+\")\n", + " loss_file.write(\"%lu epoch: %s step: %s total_loss: %.5f\" %\n", + " (time_stamp_current - TIME_STAMP_FIRST,\n", + " cb_params.cur_epoch_num, cur_step_in_epoch, total_loss))\n", + " loss_file.write(\"\\n\")\n", + " loss_file.close()\n", + "\n", + " self.count = 0\n", + " self.loss_sum = 0\n", + " \n", + " if cur_step_in_epoch > 100 and total_loss < 1:\n", + " print(\"End training, time:\", cur_time, \",epoch:\", cb_params.cur_epoch_num,\n", + " \",step:\", cur_step_in_epoch, \",loss:\", total_loss)\n", + " run_context.request_stop()\n", + "\n", + "\n", + "class LossNet(nn.Cell):\n", + " \"\"\"MaskRcnn loss sum\"\"\"\n", + " def construct(self, x1, x2):\n", + " return x1 + x2\n", + "\n", + "\n", + "class WithLossCell(nn.Cell):\n", + " \"\"\"\n", + " Wrap the network with loss function to compute loss.\n", + "\n", + " Args:\n", + " backbone (Cell): The target network to wrap.\n", + " loss_fn (Cell): The loss function used to compute loss.\n", + "\n", + " Inputs:\n", + " - **x** (Tensor) - Input variant.\n", + " - **img_shape** (Tensor) - Img shape.\n", + " - **gt_bboxe** (Tensor) - Ground truth bounding boxes.\n", + " - **gt_label** (Tensor) - Ground truth labels.\n", + " - **gt_num** (int) - The number of ground truth.\n", + " - **gt_mask** (Tensor) - Ground truth mask.\n", + "\n", + " Outputs:\n", + " Loss network, Cell\n", + "\n", + " Support Platform:\n", + " ``Ascend`` ``CPU`` ``GPU``\n", + "\n", + " Examples:\n", + " >>> from src.utils.config import config\n", + " >>> from src.model.mask_rcnn_r50 import MaskRcnnResnet50\n", + " >>> net = MaskRcnnMobilenetV1(config=config)\n", + " >>> loss = LossNet()\n", + " >>> net_with_loss = WithLossCell(net, loss)\n", + " \"\"\"\n", + " def __init__(self, backbone, loss_fn):\n", + " super(WithLossCell, self).__init__(auto_prefix=False)\n", + " self._backbone = backbone\n", + " self._loss_fn = loss_fn\n", + "\n", + " def construct(self, x, img_shape, gt_bboxe, gt_label, gt_num, gt_mask):\n", + " loss1, loss2, _, _, _, _, _ = self._backbone(x, img_shape, gt_bboxe, gt_label, gt_num, gt_mask)\n", + " return self._loss_fn(loss1, loss2)\n", + "\n", + " @property\n", + " def backbone_network(self):\n", + " \"\"\"\n", + " Get the backbone network.\n", + "\n", + " Returns:\n", + " Cell, return backbone network.\n", + " \"\"\"\n", + " return self._backbone\n", + "\n", + "class TrainOneStepCell(nn.Cell):\n", + " \"\"\"\n", + " Network training package class.\n", + "\n", + " Append an optimizer to the training network\n", + " after that the construct function.\n", + " can be called to create the backward graph.\n", + "\n", + " Args:\n", + " network (Cell): The training network.\n", + " optimizer (Cell): Optimizer for updating the weights.\n", + " sens (Number): The adjust parameter. Default: 1.0.\n", + " reduce_flag (bool): The reduce flag. Default: False.\n", + " mean (bool): Allreduce method. Default: False.\n", + " degree (int): Device number. Default: None.\n", + "\n", + " Inputs:\n", + " - **x** (Tensor) - Input variant.\n", + " - **img_shape** (Tensor) - Img shape.\n", + " - **gt_bboxe** (Tensor) - Ground truth bounding boxes.\n", + " - **gt_label** (Tensor) - Ground truth labels.\n", + " - **gt_num** (int) - The number of ground truth.\n", + " - **gt_mask** (Tensor) - Ground truth mask.\n", + "\n", + " Outputs:\n", + " Float, loss result.\n", + "\n", + " Support Platform:\n", + " ``Ascend`` ``CPU`` ``GPU``\n", + "\n", + " Examples:\n", + " >>> from src.utils.config import config\n", + " >>> from src.model.mask_rcnn_r50 import MaskRcnnResnet50\n", + " >>> net = MaskRcnnResnet50(config=config)\n", + " >>> loss = LossNet()\n", + " >>> net_with_loss = WithLossCell(net, loss)\n", + " >>> lr = Tensor(dynamic_lr(config, rank_size=1, start_steps=0), mstype.float32)\n", + " >>> opt = Momentum(params=net.trainable_params(), learning_rate=lr, momentum=0.91,\n", + " ... weight_decay=1e-4, loss_scale=1)\n", + " >>> net = TrainOneStepCell(net_with_loss, opt, sens=config.loss_scale)\n", + " \"\"\"\n", + " def __init__(self, network, optimizer, sens=1.0, reduce_flag=False, mean=True, degree=None):\n", + " super(TrainOneStepCell, self).__init__(auto_prefix=False)\n", + " self.network = network\n", + " self.network.set_grad()\n", + " self.weights = ParameterTuple(network.trainable_params())\n", + " self.optimizer = optimizer\n", + " self.grad = C.GradOperation(get_by_list=True, sens_param=True)\n", + "\n", + " if config.device_target == \"Ascend\":\n", + " self.sens = Tensor((np.ones((1,)) * sens).astype(np.float16))\n", + " else:\n", + " self.sens = Tensor((np.ones((1,)) * sens).astype(np.float32))\n", + " self.reduce_flag = reduce_flag\n", + " self.hyper_map = C.HyperMap()\n", + " if reduce_flag:\n", + " self.grad_reducer = DistributedGradReducer(optimizer.parameters, mean, degree)\n", + "\n", + " def construct(self, x, img_shape, gt_bboxe, gt_label, gt_num, gt_mask):\n", + " \"\"\"Construct Network training package class.\"\"\"\n", + " weights = self.weights\n", + " loss = self.network(x, img_shape, gt_bboxe, gt_label, gt_num, gt_mask)\n", + " grads = self.grad(self.network, weights)(x, img_shape, gt_bboxe, gt_label, gt_num, gt_mask, self.sens)\n", + " if self.reduce_flag:\n", + " grads = self.grad_reducer(grads)\n", + " grads = self.hyper_map(F.partial(clip_grad, GRADIENT_CLIP_TYPE, GRADIENT_CLIP_VALUE), grads)\n", + " self.optimizer(grads)\n", + " return loss" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 训练\n", + "\n", + "### 模型训练参数\n", + "\n", + "在这里，我们列出了一些重要的训练参数。此外，您可以查看配置文件config.py的详细信息。\n", + "\n", + "| Parameter | Default | Description |\n", + "| ---- | ---- | ---- |\n", + "| workers | 1 | Number of parallel workers |\n", + "| device_target | GPU | Device type |\n", + "| learning_rate | 0.002 | learning rate |\n", + "| weight_decay | 1e-4 | Control weight decay speed |\n", + "| total_epoch | 13 | Number of epoch |\n", + "| batch_size | 2 | Batch size |\n", + "| dataset | coco | Dataset name |\n", + "| pre_trained | ./checkpoint | The path of pretrained model |\n", + "| checkpoint_path | ./ckpt_0 | The path to save |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 训练模型\n", + "\n", + "模型训练需要定义好优化器、损失函数等。同时，可以加载预训练模型以加快模型训练。\n", + "\n", + "因此，我们定义权重文件加载函数。" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def load_pretrained_ckpt(net, load_path, device_target):\n", + " \"\"\"\n", + " Load pretrained checkpoint.\n", + "\n", + " Args:\n", + " net(Cell): Used Network\n", + " load_path(string): The path of checkpoint.\n", + " device_target(string): device target.\n", + "\n", + " Returns:\n", + " Cell, the network with pretrained weights.\n", + " \"\"\"\n", + " param_dict = load_checkpoint(load_path)\n", + " if config.pretrain_epoch_size == 0:\n", + " for item in list(param_dict.keys()):\n", + " if not (item.startswith('backbone') or item.startswith('rcnn_mask')):\n", + " param_dict.pop(item)\n", + "\n", + " if device_target == 'GPU':\n", + " for key, value in param_dict.items():\n", + " tensor = Tensor(value, mstype.float32)\n", + " param_dict[key] = Parameter(tensor, key)\n", + "\n", + " load_param_into_net(net, param_dict)\n", + " return net" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "本案例中，为了方便展示效果，选取了数据集中的部分数据进行了1个epoch的训练，由于加载了预训练模型，所以loss值快速趋于稳定，在1附近间波动，这可以作为判断模型收敛的一个标准。\n", + "\n", + "训练得到的ckpt文件被保存在checkpoint文件夹内，可以作为后续fine-tune以及推理的加载模型使用。" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Start train for maskrcnn!\n", + "Start create dataset!\n", + "total images num: 51790\n", + "Create dataset done!\n", + "Loading pretrained resnet50 checkpoint\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[WARNING] 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rcnn_cls.fpn_cls.cls_scores.bias is not loaded.\n", + "[WARNING] ME(53783:281472933222272,MainProcess):2022-11-16-13:07:05.326.489 [mindspore/train/serialization.py:650] rcnn_cls.fpn_cls.reg_scores.weight is not loaded.\n", + "[WARNING] ME(53783:281472933222272,MainProcess):2022-11-16-13:07:05.327.092 [mindspore/train/serialization.py:650] rcnn_cls.fpn_cls.reg_scores.bias is not loaded.\n", + "[WARNING] ME(53783:281472933222272,MainProcess):2022-11-16-13:07:05.327.684 [mindspore/train/serialization.py:650] rcnn_mask.fpn_mask.mask_conv1.weight is not loaded.\n", + "[WARNING] ME(53783:281472933222272,MainProcess):2022-11-16-13:07:05.328.270 [mindspore/train/serialization.py:650] rcnn_mask.fpn_mask.mask_conv1.bias is not loaded.\n", + "[WARNING] ME(53783:281472933222272,MainProcess):2022-11-16-13:07:05.328.861 [mindspore/train/serialization.py:650] rcnn_mask.fpn_mask.mask_conv2.weight is not loaded.\n", + "[WARNING] ME(53783:281472933222272,MainProcess):2022-11-16-13:07:05.329.446 [mindspore/train/serialization.py:650] rcnn_mask.fpn_mask.mask_conv2.bias is not loaded.\n", + "[WARNING] ME(53783:281472933222272,MainProcess):2022-11-16-13:07:05.330.032 [mindspore/train/serialization.py:650] rcnn_mask.fpn_mask.mask_conv3.weight is not loaded.\n", + "[WARNING] ME(53783:281472933222272,MainProcess):2022-11-16-13:07:05.330.620 [mindspore/train/serialization.py:650] rcnn_mask.fpn_mask.mask_conv3.bias is not loaded.\n", + "[WARNING] ME(53783:281472933222272,MainProcess):2022-11-16-13:07:05.331.230 [mindspore/train/serialization.py:650] rcnn_mask.fpn_mask.mask_conv4.weight is not loaded.\n", + "[WARNING] ME(53783:281472933222272,MainProcess):2022-11-16-13:07:05.331.821 [mindspore/train/serialization.py:650] rcnn_mask.fpn_mask.mask_conv4.bias is not loaded.\n", + "[WARNING] ME(53783:281472933222272,MainProcess):2022-11-16-13:07:05.332.408 [mindspore/train/serialization.py:650] rcnn_mask.fpn_mask.mask_deconv5.weight is not loaded.\n", + "[WARNING] ME(53783:281472933222272,MainProcess):2022-11-16-13:07:05.332.997 [mindspore/train/serialization.py:650] rcnn_mask.fpn_mask.mask_deconv5.bias is not loaded.\n", + "[WARNING] ME(53783:281472933222272,MainProcess):2022-11-16-13:07:05.333.678 [mindspore/train/serialization.py:650] rcnn_mask.fpn_mask.mask_conv6.weight is not loaded.\n", + "[WARNING] ME(53783:281472933222272,MainProcess):2022-11-16-13:07:05.334.277 [mindspore/train/serialization.py:650] rcnn_mask.fpn_mask.mask_conv6.bias is not loaded.\n", + "[WARNING] PIPELINE(53783,ffff8632d780,python):2022-11-16-13:07:10.258.620 [mindspore/ccsrc/pipeline/jit/pipeline.cc:173] CheckArgValid] The data types of Tensor:[[ True False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False]\n", + " [ True True True True True True True True True True True True\n", + " True True True True True True False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False]] is bool, which may cause SelectKernelInfo failure for operator [AddN]. For more details, please refer to the FAQ at https://www.mindspore.cn.\n", + "[WARNING] PIPELINE(53783,ffff8632d780,python):2022-11-16-13:07:10.261.831 [mindspore/ccsrc/pipeline/jit/pipeline.cc:173] CheckArgValid] The data types of Tensor:[[[[False False False ... False False False]\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]\n", + " ...\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]]\n", + "\n", + " [[False False False ... False False False]\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]\n", + " ...\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]]\n", + "\n", + " [[False False False ... False False False]\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]\n", + " ...\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]]\n", + "\n", + " ...\n", + "\n", + " [[False False False ... False False False]\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]\n", + " ...\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]]\n", + "\n", + " [[False False False ... False False False]\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]\n", + " ...\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]]\n", + "\n", + " [[False False False ... False False False]\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]\n", + " ...\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]]]\n", + "\n", + "\n", + " [[[False False False ... False False False]\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]\n", + " ...\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]]\n", + "\n", + " [[False False False ... False False False]\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]\n", + " ...\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]]\n", + "\n", + " [[False False False ... False False False]\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]\n", + " ...\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]]\n", + "\n", + " ...\n", + "\n", + " [[False False False ... False False False]\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]\n", + " ...\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]]\n", + "\n", + " [[False False False ... False False False]\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]\n", + " ...\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]]\n", + "\n", + " [[False False False ... False False False]\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]\n", + " ...\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]\n", + " [False False False ... False False False]]]] is bool, which may cause SelectKernelInfo failure for operator [AddN]. For more details, please refer to the FAQ at https://www.mindspore.cn.\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:09:23.596.063 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:11, name :top_k_d_7508351121019375752_0, message:2022-11-16 13:09:23.595819: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:09:23.599.058 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:12, name :top_k_d_7466765207923139055_0, message:2022-11-16 13:09:23.598865: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:09:23.601.986 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:13, name :top_k_d_17452859244373013215_0, message:2022-11-16 13:09:23.601810: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:09:23.604.934 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:14, name :top_k_d_9871390996356273705_0, message:2022-11-16 13:09:23.604756: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:09:23.608.086 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:15, name :top_k_d_7508351121019375752_0, message:2022-11-16 13:09:23.607906: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:09:23.610.983 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:16, name :top_k_d_7466765207923139055_0, message:2022-11-16 13:09:23.610807: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:09:23.613.924 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:17, name :top_k_d_17452859244373013215_0, message:2022-11-16 13:09:23.613746: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:09:23.616.869 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:18, name :top_k_d_9871390996356273705_0, message:2022-11-16 13:09:23.616689: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] DEVICE(53783,ffff8632d780,python):2022-11-16-13:09:30.871.298 [mindspore/ccsrc/plugin/device/ascend/hal/device/kernel_select_ascend.cc:330] FilterRaisedOrReducePrecisionMatchedKernelInfo] Operator:[TopK] don't support int64, reduce precision from int64 to int32.\n", + "[WARNING] DEVICE(53783,ffff8632d780,python):2022-11-16-13:09:30.874.818 [mindspore/ccsrc/plugin/device/ascend/hal/device/kernel_select_ascend.cc:330] FilterRaisedOrReducePrecisionMatchedKernelInfo] Operator:[TopK] don't support int64, reduce precision from int64 to int32.\n", + "[WARNING] DEVICE(53783,ffff8632d780,python):2022-11-16-13:09:37.795.319 [mindspore/ccsrc/plugin/device/ascend/hal/device/kernel_select_ascend.cc:330] FilterRaisedOrReducePrecisionMatchedKernelInfo] Operator:[CropAndResize] don't support int64, reduce precision from int64 to int32.\n", + "[WARNING] DEVICE(53783,ffff8632d780,python):2022-11-16-13:09:38.712.492 [mindspore/ccsrc/plugin/device/ascend/hal/device/kernel_select_ascend.cc:330] FilterRaisedOrReducePrecisionMatchedKernelInfo] Operator:[CropAndResize] don't support int64, reduce precision from int64 to int32.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "245 epoch: 1 step: 1 total_loss: 2592.00000\n", + "247 epoch: 1 step: 2 total_loss: 11584.00000\n", + "248 epoch: 1 step: 3 total_loss: 7752.00000\n", + "249 epoch: 1 step: 4 total_loss: 12256.00000\n", + "250 epoch: 1 step: 5 total_loss: 6384.00000\n", + "250 epoch: 1 step: 6 total_loss: 1470.00000\n", + "251 epoch: 1 step: 7 total_loss: 11616.00000\n", + "252 epoch: 1 step: 8 total_loss: 4980.00000\n", + "254 epoch: 1 step: 9 total_loss: 1314.00000\n", + "254 epoch: 1 step: 10 total_loss: 5428.00000\n", + "255 epoch: 1 step: 11 total_loss: 6796.00000\n", + "257 epoch: 1 step: 12 total_loss: 4022.00000\n", + "258 epoch: 1 step: 13 total_loss: 6060.00000\n", + "259 epoch: 1 step: 14 total_loss: 5004.00000\n", + "260 epoch: 1 step: 15 total_loss: 3418.00000\n", + "261 epoch: 1 step: 16 total_loss: 5192.00000\n", + "262 epoch: 1 step: 17 total_loss: 3436.00000\n", + "263 epoch: 1 step: 18 total_loss: 3482.00000\n", + "264 epoch: 1 step: 19 total_loss: 4928.00000\n", + "264 epoch: 1 step: 20 total_loss: 3746.00000\n", + "265 epoch: 1 step: 21 total_loss: 4328.00000\n", + "266 epoch: 1 step: 22 total_loss: 3620.00000\n", + "267 epoch: 1 step: 23 total_loss: 3224.00000\n", + "268 epoch: 1 step: 24 total_loss: 3640.00000\n", + "269 epoch: 1 step: 25 total_loss: 3890.00000\n", + "270 epoch: 1 step: 26 total_loss: 3048.00000\n", + "271 epoch: 1 step: 27 total_loss: 2412.00000\n", + "272 epoch: 1 step: 28 total_loss: 2638.00000\n", + "272 epoch: 1 step: 29 total_loss: 3576.00000\n", + "273 epoch: 1 step: 30 total_loss: 3140.00000\n", + "274 epoch: 1 step: 31 total_loss: 2780.00000\n", + "275 epoch: 1 step: 32 total_loss: 2264.00000\n", + "276 epoch: 1 step: 33 total_loss: 2598.00000\n", + "278 epoch: 1 step: 34 total_loss: 2160.00000\n", + "279 epoch: 1 step: 35 total_loss: 3186.00000\n", + "280 epoch: 1 step: 36 total_loss: 2262.00000\n", + "281 epoch: 1 step: 37 total_loss: 2064.00000\n", + "282 epoch: 1 step: 38 total_loss: 2476.00000\n", + "283 epoch: 1 step: 39 total_loss: 2638.00000\n", + "284 epoch: 1 step: 40 total_loss: 2468.00000\n", + "285 epoch: 1 step: 41 total_loss: 1935.00000\n", + "286 epoch: 1 step: 42 total_loss: 2432.00000\n", + "287 epoch: 1 step: 43 total_loss: 2112.00000\n", + "287 epoch: 1 step: 44 total_loss: 1336.00000\n", + "289 epoch: 1 step: 45 total_loss: 1803.00000\n", + "290 epoch: 1 step: 46 total_loss: 1526.00000\n", + "291 epoch: 1 step: 47 total_loss: 2080.00000\n", + "292 epoch: 1 step: 48 total_loss: 2644.00000\n", + "293 epoch: 1 step: 49 total_loss: 2174.00000\n", + "294 epoch: 1 step: 50 total_loss: 2512.00000\n", + "295 epoch: 1 step: 51 total_loss: 1017.00000\n", + "297 epoch: 1 step: 52 total_loss: 1112.00000\n", + "298 epoch: 1 step: 53 total_loss: 926.00000\n", + "299 epoch: 1 step: 54 total_loss: 1865.00000\n", + "301 epoch: 1 step: 55 total_loss: 1866.00000\n", + "302 epoch: 1 step: 56 total_loss: 1537.00000\n", + "303 epoch: 1 step: 57 total_loss: 1398.00000\n", + "304 epoch: 1 step: 58 total_loss: 1460.00000\n", + "305 epoch: 1 step: 59 total_loss: 692.50000\n", + "307 epoch: 1 step: 60 total_loss: 722.00000\n", + "308 epoch: 1 step: 61 total_loss: 896.50000\n", + "309 epoch: 1 step: 62 total_loss: 1724.00000\n", + "311 epoch: 1 step: 63 total_loss: 224.75000\n", + "312 epoch: 1 step: 64 total_loss: 1126.00000\n", + "313 epoch: 1 step: 65 total_loss: 401.75000\n", + "314 epoch: 1 step: 66 total_loss: 758.00000\n", + "315 epoch: 1 step: 67 total_loss: 692.00000\n", + "316 epoch: 1 step: 68 total_loss: 1184.00000\n", + "317 epoch: 1 step: 69 total_loss: 956.00000\n", + "318 epoch: 1 step: 70 total_loss: 613.50000\n", + "319 epoch: 1 step: 71 total_loss: 401.50000\n", + "320 epoch: 1 step: 72 total_loss: 329.00000\n", + "321 epoch: 1 step: 73 total_loss: 199.75000\n", + "322 epoch: 1 step: 74 total_loss: 255.87500\n", + "323 epoch: 1 step: 75 total_loss: 108.18750\n", + "324 epoch: 1 step: 76 total_loss: 607.00000\n", + "325 epoch: 1 step: 77 total_loss: 531.50000\n", + "326 epoch: 1 step: 78 total_loss: 535.00000\n", + "327 epoch: 1 step: 79 total_loss: 598.00000\n", + "329 epoch: 1 step: 80 total_loss: 504.50000\n", + "330 epoch: 1 step: 81 total_loss: 1024.00000\n", + "331 epoch: 1 step: 82 total_loss: 373.00000\n", + "332 epoch: 1 step: 83 total_loss: 123.62500\n", + "333 epoch: 1 step: 84 total_loss: 441.50000\n", + "334 epoch: 1 step: 85 total_loss: 54.93750\n", + "336 epoch: 1 step: 86 total_loss: 86.75000\n", + "337 epoch: 1 step: 87 total_loss: 4.78906\n", + "338 epoch: 1 step: 88 total_loss: 7.96094\n", + "339 epoch: 1 step: 89 total_loss: 6.01562\n", + "340 epoch: 1 step: 90 total_loss: 5.78125\n", + "341 epoch: 1 step: 91 total_loss: 5.37109\n", + "342 epoch: 1 step: 92 total_loss: 26.62500\n", + "343 epoch: 1 step: 93 total_loss: 1.47656\n", + "344 epoch: 1 step: 94 total_loss: 2.57031\n", + "345 epoch: 1 step: 95 total_loss: 16.39062\n", + "346 epoch: 1 step: 96 total_loss: 4.41797\n", + "347 epoch: 1 step: 97 total_loss: 3.37305\n", + "348 epoch: 1 step: 98 total_loss: 8.80469\n", + "349 epoch: 1 step: 99 total_loss: 3.15625\n", + "350 epoch: 1 step: 100 total_loss: 1.19531\n", + "350 epoch: 1 step: 101 total_loss: 1.85547\n", + "351 epoch: 1 step: 102 total_loss: 2.41602\n", + "352 epoch: 1 step: 103 total_loss: 2.27930\n", + "353 epoch: 1 step: 104 total_loss: 29.39062\n", + "354 epoch: 1 step: 105 total_loss: 3.41992\n", + "355 epoch: 1 step: 106 total_loss: 1.57520\n", + "356 epoch: 1 step: 107 total_loss: 3.26953\n", + "357 epoch: 1 step: 108 total_loss: 2.28125\n", + "358 epoch: 1 step: 109 total_loss: 9.93750\n", + "359 epoch: 1 step: 110 total_loss: 8.59375\n", + "360 epoch: 1 step: 111 total_loss: 23.15625\n", + "361 epoch: 1 step: 112 total_loss: 64.93750\n", + "362 epoch: 1 step: 113 total_loss: 2.15820\n", + "363 epoch: 1 step: 114 total_loss: 3.26367\n", + "364 epoch: 1 step: 115 total_loss: 1.33984\n", + "365 epoch: 1 step: 116 total_loss: 1.49707\n", + "366 epoch: 1 step: 117 total_loss: 1.06250\n", + "367 epoch: 1 step: 118 total_loss: 1.42383\n", + "368 epoch: 1 step: 119 total_loss: 3.01562\n", + "369 epoch: 1 step: 120 total_loss: 38.87500\n", + "371 epoch: 1 step: 121 total_loss: 3.65430\n", + "372 epoch: 1 step: 122 total_loss: 15.10938\n", + "373 epoch: 1 step: 123 total_loss: 8.26562\n", + "374 epoch: 1 step: 124 total_loss: 7.73828\n", + "375 epoch: 1 step: 125 total_loss: 3.43750\n", + "377 epoch: 1 step: 126 total_loss: 4.44141\n", + "378 epoch: 1 step: 127 total_loss: 2.40625\n", + "379 epoch: 1 step: 128 total_loss: 2.77344\n", + "380 epoch: 1 step: 129 total_loss: 0.91992\n", + "End training, time: 1668575608.1557152 ,epoch: 1 ,step: 129 ,loss: 0.919921875\n", + "epoch time: 380117.420 ms, per step time: 7.340 ms\n" + ] + } + ], + "source": [ + "from utils.lr_schedule import dynamic_lr\n", + "\n", + "set_seed(1)\n", + "\n", + "def train_maskrcnn():\n", + " \"\"\"construct the traning function\"\"\"\n", + " # Allocating memory Environment\n", + " device_target = config.device_target\n", + " rank = 0\n", + " device_num = 1\n", + " context.set_context(mode=context.GRAPH_MODE, device_target=device_target)\n", + "\n", + " print(\"Start train for maskrcnn!\")\n", + "\n", + " dataset_sink_mode_flag = True\n", + " if not config.do_eval and config.run_distribute:\n", + " init()\n", + " rank = get_rank()\n", + " dataset_sink_mode_flag = device_target == 'Ascend'\n", + " device_num = get_group_size()\n", + " context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,\n", + " gradients_mean=True)\n", + " else:\n", + " rank = 0\n", + " device_num = 1\n", + "\n", + " print(\"Start create dataset!\")\n", + " # Call the interface for data processing\n", + " # It will generate mindrecord file in config.mindrecord_dir,\n", + " # and the file name is MaskRcnn.mindrecord0, 1, ... file_num.\n", + " prefix = \"MaskRcnn.mindrecord\"\n", + " mindrecord_dir = os.path.join(config.data_root, config.mindrecord_dir)\n", + " mindrecord_file = os.path.join(mindrecord_dir, prefix + \"0\")\n", + " if rank == 0 and not os.path.exists(mindrecord_file):\n", + " create_mindrecord_dir(prefix, mindrecord_dir)\n", + " # When create MindDataset, using the fitst mindrecord file,\n", + " # such as MaskRcnn.mindrecord0.\n", + "\n", + " dataset = create_coco_dataset(mindrecord_file, batch_size=config.batch_size, device_num=device_num, rank_id=rank)\n", + " dataset_size = dataset.get_dataset_size()\n", + " print(\"total images num: \", dataset_size)\n", + " print(\"Create dataset done!\")\n", + "\n", + " # Net Instance\n", + " net = MaskRcnnResnet50(config=config)\n", + " net = net.set_train()\n", + "\n", + " # load pretrained model\n", + " load_path = config.pre_trained\n", + " if load_path != \"\":\n", + " print(\"Loading pretrained resnet50 checkpoint\")\n", + " net = load_pretrained_ckpt(net=net, load_path=load_path, device_target=device_target)\n", + "\n", + " loss = LossNet()\n", + " lr = Tensor(dynamic_lr(config, rank_size=device_num, start_steps=config.pretrain_epoch_size * dataset_size),\n", + " mstype.float32)\n", + " opt = Momentum(params=net.trainable_params(), learning_rate=lr, momentum=config.momentum,\n", + " weight_decay=config.weight_decay, loss_scale=config.loss_scale)\n", + " # wrap the loss function\n", + " net_with_loss = WithLossCell(net, loss)\n", + " # Use TrainOneStepCell set the training pipeline.\n", + " net = TrainOneStepCell(net_with_loss, opt, sens=config.loss_scale)\n", + " # Monitor the traning process.\n", + " time_cb = TimeMonitor(data_size=dataset_size)\n", + " loss_cb = LossCallBack(rank_id=rank)\n", + " cb = [time_cb, loss_cb]\n", + " # save the trained model\n", + " if config.save_checkpoint:\n", + " # set saved weights.\n", + " ckpt_step = config.save_checkpoint_epochs * dataset_size\n", + " ckptconfig = CheckpointConfig(save_checkpoint_steps=5000, keep_checkpoint_max=config.keep_checkpoint_max)\n", + " save_checkpoint_path = os.path.join(config.save_checkpoint_path, 'ckpt_' + str(rank) + '/')\n", + " # apply saved weights.\n", + " ckpoint_cb = ModelCheckpoint(prefix='mask_rcnn', directory=save_checkpoint_path, config=ckptconfig)\n", + " cb += [ckpoint_cb]\n", + " # start training.\n", + " model = Model(net)\n", + " model.train(config.epoch_size, dataset, callbacks=cb, dataset_sink_mode=False)\n", + "\n", + "if __name__ == '__main__':\n", + " train_maskrcnn()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 评估\n", + "\n", + "完成训练后，我们可以将我们训练的模型保存在checkpoint目录下。\n", + "\n", + "在COCO的validation数据集上，可以评估我们训练好的模型的准确性。" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "eval.py config:\n", + " Namespace(activate_num_classes=256, anchor_ratios=[0.5, 1.0, 2.0], anchor_scales=[8], anchor_strides=[4, 8, 16, 32, 64], ann_file='../../coco2017bk/annotations/instances_val2017.json', ann_path='../../coco2017bk/annotations/instances_val2017.json', base_lr=0.02, base_step=59633, batch_size=2, batch_size_export=1, checkpoint_path='../checkpoint/maskrcnn_coco2017_acc32.9.ckpt', ckpt_file='./checkpoint/maskrcnn_gpu_coco.ckpt', data_classes=('background', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'), data_root='../../coco2017bk', dataset='coco', device_id=0, device_num=1, device_target='Ascend', do_eval=False, do_train=True, enable_profiling=False, epoch_size=12, expand_ratio=1.0, feature_shapes=[(192, 320), (96, 160), (48, 80), (24, 40), (12, 20)], file_name='./checkpoint/maskrcnn_coco2017_acc32.9.ckpt', flip_ratio=0.5, fpn_in_channels=[256, 512, 1024, 2048], fpn_num_outs=5, fpn_out_channels=256, img_height=768, img_path='../../coco2017bk/val2017', img_width=1280, instance_set='annotations/instances_{}.json', keep_checkpoint_max=12, keep_ratio=True, loss_scale=1, mask_shape=[28, 28], mask_thr_binary=0.5, max_instance_count=128, min_pos_iou=0.3, min_pos_iou_stage2=0.5, mindrecord_dir='./MindRecord_COCO/MindRecord_COCO', momentum=0.91, neg_iou_thr=0.3, neg_iou_thr_stage2=0.5, num_anchors=3, num_bboxes=245520, num_bboxes_stage2=2000, num_classes=81, num_expected_neg=256, num_expected_neg_stage2=512, num_expected_pos=128, num_expected_pos_stage2=128, num_expected_total_stage2=512, num_gts=128, only_create_dataset=False, pos_iou_thr=0.7, pos_iou_thr_stage2=0.5, pre_trained='../../maskrcnnr5/checkpoint/resnet50_ascend_v180_imagenet2012_official_cv_top1acc76.97_top5acc93.44.ckpt', pretrain_epoch_size=0, rank_id=0, rcnn_fc_out_channels=1024, rcnn_in_channels=256, rcnn_loss_cls_weight=1, rcnn_loss_mask_fb_weight=1, rcnn_loss_reg_weight=1, rcnn_mask_out_channels=256, rcnn_num_layers=2, rcnn_target_means=[0.0, 0.0, 0.0, 0.0], rcnn_target_stds=[0.1, 0.1, 0.2, 0.2], resnet_block=[3, 4, 6, 3], resnet_in_channels=[64, 256, 512, 1024], resnet_out_channels=[256, 512, 1024, 2048], result_path='./results', roi_align_featmap_strides=[4, 8, 16, 32], roi_align_finest_scale=56, roi_align_out_channels=256, roi_layer={'type': 'RoIAlign', 'out_size': 7, 'mask_out_size': 14, 'sample_num': 2}, roi_sample_num=640, rpn_cls_out_channels=1, rpn_feat_channels=256, rpn_head_use_sigmoid=True, rpn_head_weight=1.0, rpn_in_channels=256, rpn_loss_cls_weight=1.0, rpn_loss_reg_weight=1.0, rpn_max_num=1000, rpn_min_bbox_min_size=0, rpn_nms_across_levels=False, rpn_nms_post=1000, rpn_nms_pre=1000, rpn_nms_thr=0.7, rpn_proposal_max_num=2000, rpn_proposal_min_bbox_size=0, rpn_proposal_nms_across_levels=False, rpn_proposal_nms_post=2000, rpn_proposal_nms_pre=2000, rpn_proposal_nms_thr=0.7, rpn_target_means=[0.0, 0.0, 0.0, 0.0], rpn_target_stds=[1.0, 1.0, 1.0, 1.0], run_distribute=False, save_checkpoint=True, save_checkpoint_epochs=1, save_checkpoint_path='./', sgd_momentum=0.9, test_batch_size=2, test_iou_thr=0.5, test_max_per_img=100, test_score_thr=0.05, total_epoch=13, train_data_type='train2017', use_sigmoid_cls=True, val_data_type='val2017', warmup_ratio=0.3333333333333333, warmup_step=500, weight_decay=0.0001)\n", + "Start Eval!\n", + "loading annotations into memory...\n", + "Done (t=1.91s)\n", + "creating index...\n", + "index created!\n", + "total images num: 2500\n", + "Processing, please wait a moment.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[WARNING] PIPELINE(53783,ffff8632d780,python):2022-11-16-13:13:37.483.470 [mindspore/ccsrc/pipeline/jit/pipeline.cc:173] CheckArgValid] The data types of Tensor:[[False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False]\n", + " [False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False]] is bool, which may cause SelectKernelInfo failure for operator [AddN]. For more details, please refer to the FAQ at https://www.mindspore.cn.\n", + "[WARNING] PIPELINE(53783,ffff8632d780,python):2022-11-16-13:13:37.483.933 [mindspore/ccsrc/pipeline/jit/pipeline.cc:173] CheckArgValid] The data types of Tensor:[[[[False]]]\n", + "\n", + "\n", + " [[[False]]]] is bool, which may cause SelectKernelInfo failure for operator [AddN]. For more details, please refer to the FAQ at https://www.mindspore.cn.\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.440.004 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44661, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.439764: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.443.323 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44662, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.443141: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.446.531 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44663, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.446354: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] 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id:44683, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.515399: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.518.797 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44684, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.518622: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.522.262 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44685, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.522084: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.525.501 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44686, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.525324: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.528.727 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44687, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.528553: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.531.929 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44688, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.531757: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.535.161 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44689, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.534973: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.538.358 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44690, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.538182: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.541.537 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44691, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.541365: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.544.867 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44692, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.544687: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.548.083 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44693, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.547906: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.551.311 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44694, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.551136: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.554.516 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44695, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.554341: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.557.705 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44696, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.557531: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.560.906 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44697, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.560729: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.564.123 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44698, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.563948: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.567.320 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44699, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.567145: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.570.699 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44700, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.570520: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.573.865 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44701, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.573687: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.577.455 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44702, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.577279: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.580.669 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44703, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.580492: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.583.820 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44704, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.583648: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.586.958 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44705, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.586782: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.590.159 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44706, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.589983: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.593.332 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44707, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.593153: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.596.706 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44708, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.596526: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.599.908 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44709, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.599729: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.603.059 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44710, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.602875: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.606.238 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44711, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.606060: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.609.408 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44712, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.609225: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.612.557 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44713, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.612380: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.615.714 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44714, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.615539: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.618.881 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44715, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.618706: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.622.241 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44716, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.622064: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.625.402 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44717, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.625226: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.628.509 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44718, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.628326: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.631.596 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44719, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.631422: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.634.694 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44720, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.634505: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.637.839 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44721, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.637664: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.640.948 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44722, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.640776: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.644.057 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44723, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.643878: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.649.076 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44724, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.648888: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.652.297 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44725, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.652119: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.655.806 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44726, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.655621: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.659.961 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44727, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.659771: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.663.862 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44728, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.663674: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.667.498 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44729, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.667318: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.671.131 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44730, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.670935: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.674.854 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44731, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.674661: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.678.382 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44732, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.678195: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.681.974 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44733, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.681780: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.685.576 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44734, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.685393: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.689.080 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44735, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.688898: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.692.633 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44736, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.692451: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.696.142 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44737, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.695960: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.699.636 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44738, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.699451: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.703.248 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44739, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.703059: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.706.668 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44740, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.706493: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.710.137 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44741, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.709958: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.713.591 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44742, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.713406: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.717.043 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44743, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.716864: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.720.471 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44744, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.720292: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.723.917 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44745, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.723728: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.727.359 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44746, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.727166: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.730.894 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44747, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.730715: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.734.341 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44748, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.734161: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.737.772 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44749, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.737593: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.741.202 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44750, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.741025: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.744.615 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44751, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.744435: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.748.027 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44752, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.747846: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.751.414 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44753, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.751227: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.754.696 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44754, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.754523: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.758.154 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44755, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.757970: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.761.542 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44756, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.761359: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.764.896 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44757, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.764718: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.768.301 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44758, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.768122: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.771.687 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44759, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.771480: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.775.061 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44760, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.774870: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.778.421 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44761, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.778244: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.781.822 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44762, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.781645: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.785.287 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44763, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.785103: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.788.682 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44764, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.788471: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.792.022 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44765, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.791842: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.795.317 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44766, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.795135: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.798.534 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44767, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.798354: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.801.756 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44768, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.801578: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.804.906 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44769, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.804732: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.808.078 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44770, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.807898: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.811.336 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44771, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.811158: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.814.454 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44772, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.814282: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.817.584 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44773, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.817405: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.820.736 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44774, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.820558: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.823.863 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44775, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.823686: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.827.020 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44776, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.826846: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.830.160 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44777, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.829986: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.833.318 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44778, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.833146: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.836.581 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44779, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.836399: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.839.752 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44780, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.839573: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.842.894 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44781, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.842716: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.846.061 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44782, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.845885: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.849.234 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44783, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.849057: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.852.441 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44784, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.852236: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.855.612 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44785, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.855428: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.858.731 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44786, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.858554: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.861.984 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44787, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.861809: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.865.148 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44788, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.864974: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.868.320 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44789, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.868138: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.871.453 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44790, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.871277: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.874.568 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44791, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.874387: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.877.671 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44792, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.877494: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.880.769 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44793, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.880598: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.883.872 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44794, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.883693: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.887.065 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44795, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.886876: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.890.186 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44796, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.890004: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.893.301 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44797, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.893124: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.896.420 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44798, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.896243: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.899.579 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44799, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.899403: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.902.687 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44800, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.902510: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.905.804 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44801, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.905630: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.908.917 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44802, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.908742: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.912.137 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44803, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.911958: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.915.248 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44804, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.915069: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.918.359 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44805, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.918186: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.921.496 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44806, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.921322: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.924.581 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44807, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.924407: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.927.669 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44808, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.927489: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.930.740 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44809, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.930566: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.933.817 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44810, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.933639: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.936.976 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44811, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.936797: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.940.098 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44812, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.939891: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.943.159 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44813, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.942962: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.946.231 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44814, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.946054: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.949.339 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44815, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.949164: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.952.417 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44816, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.952245: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.955.486 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44817, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.955307: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.958.532 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44818, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.958362: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.963.393 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44819, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.963207: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:14:59.966.497 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44820, name :top_k_d_5043579305982986424_0, message:2022-11-16 13:14:59.966321: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:15:00.008.518 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44831, name :top_k_d_13999888294925708646_0, message:2022-11-16 13:15:00.008330: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:15:00.011.647 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44832, name :top_k_d_13876671179693792995_0, message:2022-11-16 13:15:00.011465: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:15:00.014.772 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44833, name :top_k_d_13928381274987235808_0, message:2022-11-16 13:15:00.014594: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:15:00.017.884 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44834, name :top_k_d_9871390996356273705_0, message:2022-11-16 13:15:00.017709: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:15:00.021.192 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44835, name :top_k_d_13999888294925708646_0, message:2022-11-16 13:15:00.021011: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:15:00.024.351 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44836, name :top_k_d_13876671179693792995_0, message:2022-11-16 13:15:00.024171: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:15:00.027.508 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44837, name :top_k_d_13928381274987235808_0, message:2022-11-16 13:15:00.027303: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-13:15:00.030.690 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:44838, name :top_k_d_9871390996356273705_0, message:2022-11-16 13:15:00.030500: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] DEVICE(53783,ffff8632d780,python):2022-11-16-13:15:06.728.745 [mindspore/ccsrc/plugin/device/ascend/hal/device/kernel_select_ascend.cc:330] FilterRaisedOrReducePrecisionMatchedKernelInfo] Operator:[TopK] don't support int64, reduce precision from int64 to int32.\n", + "[WARNING] DEVICE(53783,ffff8632d780,python):2022-11-16-13:15:06.732.330 [mindspore/ccsrc/plugin/device/ascend/hal/device/kernel_select_ascend.cc:330] FilterRaisedOrReducePrecisionMatchedKernelInfo] Operator:[TopK] don't support int64, reduce precision from int64 to int32.\n", + "[WARNING] PRE_ACT(53783,ffff8632d780,python):2022-11-16-13:16:49.292.540 [mindspore/ccsrc/backend/common/somas/somas.cc:294] UpdateTensorsOffset] Mismatch size of tensor 593 0 vs 4096\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iter 1 cost time 201.93067073822021\n", + "Iter 2 cost time 0.381087064743042\n", + "Iter 3 cost time 0.37433290481567383\n", + "Iter 4 cost time 0.37767982482910156\n", + "Iter 5 cost time 0.37535786628723145\n", + "Iter 6 cost time 0.3764312267303467\n", + "Iter 7 cost time 0.37731409072875977\n", + "Iter 8 cost time 0.4235191345214844\n", + "Iter 9 cost time 0.40209031105041504\n", + "Iter 10 cost time 0.3951232433319092\n", + "Iter 11 cost time 0.37851643562316895\n", + "Iter 12 cost time 0.4024662971496582\n", + "Iter 13 cost time 0.3763906955718994\n", + "Iter 14 cost time 0.3807048797607422\n", + "Iter 15 cost time 0.37706542015075684\n", + "Iter 16 cost time 0.40575623512268066\n", + "Iter 17 cost time 0.4049263000488281\n", + "Iter 18 cost time 0.4000437259674072\n", + "Iter 19 cost time 0.406721830368042\n", + "Iter 20 cost time 0.4007854461669922\n", + "Iter 21 cost time 0.4026055335998535\n", + "Iter 22 cost time 0.4041905403137207\n", + "Iter 23 cost time 0.39791154861450195\n", + "Iter 24 cost time 0.4001796245574951\n", + "Iter 25 cost time 0.3960442543029785\n", + "Iter 26 cost time 0.3771064281463623\n", + "Iter 27 cost time 0.3795924186706543\n", + "Iter 28 cost time 0.3773050308227539\n", + "Iter 29 cost time 0.37631702423095703\n", + "Iter 30 cost time 0.37659239768981934\n", + "Iter 31 cost time 0.37523770332336426\n", + "Iter 32 cost time 0.37642598152160645\n", + "Iter 33 cost time 0.37662506103515625\n", + "Iter 34 cost time 0.3820650577545166\n", + "Iter 35 cost time 0.3776404857635498\n", + "Iter 36 cost time 0.37567996978759766\n", + "Iter 37 cost time 0.41228222846984863\n", + "Iter 38 cost time 0.37882184982299805\n", + "Iter 39 cost time 0.37426018714904785\n", + "Iter 40 cost time 0.3798055648803711\n", + "Iter 41 cost time 0.3745293617248535\n", + "Iter 42 cost time 0.3935048580169678\n", + "Iter 43 cost time 0.38555240631103516\n", + "Iter 44 cost time 0.3785693645477295\n", + "Iter 45 cost time 0.38210058212280273\n", + "Iter 46 cost time 0.3777649402618408\n", + "Iter 47 cost time 0.37603211402893066\n", + "Iter 48 cost time 0.37601304054260254\n", + "Iter 49 cost time 0.3779432773590088\n", + "Iter 50 cost time 0.37097668647766113\n", + "Iter 51 cost time 0.37497854232788086\n", + "Iter 52 cost time 0.3735172748565674\n", + "Iter 53 cost time 0.37395548820495605\n", + "Iter 54 cost time 0.37637877464294434\n", + "Iter 55 cost time 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"Iter 2440 cost time 0.36191749572753906\n", + "Iter 2441 cost time 0.35969090461730957\n", + "Iter 2442 cost time 0.36065125465393066\n", + "Iter 2443 cost time 0.3609325885772705\n", + "Iter 2444 cost time 0.3573911190032959\n", + "Iter 2445 cost time 0.3628673553466797\n", + "Iter 2446 cost time 0.3597702980041504\n", + "Iter 2447 cost time 0.35895442962646484\n", + "Iter 2448 cost time 0.36231470108032227\n", + "Iter 2449 cost time 0.3699021339416504\n", + "Iter 2450 cost time 0.3602917194366455\n", + "Iter 2451 cost time 0.36194276809692383\n", + "Iter 2452 cost time 0.35837650299072266\n", + "Iter 2453 cost time 0.3585019111633301\n", + "Iter 2454 cost time 0.359804630279541\n", + "Iter 2455 cost time 0.35939455032348633\n", + "Iter 2456 cost time 0.36446380615234375\n", + "Iter 2457 cost time 0.36127495765686035\n", + "Iter 2458 cost time 0.3605196475982666\n", + "Iter 2459 cost time 0.3600139617919922\n", + "Iter 2460 cost time 0.35962581634521484\n", + "Iter 2461 cost time 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"Iter 2483 cost time 0.3682258129119873\n", + "Iter 2484 cost time 0.35993337631225586\n", + "Iter 2485 cost time 0.3708980083465576\n", + "Iter 2486 cost time 0.3619880676269531\n", + "Iter 2487 cost time 0.37052464485168457\n", + "Iter 2488 cost time 0.3644845485687256\n", + "Iter 2489 cost time 0.3704373836517334\n", + "Iter 2490 cost time 0.3622314929962158\n", + "Iter 2491 cost time 0.3693716526031494\n", + "Iter 2492 cost time 0.361583948135376\n", + "Iter 2493 cost time 0.36986517906188965\n", + "Iter 2494 cost time 0.3623836040496826\n", + "Iter 2495 cost time 0.3687863349914551\n", + "Iter 2496 cost time 0.36101222038269043\n", + "Iter 2497 cost time 0.37028932571411133\n", + "Iter 2498 cost time 0.36310791969299316\n", + "Iter 2499 cost time 0.36850666999816895\n", + "Iter 2500 cost time 0.3617856502532959\n", + "Loading and preparing results...\n", + "DONE (t=3.74s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=89.05s).\n", + "Accumulating evaluation results...\n", + "DONE (t=13.31s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.374\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.599\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.405\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.234\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.414\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.475\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.311\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.501\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.529\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.362\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.569\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.656\n", + "Loading and preparing results...\n", + "DONE (t=7.09s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *segm*\n", + "DONE (t=97.04s).\n", + "Accumulating evaluation results...\n", + "DONE (t=12.76s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.329\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.554\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.343\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.163\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.356\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.476\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.284\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.434\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.453\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.281\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.488\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.593\n", + "ckpt_path= ../checkpoint/maskrcnn_coco2017_acc32.9.ckpt\n" + ] + } + ], + "source": [ + "from pycocotools.coco import COCO\n", + "\n", + "from utils.util import coco_eval, bbox2result_1image, results2json, get_seg_masks\n", + "\n", + "set_seed(1)\n", + "\n", + "def maskrcnn_eval(dataset_path, ckpt_path, ann_file):\n", + " \"\"\"MaskRcnn evaluation.\"\"\"\n", + " ds = create_coco_dataset(dataset_path, batch_size=config.test_batch_size, is_training=False)\n", + "\n", + " net = MaskRcnnResnet50(config)\n", + " param_dict = load_checkpoint(ckpt_path)\n", + " load_param_into_net(net, param_dict)\n", + " net.set_train(False)\n", + "\n", + " eval_iter = 0\n", + " total = ds.get_dataset_size()\n", + " outputs = []\n", + " dataset_coco = COCO(ann_file)\n", + "\n", + " print(\"total images num: \", total)\n", + " print(\"Processing, please wait a moment.\")\n", + " max_num = 128\n", + " for data in ds.create_dict_iterator(output_numpy=True, num_epochs=1):\n", + " eval_iter = eval_iter + 1\n", + "\n", + " img_data = data['image']\n", + " img_metas = data['image_shape']\n", + " gt_bboxes = data['box']\n", + " gt_labels = data['label']\n", + " gt_num = data['valid_num']\n", + " gt_mask = data[\"mask\"]\n", + "\n", + " start = time.time()\n", + " # run net\n", + " output = net(Tensor(img_data), Tensor(img_metas), Tensor(gt_bboxes),\n", + " Tensor(gt_labels), Tensor(gt_num), Tensor(gt_mask))\n", + " end = time.time()\n", + " print(\"Iter {} cost time {}\".format(eval_iter, end - start))\n", + "\n", + " # output\n", + " all_bbox = output[0]\n", + " all_label = output[1]\n", + " all_mask = output[2]\n", + " all_mask_fb = output[3]\n", + "\n", + " for j in range(config.test_batch_size):\n", + " all_bbox_squee = np.squeeze(all_bbox.asnumpy()[j, :, :])\n", + " all_label_squee = np.squeeze(all_label.asnumpy()[j, :, :])\n", + " all_mask_squee = np.squeeze(all_mask.asnumpy()[j, :, :])\n", + " all_mask_fb_squee = np.squeeze(all_mask_fb.asnumpy()[j, :, :, :])\n", + "\n", + " all_bboxes_tmp_mask = all_bbox_squee[all_mask_squee, :]\n", + " all_labels_tmp_mask = all_label_squee[all_mask_squee]\n", + " all_mask_fb_tmp_mask = all_mask_fb_squee[all_mask_squee, :, :]\n", + "\n", + " if all_bboxes_tmp_mask.shape[0] > max_num:\n", + " inds = np.argsort(-all_bboxes_tmp_mask[:, -1])\n", + " inds = inds[:max_num]\n", + " all_bboxes_tmp_mask = all_bboxes_tmp_mask[inds]\n", + " all_labels_tmp_mask = all_labels_tmp_mask[inds]\n", + " all_mask_fb_tmp_mask = all_mask_fb_tmp_mask[inds]\n", + "\n", + " bbox_results = bbox2result_1image(all_bboxes_tmp_mask, all_labels_tmp_mask, config.num_classes)\n", + " segm_results = get_seg_masks(all_mask_fb_tmp_mask, all_bboxes_tmp_mask, all_labels_tmp_mask,\n", + " img_metas[j], True, config.num_classes)\n", + " outputs.append((bbox_results, segm_results))\n", + "\n", + " eval_types = [\"bbox\", \"segm\"]\n", + " result_files = results2json(dataset_coco, outputs, \"./results.pkl\")\n", + " coco_eval(result_files, eval_types, dataset_coco, single_result=False)\n", + "\n", + "def eval_():\n", + " \"\"\"Execute the Evaluation.\"\"\"\n", + " device_target = config.device_target\n", + " context.set_context(mode=context.GRAPH_MODE, device_target=device_target)\n", + "\n", + " mindrecord_dir = os.path.join(config.data_root, config.mindrecord_dir)\n", + " print('\\neval.py config:\\n', config)\n", + " prefix = \"MaskRcnn_eval.mindrecord\"\n", + " mindrecord_file = os.path.join(mindrecord_dir, prefix)\n", + "\n", + " if not os.path.exists(mindrecord_file):\n", + " if not os.path.isdir(mindrecord_dir):\n", + " os.makedirs(mindrecord_dir)\n", + " if config.dataset == \"coco\":\n", + " if os.path.isdir(config.data_root):\n", + " print(\"Create Mindrecord.\")\n", + " data_to_mindrecord_byte_image(\"coco\", False, prefix, file_num=1)\n", + " print(\"Create Mindrecord Done, at {}\".format(mindrecord_dir))\n", + " else:\n", + " print(\"data_root not exits.\")\n", + " else:\n", + " if os.path.isdir(config.IMAGE_DIR) and os.path.exists(config.ANNO_PATH):\n", + " print(\"Create Mindrecord.\")\n", + " data_to_mindrecord_byte_image(\"other\", False, prefix, file_num=1)\n", + " print(\"Create Mindrecord Done, at {}\".format(mindrecord_dir))\n", + " else:\n", + " print(\"IMAGE_DIR or ANNO_PATH not exits.\")\n", + "\n", + " print(\"Start Eval!\")\n", + " maskrcnn_eval(mindrecord_file, config.checkpoint_path, config.ann_file)\n", + " print(\"ckpt_path=\", config.checkpoint_path)\n", + "\n", + "if __name__ == '__main__':\n", + " eval_()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 推理\n", + "\n", + "最后，可以使用自己的数据集来测试训练后的模型，完成目标检测。" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Image ID: 1061\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[WARNING] PIPELINE(53783,ffff8632d780,python):2022-11-16-15:24:20.977.673 [mindspore/ccsrc/pipeline/jit/pipeline.cc:173] CheckArgValid] The data types of Tensor:[[False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False]\n", + " [False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False]] is bool, which may cause SelectKernelInfo failure for operator [AddN]. For more details, please refer to the FAQ at https://www.mindspore.cn.\n", + "[WARNING] PIPELINE(53783,ffff8632d780,python):2022-11-16-15:24:20.977.958 [mindspore/ccsrc/pipeline/jit/pipeline.cc:173] CheckArgValid] The data types of Tensor:[[[[False]]]\n", + "\n", + "\n", + " [[[False]]]] is bool, which may cause SelectKernelInfo failure for operator [AddN]. For more details, please refer to the FAQ at https://www.mindspore.cn.\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.815.348 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200061, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.814849: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.820.559 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200062, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.820325: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.824.000 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200063, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.823803: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] 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id:200070, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.847314: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.850.784 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200071, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.850595: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.854.093 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200072, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.853903: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.857.441 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200073, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.857215: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.860.770 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200074, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.860572: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.864.223 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200075, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.864024: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.867.350 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200076, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.867163: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.870.379 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200077, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.870196: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.873.410 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200078, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.873226: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.876.438 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200079, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.876258: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.879.463 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200080, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.879272: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.882.479 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200081, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.882292: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.885.528 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200082, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.885343: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.888.671 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200083, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.888482: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.891.694 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200084, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.891505: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.894.702 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200085, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.894518: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.897.721 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200086, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.897510: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.900.744 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200087, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.900564: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.903.714 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200088, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.903531: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.906.695 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200089, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.906515: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.909.673 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200090, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.909493: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.912.745 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200091, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.912559: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.915.718 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200092, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.915535: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.918.684 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200093, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.918500: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.921.654 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200094, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.921472: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.924.646 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200095, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.924462: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.927.614 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200096, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.927431: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.930.571 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200097, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.930390: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.933.561 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200098, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.933377: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.936.690 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200099, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.936503: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.939.723 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200100, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.939537: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.942.670 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200101, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.942489: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.946.551 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200102, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.946370: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.949.519 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200103, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.949339: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.952.500 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200104, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.952316: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.955.478 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200105, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.955292: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.958.438 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200106, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.958253: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.961.557 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200107, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.961368: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.964.555 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200108, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.964372: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.967.538 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200109, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.967351: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.970.492 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200110, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.970307: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.973.470 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200111, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.973288: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.976.444 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200112, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.976260: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.979.431 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200113, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.979248: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.982.366 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200114, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.982183: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.985.433 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200115, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.985251: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.988.390 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200116, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.988208: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.991.349 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200117, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.991165: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.994.310 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200118, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.994122: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:31.997.271 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200119, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:31.997087: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.000.253 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200120, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.000066: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.003.240 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200121, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.003052: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.006.217 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200122, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.006032: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.009.335 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200123, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.009141: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.012.300 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200124, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.012117: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.015.247 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200125, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.015066: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.018.205 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200126, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.018022: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.021.161 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200127, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.020979: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.024.116 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200128, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.023933: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.027.069 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200129, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.026877: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.030.027 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200130, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.029844: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.033.108 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200131, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.032918: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.036.109 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200132, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.035920: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.039.072 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200133, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.038883: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.042.013 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200134, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.041829: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.044.982 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200135, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.044793: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.047.941 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200136, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.047759: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.050.873 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200137, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.050693: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.053.822 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200138, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.053637: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.058.719 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200139, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.058522: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.061.707 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200140, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.061525: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.064.704 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200141, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.064517: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.075.994 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200142, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.075763: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.079.350 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200143, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.079154: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.082.772 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200144, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.082575: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.086.106 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200145, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.085903: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.089.453 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200146, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.089257: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.092.964 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200147, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.092757: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.096.286 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200148, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.096087: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.099.608 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200149, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.099403: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.102.897 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200150, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.102700: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.106.011 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200151, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.105810: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.109.111 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200152, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.108915: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.112.183 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200153, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.111987: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.115.243 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200154, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.115047: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.118.446 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200155, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.118250: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.121.463 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200156, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.121281: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.124.451 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200157, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.124266: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.127.421 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200158, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.127232: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.130.372 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200159, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.130187: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.133.328 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200160, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.133142: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.136.306 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200161, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.136123: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.139.303 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200162, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.139114: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.142.445 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200163, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.142253: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.145.420 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200164, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.145238: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.148.421 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200165, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.148240: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.151.415 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200166, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.151230: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.154.363 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200167, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.154182: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.157.348 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200168, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.157164: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.160.356 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200169, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.160165: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.163.333 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200170, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.163147: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.166.387 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200171, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.166200: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.169.341 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200172, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.169157: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.172.312 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200173, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.172129: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.175.279 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200174, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.175095: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.178.216 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200175, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.178035: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.181.158 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200176, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.180975: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.184.104 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200177, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.183921: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.187.077 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200178, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.186883: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.190.108 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200179, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.189922: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.193.069 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200180, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.192885: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.196.030 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200181, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.195848: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.198.976 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200182, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.198797: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.201.927 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200183, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.201745: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.204.873 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200184, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.204689: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.207.841 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200185, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.207659: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.210.905 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200186, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.210705: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.213.867 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200187, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.213684: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.216.836 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200188, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.216652: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.219.865 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200189, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.219661: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.222.802 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200190, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.222623: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.225.763 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200191, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.225581: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.228.709 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200192, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.228529: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.231.651 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200193, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.231467: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.234.679 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200194, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.234495: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.237.647 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200195, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.237466: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.240.607 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200196, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.240424: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.243.559 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200197, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.243380: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.246.477 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200198, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.246292: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.249.409 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200199, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.249226: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.252.356 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200200, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.252174: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.255.299 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200201, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.255113: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.258.350 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200202, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.258161: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.261.342 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200203, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.261164: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.264.335 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200204, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.264152: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.267.289 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200205, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.267106: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.270.252 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200206, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.270067: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.273.198 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200207, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.273015: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.276.154 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200208, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.275967: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.279.085 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200209, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.278890: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.282.121 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200210, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.281934: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.285.078 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200211, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.284894: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.288.090 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200212, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.287896: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.294.709 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200213, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.294525: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.297.824 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200214, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.297637: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.300.930 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200215, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.300745: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.304.047 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200216, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.303859: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.307.137 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200217, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.306941: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.310.309 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200218, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.310118: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.313.374 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200219, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.313188: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.318.091 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200220, name :top_k_d_5043579305982986424_0, message:2022-11-16 15:25:32.317899: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.368.402 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200231, name :top_k_d_13999888294925708646_0, message:2022-11-16 15:25:32.368154: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.371.854 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200232, name :top_k_d_13876671179693792995_0, message:2022-11-16 15:25:32.371654: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.375.278 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200233, name :top_k_d_13928381274987235808_0, message:2022-11-16 15:25:32.375074: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.380.453 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200234, name :top_k_d_9871390996356273705_0, message:2022-11-16 15:25:32.380229: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.383.954 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200235, name :top_k_d_13999888294925708646_0, message:2022-11-16 15:25:32.383755: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.387.337 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200236, name :top_k_d_13876671179693792995_0, message:2022-11-16 15:25:32.387142: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.392.357 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200237, name :top_k_d_13928381274987235808_0, message:2022-11-16 15:25:32.392155: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] KERNEL(53783,ffff8632d780,python):2022-11-16-15:25:32.395.770 [mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc:143] PrintInfo] Job id:200238, name :top_k_d_9871390996356273705_0, message:2022-11-16 15:25:32.395573: Unsupported reason is k is too big, k:1006632960\n", + "[WARNING] DEVICE(53783,ffff8632d780,python):2022-11-16-15:25:38.329.469 [mindspore/ccsrc/plugin/device/ascend/hal/device/kernel_select_ascend.cc:330] FilterRaisedOrReducePrecisionMatchedKernelInfo] Operator:[TopK] don't support int64, reduce precision from int64 to int32.\n", + "[WARNING] DEVICE(53783,ffff8632d780,python):2022-11-16-15:25:38.332.872 [mindspore/ccsrc/plugin/device/ascend/hal/device/kernel_select_ascend.cc:330] FilterRaisedOrReducePrecisionMatchedKernelInfo] Operator:[TopK] don't support int64, reduce precision from int64 to int32.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost time of detection: 193.36\n", + "Class Num: 5\n" + ] + }, + { + "data": { + "image/png": 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kNmw54zXZUKJVvY1NMA2jNCs2A5T7qVmjrfqlD7G0o1ca2cGTLKsErnmJ2MrJjQogGU7pZ6UKqnvM84R5RxrtTTidF6CcUZcjnl6d8eYbT/Hc8xWFJjAqCAtAVWc8BY8CxnkqKJOqqio4TlSwcAXrmrNSIZnzCWDag1HBbPwqM6i2vM9wtk7kmZjUpS4IWNOrXzK1RffifNNCdhIIBEzxmcHxSlr67WVBKBrotc9blWzLQ2PGUhi8+e7PA89udJ3/fVmNya8tM1L6lMx/yWkx3RH9LupERMaUis/DKH7U0L0fn3Y2z5bOonnmbqnyMRnNLAry/kX2t5FvTs5CpkyS635WIMvJFt79Mh/jlanrj/3etF/E8MZ2gHWPswfWB93msPXaSmgi+qHBy0q56qFYKaJtFZDHBFXHNi+zF5ZpgxKwxGnStkd53raVs/5szWppg/GqR3cpKdDYktroK9NtTeACiH6xFQY1jaeydPWS1M18xlJ+w3dSvrJgkkqsuqk2K2vROwOgbmkdhVxbEE4pMrYkSGcooD+qTLb0pZKL98u0lTeMFShmX6eSAiZIAGyJk7yszkylTP5tSSR3+iAFxBp4wmflNDFj37sZObOJlaVTExX0IBM2Nq8UOoI46GZJHREDHQvVZwRLHOQlppl+9rkdNw91klq2ftP6DcGDYpWJ8WCtnAZFlt+rUdGZz6Clz54RAZiQ54pdl1SzMYJBs2RQZ0VT+hBMMavMOtMKCt3S6nx2VLnq8kZL1Ghnp5JWkOjacUo4hnz7JiDRM1WXVHIEVdcHUtT8QeaxFfFby2niGEvU9S8Fz2Sd6rSxvqfERjuDzMiMkG26vEugJrlCTcAYNtp0wbN7761txJHyIdkuIl2/T+BlibJEHXWaGhDbmLQMu6Fo3sl0z2M94P0J7zlY7A1i+2PLfH1AGB+peSaBYrtsIpzzNhu2duKTk25IoHXac7Dodee6Gkc9fmv22ISU5u422R8Hs9nJNyGCv5wda6+wsbzRTJuFiueSsYH3OwfHUbZ1HHIQHDhF6c2gEgDYQkYzeJxm4rbHhHTnEAEovGDCjLlMuNhPIK44nRhlmnFczuBasdQTHr57idM5FB+BAToBvFcaiTKaC8CQvTcoBaiRfS1EYCrgUt2A7KaCc5VhmVDEMSVbvw93JqMba169ju/kc2losRUcBD2vUYo2VOoUxL4/iOIucPrn4AXXjLs5tM1zXepTsgy7c5o9mBb368BdVQs23VmzOYHYC9mtj742gHL6iYUOx7ATkwYPMpqYLoAHM20whoYupEsTLUTwWZGWwNG28f+zaJL7ooPay6GVy/ooz8ABrS5kw7+m9k1RAb7cuZ+dcb2HCAzXY2pGXT+TBhTqvUWgj4b3Fso6N9q0hF/MMMlYhj5rWy/afNZ33wy2gmuY88tb9A4Hv6spuT7U8I2RA8WcPf1cQ0H3UleKq6igVwFsCSJIg+m0L4j8udEs6+htWlgSI9pdL6/2st3ffgAyvT150MU5jY3zZjubulFXnsHfGtae51eYUyKDkiavtrGlqehk3Pu1qrpdCSIGQ9/X70SaywSAYgEKt/V29bSzRGHXXRc5heB+RTvhFcqtt/dOCyg/eUC3rT+b6qxVikA+63tKhX3cU0LKW2Xo6lpRJqbvKy+pZxkXcl6mNItk+qcqP3A1XFJr+TfVTQ1fIc+w6Rg7D8Yyy+ha68cFwlKLmyWyoEttFnPL9/6Z43ci71PPxibbHshytjVJV+fMQSd3+QfHJQXgALlc9Mlro5ai6rptrSW25C+Wo66Sm4nzfcuAN9d6N71+FNxGYPi9Bu89WHQHCB7UEEneJxsMd/jsvfR85UCb4WzsZDAXkBzSbj9QoQImlo3ZCMEsyViYQJdJsOz39eW+hbMa373eEm1mcfQMZmqzpKymGzJ39qN+e49QJMtL5PsLWtys+sCxsLazpajcQLDvoQylydDUUfNOMeNoKT6WJZtMDJLd5660NmffjDeIsBCDccYBBftC2O0Jr735Jl75zBuYy4Tn772I73v5Lg6HG3juxnO4eecGfu1Tn8OxPkEpL2KeCHw+A9iJIi+EUgvAMttYeQ9iQiFxcBcANE3YzzMWsISpJHsb6xPBnwrpnoqYefW9R2gdcU89NIo3ghIgOcgbM7hbvAUAFVXbbfnYXQx7F+w4ldn2QVXNNFevrxQRwokhY+T772wcE/9x1SDE+pKNnO4ZSf2DYtwbTHa+s36aYyj9n3QvZE+DrT0WOaDrZ2ZB7UFPMsO8bWiyk2UzA0QUGW1qA0eglZFmD4iPgRn7dmyug37PUfCB1UbIB0T0yazcF532Tu3bDLDwOwCZfWTAFZP03vuS9Y69k+lXfa1odgKULvoKg3zmAtTyfA5OjWLBX+tVG4GhFxE+UfuRvG859AKtQ5vpQyQHegCCZ0HR/gRdbY8ZUaQtLMCopPizOozGLyUcHnf4dG+mBH+KSwVQZN9ktd9AqKiyj5rgM76zHhpESuHdtDa/Jj1Ndp8i6Oq53pz73kbk+vw/sqBU/vMVAY0tNU/SEjTXBJ+do+ezQs0QEUqpjp8HIuteNH2JpK/qNirNITnCx0ZteZ1rlaQPCcGJGST7DhAbo7CSuSzvgMzQWaAT+r/dW6xINf5DK+t5ZlFkNbeXgz551gbmXCPA3Fq5InpsaoJjfROA7gtkoQnQ7++05GiKGFICiS1jY4dpQQ+eoRywhX1nZkzpYLPK7PuEAU1Iml5hQpmL8x0R9DAc24cqdbvfU2RPNDODa2tzrburIIbZddtEaRwofu/pKXmwOIhH6N4eyhSvRYBvdC0ml3J6VgoIEUGd/rcO6C1pkG2P8WgeVqnHViTZgo8KSLCmNmIrAGttayujbf846XBZgWVnOqz5bJ3MWOugWKMB6DJ+cyndqoat7OsY8P6C975nkWVpkRlxQhsohl+0Hvw+UNx0qt1Ro0Y/RAGEo5HLBoIwvdaDHcjDtcYSguT0oFMo+cADU5LRPf3MgGWMiAKX/rARUyQeyKETsGRQWyczuiLvlaTgORk2U8yG4BbNU4vU0i1wYmRhNvq6gjWtinWwaPwgZYuciMpHTBOA+YSvfO11/E8/9StYjsDHP/4x/LF/7vfj7o13cP8RAa/v8dprj7HghE/8yC3ME+N8+QD7fQGwx4KKmRYwCCcuuHnjAIBwdTyicsVuIpxmCbjfefsh7ty+wI0bOzw9Vtw8HFD5DK6MUoCFF4CNjvBxy4FED+7sEDXjkSgUNM/jkcFmCWHGLdpqAyYNtprldlGWuvYcF2olId5x78APEvAxp+zw2OEF2dIa70QQKvUG/hl3QGfF0vd1oJbpF4FrPCOEHFAKcPoMbhikLR2SiNA8j3Y3yie6sbr/v1E75rpxVW9aPrhJi/jMPp6sFTIoj4t2wdraNOBWZkMh5gDKOdUiU7ana9xcR6X64gTreE/qp37lYTi6WVaIZAYidYEAXzJoA5CTAdKu0tNJ0vKC69rmfXL9WVTpNjOxWq6hZqIJEUFPQfK+Fu2Dz9bY6oBkP4rvk4yRWLl3jf6Ukn56Z9bdPWpehIIf0C6V4+5dNnq1ouCVbib/Ozl3eqB9LvRN3OW6Dtc+c12lIm2BfoMWbGlz9IXUWQaRLxs17uSG0p2XoGbMTmHOSVU5oIllSwPQ6uEsK95/5UmGjte2PLaPusAA0PgyeE3k4nrd2etdwcMWWLd83wSEydJHvazBR+5T0/VEOwsuks5a1Wd9JLR5UUvAcPCOj2XmK+VrTQ44j6tiWAdza9sg3ZGxDCwzDQEfq+TbZfSJ2qCvr6cXn6YW1y3JZ3AtvmEbbSw5SiQLh2QuZUUGYmFxq6M7P9YTEGgVRk8DBmy5L2vrtgTZRZPY9Zh2saWpVWok9WC010E25hjwPof3PrNIcXiBMXHsSdke+d452naWspFH87cp5/9d85s5Y/6E3bkmxbUq8v60bzA5MjkgZYQRdyilrUsdpEYIrQba7jtW/TbHI+Oj7zev9UEnbeCuKodaekrA3+PRkZaiHruKoVOHCb32ZZuxozqBagHhEo+fXOLVV9/GcmTcvP0izpXw9rv38eYbT/D222e8e/8KF3euUKaCec9yFPiyA6H6kkSiCdM848nxCeZpAqYJBXLK6jzP4OUJvvyV1/H88zdw5+4FvvjKu/jHft8/gj2A4/Eozp2e/EmgJvvajksY1DZwXvM5gTS40+9+lL+8678DPjMeXNVQUendORcNf7Sy5vsZPSCIssJGESjae1nOeifP/jJT1x78faMXqzFd0WRDcFuHPcm60oaI0iyC/pKso8mEOSTh+NslCS0OZhP7Z5kWK+dBC+WZYn7GLMt1fYzxavvsBj/14Vqd6fs/U7CsjrEt/+l5J9oMB8VO62SEk9CpOVWT1CwHBYWTEbwGdZ6601tTv3hVb+80e4q5pVF2o1gdf6vCJNGcEKMfxRHwwTOIt0wI8nfAs+gmC1X3P1kyzN8wHG1ZLgd9zfGS2dcIS3wWFrY2wGyDUUfL1I4uVobMNgUJiciDECVBG67rh5LL2DH2lOQnaTPK8p2SGIGNqrMmgqdVwJgTqO3YuwJCD16un+4welHGxaqzFQ1SLzUVtTxNMYKpP20CQOgYNPdkG5m8UTCB2bZ8im4ivrcmpEz4KDbkryUsIlGXcUrINfW3uG/RJkKL5oTLyu17+tX2mKZMg+PoshwiB+8OW2KcWqQMbxNVwybJWEgBwyYdngWtTUk4oCVI89wQ3yq0ocZXOjrRxG1Iqt+Txk0lcN2Q5TKFVA2CGZUtG+BJ//RWy29hBynjbQheI3ci6jaXumUDA7OGNZIy4tyVZ5CXTBao7c8WUteYwAHvE3jPweKcrnxoHbF2OWS7jyk9t8MsiEIYKf7ZO5QfJOgDHLJDHkyZqHDHstS449CyJ+1djWFlYibDBB9uHKVPYfYJUX5yAx44mqBlOkhxWslyKa1B8ONGspSu6L0dgCdx11VGBYxFla3hHhY2ZxbB1beRhyInTFPxItHvdgz64Spkjv8ONy5uo+wJcym4mGX53K0bF7hz+wb+zt/9dXzxC9/Aq197jA+8cAvfePAl/IHf9/346A88jxc+cAfnp+LILQDOXHBxY8YL9y7wX/zVn8SLH3gJ/8Qf/t24d+cC9+8/xb3DDXzmV7+An/m5z6LMO9y8ecAv/sor+NBzL+MjH7qN3W7G01PFft5JJlmQhy0NdAOfZmyFnhQr8Uo8bZQjyA+fcSNcgto+M2cHQ/TOVlKugDrudnx8eIjOm8afvr+EarRtzqwNWuYR0Eo2g5fybF5q1+2QzkDa7KRmTvPdgUpBcRByH8nkgqNOyvSyID5m4BtDSpmWgbfMlGa+K+muw8yltoc1nCqSSzcbWmx//ubWbGs5TRtYMyZNUngQZBn6NJ4+y9Ecqx+Ovek4C144jRcz+52CfuiNGXzzwhOsAvjEK6W0SwB9pkd1zKR3wWrvk38W3qafaLmiTeu6Z1sARzNwlZm+kBNfMpX0V0gkhQi7muscdNiyQ3lvmnTWxWire5B93Ih8EZ+JhO/bskNv7P8ifN+sJHFjoPQmilNKO+A+iDRactAlxREA6QLDmuyeMBeIIcvzfZwVDaVV2MWmRR+EokTMy6vh9DaFZZ+1fBozAH5VThMUaf3N7JsNndJ9mkQXyNVIQYvGXOmX1X20dkBTpisyX4nsUOJLn1Vs/IHAuFCRFSmJnvY534XpSUE7PC1tWamoHlQY3q0tjSWoPi5uk3Ctwy3vrGcaZbxYVz9pNcZPKiQ+M6Vjm9Wdk8Lsjh38Y3esGk4WKLGtxVgn/Ro9nhJwDS0httheJuRAqptNDKK2ARpvLB1mazZe7FcZ5GtXMm+uQ/X1LHmbZYux09uG8qsROKnCawJ46yOVwNkqaCqKBGI+YMwSWJkGzfYaaPf1Pto2iUnBJEg6NtMx99t8zaz3Aa8362WwJbLSzHuqdzMAH/C+gfd+Guq12SATvnDkjKEaEG9Py8D/tnxIjSOA/rcGn8R4yUGwsvmfGa3VPWiEhHe8lz2OrPAoiR8ROQ5+sEdydCl3snHYuGk7lEZypvv+hl5RPGMJTYNjVnTmTD7D4XXa9eOX6myWuqSMfuDS4xMxG3cAACAASURBVCpWhHaM28/dwJmvUMoMnBnEhJuHG9jvL/DFL76Fr339Pp6eznj99YKHr7yDf/DpH8aL9+7id33iA3jwxgOAD7g6PsH9coVymHD3dsH/9n9+Dh/96BX+8R/9Edy6BbzxziX28218/nNfwzvvPsTDB7JX6P7DS3z+c1/H7cNH8OHvvwNanqag1zbEG71bpeh3DSI5AR7IpDH04TFC6LdGvyflmamXZMTstE5ItvzTgTlucZl99m56A6fj5sv32qPR8x4/M6BhOHMXkmHXoJeqnPrIyVgQZN8iozbvuXFQQSXtfxtMkPe9cXp4nYAK546b71TFmMbyHiD23WS+jTvDtvZ8WOevGYIVNM4fEVaznlMBaZDd6kbBO0rDxyDGQpYmUyogS7mKOx7CeyxBCVGc5AjEQXVN94Ink9oQvtPTaHsH1Glc0l2BzOHkJeiz5jk47XXKis9J5CnzgCfD+uDD6NL5Vf14Z4fTde1GyJZtQHshuyCW5bIUmX02fSL9ksPi4woCTeRoKm4xQc+DDQBcY/upBfw1zeRs4CkzxlX26lFyfknGvHTvrWzFRp1Z6K8bIw/MOeiqHgBs5mjrvVxvk0zpkgeSjKiyKpO5WTrPlRvS5S0fcBzWwClQs/v/bBmQ02OlW7TGDXp5gsfoRO0qK+OLHhrfKOu4vgza/aCMGE/rSyScwv/KSR4AYKqatJG+wP4kecrtUjIpRuMcqNs4T1D+I8gdijCzH0GX9a2VfbEZ53MbfBN38pjonV22JsGhd4VySg5sJe4cca8kBYxA3Jvd4+t8viU37dJ57sayb58JusQ5eIBry/uA7L+2PQI2viJmpuSu8eeIE+kjeO9ns2VhgzGW/Yv3LInAzS/pd8BppkimHmQ5jHdaWX+WNzrg/Qbf1j2LIQSiKLKyLiXNCqAVmFJKc3Jb66QiWMgUjr9KrqwoCYM4D6Jcit6pZwrQfjPIQaLNDuU9JWH8SzIo5gBIveGwZIMQexum0jrh5gS2OiNndM1pkk3FExVQM/OaBIzlEJ/IaLfZ9Z7OAhWMijy7qi9DNmWnJbqTZMV5OaDiiKKHBkk2cUGtE+bJMuczCp3lUAkJk90ZqlhwmifsiDCj4PLRfXzylz6DN99d8NnPvYZ5uoEyz3jtza/i7//8L+LqyJinHW7MC+7evkJ5UPG//MKv4tOvvorf+6mXMBXg5Q/exP5wA7/rd/4OPHz4AD/1K5/Da29c4v6Dr+BLX36CD33wQ/iRj93E/s7zmD/4EvYX93H1xtdxmgh0OuGdBxWXywQujOPVJfY37wq+ZQFAmDBDjpCRo3GAWSYByhkT7VB5cR4QAyG8WZKiLVPxC4YtozuVuCA5GNt4U+qT4/Vbp7YAWLh28qGGlGOWSpIU6tTMbQIky1Wb+Q5PwNihX4rbOiG9EfY5FGl3ajP7BJGvin72SfpdSnHHwp4xTE/EDC4QVw+YA5uDMZeNyt1KAQCzOLH5ImyUSZMEJl9he68z7u44i0eFHmQmT/e+AkDVa+I9cIt+yQuA6TJzQ5oL34l0lUHBspzhgWCaGZOlbrpsskg9uQmoHFoQ50/Lmj88aG2WRgN1WYC6AKo3wIx5t2vYNIJE4RG7RD01sMpq9zxl/B2zvSnRhqC5sHfMUE1T6GgPHAkoc1nx7mrWydreCGBIeTO/YzxYfOYxvHeRhjgJGZBrMajIqcsuT7OY21q3+kmyNIzlUDbfMVSULukuVKejDYJPwkywu9jFdAb+xVlL+G7hGvJGaWWl6ZhEV7DMqtqyfVtWHzgw5JRm39kkVzkwo0xiq5ZaUc+L80jVo/hLoqGdoGv6DfqPQFiUh6ZJ5WYJ3YFAB1zIV/aKrY+92YBe0WD6E0DaKwLmRfbVCdNhmnWmmZOeQ3LAfREVg2tFKe1MMyCHzcR4t/6HUK+C9LCzZVkAFBS1F8G/SEGg0gcW5FpnAZnwDH1uPLtKWCOCrJw4r+kQsForeDH9JeMiCRFbucJYqp5CS+FDlckSWQU7TKi6WifLYrYFy7IkX9BskyTYmFlm2PO7NtZmh7VM1FGdh4WMJPs9ddzkUBg9IM5wzgGh4luKrSpoVb4nx/Igw1iD0+c4TdV8RqdBFHKbEqclK2+ZPeIo5jbIjVaMv9G0xpD5WxkIwLlWLEsV30H7U1n6vjifWkAHoBQfDzDrlSexTL/CkmLKJ2w+EezMH1SfdQ25tllGg0hkDHg/wrcRLBqzt05bn23ZysJt2OdGmeUgrK1zPZvVghpWNudJNb/L5Xa9WzNpQBdYelCa+qx1kzpvXYeS30at3LoPSXG/GyGcBttr0EmO49AEtWsqBI3D+fQ+Ou7WD0rvEMAEXmQZyETARDOA4pmv3U7uaRJH5CS/aRBFNLnyK8yYr87YzXvs5glvPDnh0595gM989i3cf+chUE6YpjPeeJPxv//sp/H47fugumC/m8Bn4GKecf/tB3j6+Cm+/tU3wATcuLjAbk/4/g/fQV0YD+4/xXO3bmAC8Ku/+kU8ffoAL7w8494HXsD9d084nYB5FkNbAFxcHHD7zgVefvkWbt5YgPMeT56edFkRAZg1iNA+YVGaJMVpM1NUYMtjmiweJEDMtqDldzMo2RiFU2B8ZyVXfJn4SvaDrk/B7SE7C7Qaf/+2ASGr61mB7RkJ/5WAvBclB3IxW9lmFi2geka1MAe7QX8TvxaXRpTc2ofPz40OiN9WddkSwuxAUPvMZNuchL5ZdxgQjpjh4L1kCB+VHIR4Df7VrhTZGsI83tUc7w19nJeWemCGDZomxzG3x9hsPvV73ebmZ1h2vw3am72AuV8pwM/lCUF3S+r1q0i2ZnBcDvW3ZqZKSW/tNjqdwyJ5kkWd+SbRovR1ftOKS8JbDzV29W96u3GSrS0dey7huAUtAaq6nDuPY6aLBQbmwJHMahMVdfCqOv4xgoQUpCKcUsPRZ7OAdnkox32rANzuUaKhdVE73o66PvOZmKRYm1NJIfo+B0OBK7n89T6E6Z5GWm2I8zUKGtQQMTJft/pNcOjbdxw49ukR9EResjra5HCvWzxOgu5FTePZnDz6DGh0l7wscp1sg12iLrRMcm8FGD6T3/t51Osp73vQgPKvpi8NP4ScmbjFewQm8/MiOOOaVhkorxgKzXZz05lEmmyLurcVWdgj8wGoI6InOrs3LZDeIoLLUcMfylvJr/ym4+k6W2o029PP0Oe/csYWN2Oqb7f6BQBSAOvtUdtbOY+ptXV5ma3o33gmtjaVbWzp9V0d8N0F6pXZtwr/xp//oxyOTqdMrPLkzGbno3Qzeq5olQfNaSCd3WuUMhWEaISSNCXczm52y9UsO0u5OQIKrTLMOevtfclCnOtFWiKUjGfUlRwfFgfQr/RYtWs1bhweka8hMI8ieoHolSkCCgO70TdiyQU3yqTANbQYilkyyXTCvCPUOnuPJ5wAugDKIiveyg5MQKUTCgoOS8XxTFgq4eZNAi038dmvvYKf+flP4ec/+au4ezHh8jzhuYs9eFnw5Lzg8emE3QxMdcHTpWKa97ixP+ByOWI+70B0xtPliEXpcqA9GAVHlnsTd4eC3TyhnBYQCIedcNmjK8aLL9/B933fPbz43A7L+YQf/cSP4vf8yMdQ+amc5Fu0ryCgLErHCYUm1GqZ8DRrzECZJyx1gWXRgeqZzojVsyHNvqbIxrXLZcyomvNU8742mfF9lvz6bAvg76XKsTbmsgCyuWqg2F6h7QCvMRrUPRe14DOt4RgGDVqiJJRWlrelS/9jlun29dQo1oZ0C6yHzaIa/Rgnfia59LbZaVVg+9siMLTa4iTHgMnXfGkZ043aBXcOo8tSYw4urumLzXIy+pk068k2PTIfCA02rHqX2e4hz2zGckyjndTjx9RH1zx4yTTp5SQy79zwxFYAueXIdNWnpI05XjZLtLV2F65f86wB0M2U8po2VWfUbe+q3UFnM/Yq9I6LzfpkOcxt21Mfx8wkPdRwUptfswwq79a6iJ5Jx+d7X/t6Oz6wMYxvkVANepKOAXt/2XDpq7frUXSW2+vgkAGXrxREZR83f+cVX7VjQIm2hBSsJ8eZO74IHiCYbC317IxmlnpZWrmyvjjfsOgRkT9JFlXmbh8rr/hKu6GJoRYvx5e5CQqMp4KmlARD2oilv6Xtt+K/LIvqldhfGcGbahjFja+R6Wa2UZMU8f5ahmzETP/Wc3OxKaB2R9o1xkurbFQ+G5EzgjB8v2XWSssidzobPzQ4cTseWVbkZ0sSAMZDm8EVAPhe0NBvbrf0kSXUXOStrrScNbSs9HPF8244AhfbZ+2oWELKcExtIun2TA+zN1K72p/a703eeE///uQnfx0DfnOBT+9saNlnw7e3DNX/T0EjwuZoDCmlPCDRlwtSVpxSfSlg2gpycoDKfdn4C04Z1T4r1reX6rf62l/MKUzfvb34HSVo4eVK1GPOkd9N2Dl4qxlPCzhY+9DRI8xSB6nstc6ekmHVL1O+bPdYMagwyiz3Uxaa9B7ICvAOpcjyTLem6WLvaS64mAtqKbh1MeO523dxeXEXv/b5CxQq2E0zFmbcvHHA6XiJy1rBLHeTVXW4L+YZd2/OOJwr+IpQueBYZeZTFL4EdafjCafKuHwq2c7DtGB/2GPCBAZhKgXfeP0tnM8n0OlF3DjscLpcIKc/FFnmRUizGnHoCQC/wF79ODGOxZIaulyJgn49r/jsXjMA2+Pe80O2aHlWJWYc+nGlcLbzM2vfgk/u8VQO2EhguNMFpCWsHWwmPrLDrs+yZ5d43ZciNcmQwLelS8we5GZNAs2wdRjp/8/Wk55lVpBEUCsrBdn4W+2t/pBkTHIKtS8l1dUETsmJi2WD4by298ICcJ5Lfv5Wf6wtbO8SuY4exfWZK7kUTAUi7QzImheNR/O4UeprdMib8Tq9thwoZv3d6OuNvm/gRRxtr/IOMHq0OrTpo+HT8feq3Y1BYf0tbBg3AmaHASG1HTp8vVrHE5XaGUErO4sd2P5HaumslQWO5oijTTC0Yw/z9rytvJQ9xr2sgsBV4koUk/dDK0lqwuiB5GtYzzUQM/PDecz69joa+hhR4zP0M7KtyqKVHNiqINOr3i0y2Usynntu/LzSp8IDMTTGJ+G09+03pFy1lXnKKQ2bOWyTiWFrJGBlfw+woD8U+aYYZ9ZO+PirXdzR+I7cPst9iYRRltOuq8IgrWznhEygvrId3HyIaptednb82oRtZhqvbGP8EbzrNtBe1/+yvs+QJARRkT2JwWgCPerlOGjr8qdJBef1je41tOIOtVx/HlT3ZULGezINeH/Cd2TPYq+QTZlb8MVsyyWzAdRjHIgiW98Zk/UMnsVj24oN4NRmBGQ9vgZ2/Hbg1CtNa+Ga5z0uZbsdmyX0Nv3lvDcsAhLeqFvVoiq79lm+b8iX/rkyXY9T/r10ZaB0l7oLplnwfPL4KZ4+ucJutwNwQpkY026P4/FdFBAOhxnLWbKh80XF5dUZP/trr+H23Zu4decAOj4FCPjqw4f4wpe+gWnayZUX9YSymzFPO5Sloj5hEE2oXEAMTDzhoHtQH58YXAsO+wOmyjieFpyXIwgL7lzsMc0TSpH5nCsGlqsFjx6cUErBdDjg3QdHvPzCXfwzP/ZH8EMffRGn4wNMuzOAnfS/VL2zzZwuAMRgnIOXOJZ/FpK9P1OJPVJF3/FAoFPOzp/dUea9UxtBoQVPtmdI6rQ9tUWNTH4/OyCbgWj6vDoRmIOP83NzhnwvB0K2N2xg0074adq3HGwqH7qjZX1PFbLJSnZw4Pl+b7zhYc16+h6iHJS5MWydlDbBBGSjG3LK6RCRbk+nOxEyW9Y7+nHSYLtkzWZ6KE3N5kNxrG8lfc5ZXDKnrcc9OZa5H5szEmjYQn8o3TPr3bbDAyAy3rkasOzp0i0EpXDzKxJ9fRmjn0ppRWjlUG/uv3QsHTkA28uge1q0gY7SNQUW7eXmybakoNBkKAcbvT2IJVspGDD9kVbGbAXeW4Gp40+6RM/2FHI/TspvhWVvP4D+1FWgJXkv2NZ1l1v1cle4QnHp5M4oa/cR2jyIlE98hAgFpXPcOKSGm/eJ9S2Cr8gRWm3glvSl7PmPJI8V7Wd+5EVGXNaOONUWDKrkNOlXdABoDoKyU8XDtgveterMlT6flf9tppHE+MD3IvuYmD7Kz9rRYCZJXDPrFY6WbEu2zj672Vkv35ZxKs2TRrbUPhGhWXpI2l9fyGkuU8c7MgzrrRWmn71oxERuD3023+1B1MmAHf+LSgjDleSbY/BXvlO2JR7MGc8kHdbQKusFiAXbTtBm6xbkqbZf1mUtArra6XbHS21QDjqtHbeWq/62z918uPuiS5IVQzb6eWm1yWlsol/Rn5Ax01HaFvUWZcD7Eb6Nexa3DVfvFLujJB5zX3j13nX1Gccl+fPn1JWPTOu63gbvfAVGg29ba36nd7rtvdyfVdbc0RcamMCb+DVtu0GVmQhCbBZu2velu9z2MwkerejbKpd8iXZ+H6VgnmYslTHPE87nE9568wHeevNt7HYFp/MJ845x794O7759hZsXL+LFl+7ieLzC+XTEnQ/s8fbDJ/hrf/PncO/eXTx37xbu7O/j6dMFrz0kPHjIuMFh8HZzQZlvgC7PONcjgAsPBWQ5xhlnPuEKe8zMoIXkhL9acIQuF6sEPgO7w4IFR0y4jUosy0mVGc4M3HruDj7xBz+GT/zuD+CLn/kSjldPUTCLIq8LfPcPL5JVncxJrOAqJ6sK2asepMCwkLvYHo7EMz5DYjMHFrx0x1Ju8qnpe6sz7W91nvScSzvWfea7d4zXvBGyk5e39kkUIuMxjr/GNxuQA7+q11ZsyscGLg4cUh/6BKtgYauuODXQlrj1mX6tMydhrtFrW4FWg3Pz7raTCqA53KuNd9d7CtHLZ/rczBT28ReyQ7ilemn1QjgRhssmJqv+tGVyHfEel6KXe6cl+wniiqPEi8weYPaB1HY/cm/aMpzLWnRh426/pXHM45J1ZMx49h1sx+WbgclStasVGoeztXK9Lmn72s4WU+O8brXcyrUdToGsjnT2ceWw90kGRKAVwZcF+yLnzaFahh+RnpLM4ZCawVYM8/E1zV3qXRDWYhPQ+A0bEHzGDSvE3+0Z89b+h/POUXFy1lM9qe3m/kOujT7IdMg4EOk1L2gTV/nE0xgeG3t23MmGkyjsiSP1DH6lCAarnp6yCjbsnh4AsrUiEmisd7mQ+SW9nur5eYPHjSLOBhxJhvYdDUI0KA5KJhkjwIJzKRNnByBe6+yP/Mm6KGO2Zc/6ZJCoVFlW3NZvCQiVjqznVQdvHDC9AWu92AfhvvUpt8CMZjaSOl5Mttff4k7DKc/auDTomP501at84f7PNauUBrzv4L1fnVHKOuuUFIkZkP6k01w2Qy5jMw1tpt0+S46jkKEes4lUgHTGCkI5lGDaFZ7UfPf2tBPr6zXI/5rshZNgz8OxznJgutF/j5a6tlsHUf5SSvXoBnuyrKDV19JsTbve8VwHltKnRU5Wqwv2t3Y4HxfU8xEvv3wP9567wNdffRdPHh+B5S7mconj8YjHD+/jja+9g/NVxe957qO4t9vh+PgKbz1+Gw9ef4Dv/+AdzLsZD956Fwzg1p0bmMqMqzOBygG7Q0WZCpZzRaETaplxPjEYBRN2wBVjOi2Y5oozTXLKFlVMpxmkd1idF8byaEZhRp0rmE/ArLOCOKJwxdP7j/GVz3wWF5e38fVXKy5uz9jtnoAmuf/RxvXixh6FJiznCfVcUA5nnM8Vp+MRAGMqNsYF52UBM1Cm2bPKOQFAtJ4t2OS5jt88o6yzTO4k6GxA3lP2LPnKzsUq471hmOMyemV3Ij3RtTV+2cFuuckbdseJiDCXOWjALQ18GVeHfx/guixgbRD7flEq43tqqC2X38uOWQ+yZyhmkZUsINK7JZH3FHEz5oF73Nvnz23/9mZwvz0+q0Arndy8uQw0jXuttT3peeMdw18+oXHQr6sfcD/U/8uOV+g0+KyWOcYy26j41HCm8nI7SzRst9vysuxVEgfMV2us8NbZizTL7U65HfjDsk+wPSVYg4mUaDS6GjyLvo0MgoApXVLuPBA28zp90QQzmlWyfUEmIyvWIfhesoZmTLHuGerwlSTPafuWjZmUg9NuM5jC9oyu3cea+9H0ySvXd1zpJCc104Nl1n49Yx42sn2eeaZo7qA/2dJmqwQZIsKknLswR7Bi8kwxIy7ks3ERfjSdKgeQc2rP7oitOtsaPktl0/HANCU/AOz7BqfmHlZ2556Rn2u/m8ClpQkRpSt5tmXGwviiJ8qi2smhaVYJ5HvrQQRaBK/iuOfaWKXfAk2hmfXNZ/DTDLnbVbZki9Gh6OGkFozostN0Em0pssWl1CyDSRazz2afK8AU2wmY2fczSh3VeWhN1KAlwLr8PcsLpzHN75ewWQxwUfnmSOCVCjATWO8X91l5Ow4VcBqsImFtym14wqsHs1eTyiEDYO4DPKVHsXFpdV8kFaTCRrdRDRycLgPej/AdnVmMAGjzhfh8nROkb/cH0/h3wNfL94azT1uRl7MAyNqmcHJNwXrYFn896PJ2tBxlQ6NYNd/7blPzuiMEXEsr8nY2+tQooEzH6GQ26KZcAVEu0Yah0zoVBGDigmlH2O0PeOml5/FgP+PVVwp4qTjsC27f3uOwL3juuR1u3SA8eXKJmzcK9vsJYEKZd7h69FQuJD5UYH/G2+8esb81YTeLIjzp0ekzA1dXT0Gninp5wo4KZppwBoGoihIrAE2EUo5gmsCaDecFoONZ+GOyE/wKQDuwZ/Ek01nKTk4/4zN2hXAoMx48eAVU7oBuHXB6esTx6oRChMPFHk+eMGoFltMM8IT5MOP27Ts4XBzAtWJZznJQCRj7eQeCHA8fY7CWDd+LxHG9iozLtqNFQFxnkGSGE188MwmTHS1nHTWKXfktHMj49ZntrOvqy1w3G9fqkGTQMk8S/DR0ctrGclxzntq6VYZ7vDrZybK/hVODtzqIlpjIS1EFx8AjZ4/DKUj4K5KhEtrrQBp8Vr3r+uToJSekG/dc5rrkwkYDriPMqRCVueHcd/pDfa1c1fbnVPdmga6/lH53583dYu2jF2lnYAL/1ERH3z64Z9j9m0lfq2PVzyb0+3jjYxtMifGzz6R7X+WbBSchElkWul6mCyWlD89OGMX9GlFXDvI9OSAedlMPayBJda2fYp9V2+aKlsmZDnmUAbmOF9lkJNuzlHRoBq/zPViDLvvc2Mk0O9TqvRY/S9Z5RTUFZak2EIFsaSlIl3kGFE1CArJydUm/Ce0Tr6aKqdEbcdopWC+pSvo5gpGgwsLN10TDJv4C2544S1JoUAvr6ZZtQFQe/kXqlH8kPZvC6GuzaIozyYtJqpN/JXwTsUzUazN8a/uVehrZqtz90A9ogQGZcFA6SpPdklTmhgfzOGdzQQ2uQq98REdT9ho76TyKsBM58Or9w6C/PDbt1uCY6vIZQqdv0vUNDqLrPKzOS56afrc60X5sEkIJwczvGtpvjMqA9wt8x4LF3mm47h3AGKMVp2bWbxWU5b+hFNoMYQRDuZwJlWUvXDh6w5qNbf49PBrrRAhEp4SKG7dWWUafeyO3zv6a/rcmN41sartHpVXUVmGW4fypdcD8VwbKzDidT3j85BKPnjzFOw8e4erqhBPOuHxyBTCj7AjnhfHgyRNcLSe8/fAJjlcVX371HXzjzQewfW1UGI8fH3E17TTjCgmsuIJQ8PjpE2CpOB0XzPOEyjZ7VrGg4ljtGJsK5kn3RelMTmW5bJht6S4DKKC6AKV4xo1I8sIFwMVuh8Nhj6vjEwA3cPn4hEePnuDRoyvM04Rbt2/gfH6K86lini9wOBxwdV/qeP7inhzAw0WP3iZdQkcoKHIvWjZiSEreHd6WL/qZZE5GLtcDfzd4Pycj8kwMGOtZNAsAeuOuz3wrSu/to5fWtVLvZTU/X//GAJW+BpBlQvMeXJBfUL4d5KwDvvw59tCsBCH5Ep1z0zgrUkj8NHLausH1AU1BxUr+u3VExgdd8NYY1cQzeaDWOiA7C51z1EHu11p3Z8Ik+q0CzB4X3qCj0WLtfPfoZbWV/PvmmZRL+o7y9609T6mv3Zi3dcJlrMmAextmmZIM2lDnjnCauU70iFZiRtLRYaMvYI5T0DfpDq+nxc2ctaafIHfum6RH4y6qC7nFU0TI4be3bY5ks9dUX6k9fTNftWbTTJnzVaJh8HrQw5NrjkvmqaDFmu/ZfljhaxTgjjdXZdwIw3VIrjJaop5a8CAIlhByZpLZl3xwZyf/Rouc4PCucVCgEMS2JV1O1MqDvRNuulXWLV+nvi2hkKstbpddt2waOPR0iJnvRBnOw5T0RLIHjW0hONGN7Rnt0G75W0BOZCX5TdTIuDdJJwLspKQ20LLRtJqCEBY8Z17Z6ELSwUh2SdvnhGNStdQQDfG+0aPjDeM3zvS1tntdab92ToHrPNc1OYBsUEn+iLWX9FeyacklX/Uner4tswO++/BtHHATxsqNbl4C6QLD7qBmZWQnAubydqmnTKN0bRGpkhPGNH82ZgDleyTxiiuLRkApZhsnsnuNwhGIQ29CwVsHqCmTjICZ6lLiRDagZXtTmkYEXcbke3WyAbOrQFDDLzC83Cokp7Yxluyzr37fn+Grh8bYfsfiKW3ZdwfI8o1KwHFi/P1f+Cw+/dmv4fLyEq+/8QaePDqDloL5MGHez5h2MiNSUUC14vLRCafzguPP/AqOl4Q6H2QG5lhRd1d49PgKF9MF5nnSo7EraCa88/gp6plAE2F/+waOxwqqC6YCHK/OeOOdK1navuxkzMok9zjuzsD+plygvZxBAHY0ofIJ08IAM6odtc4VhWaU6QLTrQPmu3scbt/E8y8/j89/5kt48+v3cXxKuHP7Fng54bWvvo3T8Yjv+74XQQTXfgAAIABJREFUcedDO/yDz7+CB+8+wsd/5GOYCmHa73D37h0sdcH5KKfBznMBClAXoXvVLKolKvLSJE4WL/OoMXPYPHLnz5xpP3018XTOeLo8IGYtmovbVX6EDcwQpf19ZA4yyUm3yApeGLy904scPynWGoJpmtqMKIrv0zF7VkyOUkBjf/uleJXZkw49uBMKE13dQ6PPbKkXcxwoVZP8tAF42gsDnTHQMWgPO0h6xnRHlkl1gK9dAkzdagoLKkyOM+65r/BdYwDi0BJAdEdWQHaHmx28Y7SSZWTdMtaNg7qaIBq987geM69r43vGP6phrIc0t9+StIm+YOMeOtuCDRkvmyHUllypIijY8VnUE22RLUvk2OdmNTU80zt3Vr6IPjCZQW1ptkp0OL1SP60+9DPR8n6BLE2LchH6hX+vPGMzCxy0QLFlwiyJOE4XnnPDUgLF9tQrfvlWBTLpYC0qMlSIZL+30onTlQaiKuVzyac/mkvA9p8F5awH2uTxCD1FvWwB3pcM3HndDOgBPfB+i9yH7m3jZKGVXftgdn2pS2PzCwiY+sbtqqWYbWHVKVPp9smRmXMpZ9crFLch5H2Tw8+CL53nWQcOgF2U2AfcRIRpFn6XS+zlN/OToOMXLkl7+Jolb5GWgbNjrW1MU7hYbq9Yh4CS7YMfBFp9GTUcTyBWZmwmAhK+DQ835pJcFwLKx2yHRikJayyXNbky3StklyecIzfFhnvbUiuaa8qU/WzfH4FgZ5xxi2gbrJHNELLa0TjgMMacoiwZ/4dtC54Iteo0TPRjb9IHvcXLl1evzxLobYBdgQeu5kFIO9cshx3w3YdvY8+iBl3uLJovGUrNmDOcQQDu3oTyIiKUSV/KBs54XJ1aYTiRoFJS/UDKjqsIk26uRt7bYTME8jefhoqEjyLV1G+l3GS3Mig9InNM8/KIMILZAYxTz0zBJAE1pWJaNDu16gyTRcUqtI6f2+12tkCcFQZVoJZJulAXFK7gaRE8VUnP2KPMFa++9gCf/IUv4XQ8g/gEmoB7+z3Ol5dYuOJ8JlkKugMmALPtAyLCoUyoUwGw4AwAPGGeAJQzpnmPw2EvOoiA3bwDHcKxWEAAT6gsdxiez3JX1X53kGU8Ve51unE4AFRwPp9QSfb7TURgnkDLCRWESpPMTJUFBQt4OePR1SXuH3coe0K52GG+2GPe73GxI3zgpQvce+EW3n5rj/MJwELg8xX2teDpoyvcf/tdLOcTbty6hXvPvYDCFWU3gZmwgJ3PmVnaJQLRBDpXEOmsKC2gqpJg4+WaONkwsqSB/cTpOdoTVUscvBPAiS/I+ZdJHMEM4WglY252sJMF5y3nO5FL1sDSApP+/rCq91Fmx7ANAtoZtrxUrHWM2Y1s4B+zFSbXVR18AKi67DlOFI0P+fRET355O5kuwFSkr+Yk9HetSZXW/0Vtdpu0cuWWnZgu0AoUEg3yAS9sQY7WEcpF6ugPKDGd0i8lJIqDglZ0RtAj1Z2DvK1Zw2bCADkIyoxuOpMdf0p0jmBpzadWn2zPiVmYIhlILGejWfCZxxhw7g6nXOu2K43MWbLVc7KEjCEpCvbT+4IurS3r+dXaCHxMT8bYZsi86PUWsXt2R2PDpRo8lqL7Z1mTeH5yp2JrmQ6TX5YkmtuxbowqRbBoqweziY9xDFNl42e6yfd2VnP4hVYyMwYfY856BtpISoA1e+88WMiBRUs/ZrvsvpOD5KyCUr1M6rzb96Sf9T1i6JJKw6Xx2qVeTU750w25ps6X5kW2M9hVTB6gejBG5mTE/YVkWjfLjOJk+sWWl+r9ieR38KmeK3G3Yq2h+Vnx9aR41cDUShBJH3VZriz0qR3/SJ9cO7psJTq4TaJEzfWd14YvAD3tPA5O6pOKq5kppwkHPTQwc7E1/s92wAK1FJja3kWhrT4Du90JPRNzju6vuT7r0VM+BINQexWZ9JOPTF8kupACuvALWx/ZqvL7LhuUsg6PsfYWXXYINY3RkurJOkvajAB1tdWFuT2NmVq9NuD9BfReN5T+O//6v8DrGTvXrMgCLjonNGR2Cvtn+bNnDkscUBOOa2p7ow7ADhoAjAXzhb5StoTzoQaVzBFPTGuKICuVaDv6srAeRGJ7PPRC98BPcKrMGwoRjTARESoWFBCKHgjAtfoGZpr0uobKmqDSgH0K29g7crwQ5gngSRTiXGVh5hlnUCU562ECllpw+xZjP9/CNN/Dk6dP8RM/+dP4m//zL6Gczph3DJoYS2UUMJ4cKyaSZZkVsrTULiUnmyWWyxlRQNjvdO9iXVCmSTenm8M3NUbgvCx49OQJjpdHABNKITQTLGTZ4wmkLZdSMM8HMJ1QcZKntEOtjKurKzx5eAnUgoubN3Dj9g6PL+9jng+4d+cmbt6Y8dIH7+H+2++AwPjwR17ErbsXePLmER/80HP443/yD6Eez3j0+iU+9KGXUXnC+SxW53AhS13reQeazuCqp9cVwvksBkj2KhaZweW4TiB4rZ216fnDsqdEhKUu4TgvNQVAyuupLvmsB/G44Y2DO6xucQ5n55/+YnYr314jkAwt5D5OAGB1bC2IjCGj1A9bCiA0rLU2ZjHuX4y2Jz+RtjU+7mSqJ1UTnt6PUnwm3rP4qf/mrIrTpullIPb4GO7Vgibd3C+NAFRQSuDXOAkUBreXzUyb3Ne+n73Tsbmc8Jrf8nh6IMZwGcqoeJlVZEDrZ+BGZreC/HXAv42njYXNRq+DhDaZILNU6sToWMvl2c9wOjhJmDqE9t30aDWfu2vb+5b+b/pAMfuQqBN98YYAwiw8po6d2IYKrktTd5882ewSwvHaGodtMmzXmRMSJkONjOhzMX9phQBMN5CzlgeDlXV1A/l37x+KhBbcBgASFNVrHE2hVXZS+wQTa2C/FYynL6vnW3JnwSu4lcPKjIXZg65SWr6rteNh9H0hGe/K63FWvoAHKP3pp9wk5CyAshjIHH42E6wBYWXLJYUeaJPtEL3GjjTAEiyyykZ29Cf1Q2TME/0bnpdVVEtKZnKNxLrhUlFjpQeC7DbuZvNMvqryDKckmNNX+dBolGU46BzBVyOf1odkjWutcoBL8mWZWWaPZVBc6l2nGC9Re9Cjt08EbOAXepbTOGZeanGo1pdKzRisxgIxRtZGTv75cHs7QdccoGeeXmo7Zhmk3QgO2+dr/VPB+Imf/sJmXQO+c8Cnd37DUfm3ec9ifGiNcwRQ2Wj2weBWFrv9Pery35vfAvIhOID5QJoean6Jsq1i12BLH/kMkfWAmioiwNQfGdCMYcbba3Y8sv6NljtHzy/BXTUq57VQpklLh1bu270rxfcFSNC58CLXS9AMmqSvYMIM4Hw6otZL7LDDfGDce/ECuxvAaVlwrAsmKnjh3m1MhfH41fuelS2KFIH0QBnNWLriXsBzwTxLIC3LJwi8hMKsqnw9S8W6amMiTLsd9rsZQJUZRnV2q2bTz+cTqBRcns4AMeZZs7ZcAJzBOGN3MYPrhEpHPL2sunSD8ejhJR4/Ah4/vsJyOoMIeHT5Bso0gc8VX/3GQ7xzvMRyrPjhH/hBPP87fhDPvXgbOF/h6vEV+MyohcClgqmAJh0/Kih7CTzMOSSaXDb6bNq12bX0uHe+SWdV7F1fjpKcKV0ItulYWZk+M+jBPlp53ZLdPosuMzDswVMG1nEzgyriRCs5fua9c9LYM4MQo5PXu2XUzMhboOiGOsmYKwETz3bPFQFgnVHdoolipHopdETGNcrKGBgavV78VmFrvNq2yZ01w69vh+0/02XkPU/vbAWQob+kqnUwuYXns/rQ0kL6QEWWwPfv5D712fk8q0AxxRX1kmp0z4pDaRWHc2QpiZmtNu3TB3xNCSLIdJ15w0qjVRgqdibPYPd0NDtbya6pCFvmMSxs3Laduk0g0q0KkrQxpzbT3O2g45dm83Kw0gVRNlPqAYPWVXU/uo7Upp6xAKe1y+x6pOGTxt1vWFm/G0/YrB4jDqhKekcYP+kChmyLsWRtgZ8vY9HaCufMK6GrOT/Ljnhn4zn7QvrDtTrZA035Q7L+1/tYLEggwA7926JLVB50a/wbDbB6xz/rRtcrRU/xRKNe/QUL1oQG9jpHfQhez0DqFznuixOshU7NWDuu/13Gu7bUp2IS3iCOiQyi2EoUtLMglH1sexXX6LWNZ7aU3dpZ4eycTOGG2qMcCyLJivFbrpOovfICMXaeULSnhBXtDafsaa8TUFkJhQe8aQNXtQ94v8C3dcBNKKOt4I90WU/zVior33N9+ZqN1hFuhdEcy2wsi156a3xpeyOyYHjQZPWF5ndj1oSWVkfPweRINA5e7D9ECFb/MvNKcURdQQvBc4JpKhdAO7rf60gOflLoYZDEkIFkCd2ySOAkdwLKUqXCmjUrhN10oUt8D1gWBj96isNhwp0LwmG/YCHgeAlgV/DSi3dwcSj49S+/gzJPmPfmDO/kgBk9xtIMUYxXwTzvUIpl5XLgsujfKjOqbvgJ0zRjnibsD7MEoYtm46sEscyEhQHUioWPmMoOB5oxTbJXs/KEaQLmQwEw4ep4heUss7f1vIBpARXg/v2zHI4DxoPHR/BSQDOB+Cm++o37WM4L3vj4Eeda8NwHL/Cxj7yM5+/cw0sv3MHx8gonPgPLhHo+g7CAJgZNeuHyMoMwyYaS+s1PRG1salgV9+L9SHJqy7dBnwZjKEDpsvqJh/vZQjOG1PG5GMi0PJZ0hhAkDpZ6bEWTJ15vymSwewvZIUrOoclPdmLM6dGOevIo0YtSH/IR/VM3w7iC5IT1AYnhxQR12tMMAvcBV7t/qvVQCohiHyuB/BL7uGsrOwSU9nayL52zMtbP1rgrca4xu+asugOaHOwgBaW/hPaiu9aJ83ZK4x45D5gnQ7XoLHHiTV7VEno985z1v7T0Mb7s71/MNobBeroh4OtK08xD0BqQPeKsRdRJbJaycsMTjndyopizrSLvoDQf4220L7Ifwp5I2SkucAd0f4/NWOYLy5faOHn9NVUWg1KhNPMUNG4CE8eAm/OYGqc22eG8f5YAoMiSlrh2BM5nEftIXXINjbQTs43SuqUzZR+j0JVofXVKjEVXL4K3Sb/kWQ8/g4OLjK89sKsWfGipxdsDetLWw5EugFxhkLCzGwx8Z3P6vV+aZ/3MtF0nyLS/JWQHsP2k+p44LHJhewp0DW2uIZsVsXLA+SDJY+tM5J6luKwowbjF2fkL0UcGoxJj0RbYkt5cvF6ov0TWV4KfhG04FTulVQMc87mYwoeYJg3utE8mrh4SZt1pPAqI9kpjHkG7VFEoth9YeEWQJLbJegS88mK1gNy650mDaDcuuZd6mhOYKZbu5kSBr1aAruYh0b2VOelrthpjPyTDV2O41BGB7PoLZYLQ4YkdjJxJfzgbJGbxsWANvZOtyT4JYPZaC9R2n/2A9xd8WzOL4Uyuf1FxRL/Ztc2ex7NvNbvcMJsrWRMi0wrkv9nMVV46kKzMdntZS9p9hi1C8c++c2S8KM0MNso3pPAah7V1Pmw2rldamSaclBGpBmpqpkQbYjDJlRKu+JixK4QyF1ABKo4ACFwLCipKhdyVxDWW72HChBkTEaZJ745KVjqrBOtrrLZKCYPc10QP2SKTjgp3coeitsUWUlfsGSC9A2qiIv+KLHMlIpRFlztptadpxlJPwFkOPdkfCNO+oKLgzAVAxYQCqhPODHBd5HshvPXWO/jl//ezONyecf/+ET/8g4yXXr6H8xVjngtomlBmlmCiinKWvSdTjEnHAq3fljK3abxbdrIgra8kVxTa3gyYL029Ru7C8LOPXzjJyZmPmlN90apn0X3Y3BOLQKTh1mv6mtvx+tukkhlNyvxuKKM9qbgPtDK1yGWQuvFI/Uw4unGlvi/buDNTgwMZPTtHxgxyoeL3eoHWhjrwTsaaN56ZJ0jb9DXndev5dr/c1Yg2TR9v6Ne48gjRX0I702t0UUejQ2K7v1p+kx66xyjculYXtn0P3evP0ht5tsqDBB8LBA02yJT5Jr3e/CaPk1yCwukixBU6uZ+mH72/BDTLjBHjrf8a3t2iG8sMpaPZ6582E5ICBPj4EVGzX9JkzHnbZk1K6Bfhz7UuiK6q7nmWjkj2MdfQ2vPktDa4pZeS3o33Wh+AmGCrjoJPopHwAaypWPK+UuUdH2RYz8JDbIodfpfG0tWttxlBWJtMQTq8ueVAOzG+lXv2QNL1peJMkFnZ2NOqSKSgTvYVU4RYVrfypCfK1H54MpONvt0qmoRW5mX/XWdQfR8mt3zTLvPUd4Brl1LmMkHToA5pR1s7Qt1YGw5NKVhg3LTc3LGRcM6IsBLAD2Uy/eZH2DbljaWNN/wwO8NM66E85gmV6Ff0b+t50zTRaiVPT+FWNrdlYMD7A76NmcUIFGWM2ylkaniAVs+yc7dW/NsGwRWhsnh/gqLMJiajqsqrlBIzDnnNf8LvuqC3d7CamQ9XtqXpXFa27fIb0o3TlGZLzahz827o8vU+CKKWNn7Sn5aVIDD3QZyOOgE0WWapYqIJu/kCwAkPnjzF08snePzoAeoCPHp0id1+AfFNzPsJX3rtDTxaJlRc4eLmARe7GQ8fPsHTq4rdLEsuZc8BA3WR/k+TaCjdu1YrY5qnIBVJZlHGVZclMXRGcZGMenNIA2TfH0kgJ3fwitEsZEu4KgoKpjqLal4KuBRQYTCdMBU9jGZZMFc9vW7agRiY9wX7Q8F5AaZaACqYiVEw4/HxCpenM8qpYJ4K3njnXXzlG6+D+AJvfL3iqx9/Ew/vv47jwnj+9gUuDgfcuHmB/XzAc/eew83DTSznihOfQYUxQfoANf75oA4zPMaX69l2NaEc+3n7g1ZCqYc8tAGFnYIZNjOWZ3d7lBL988yQlMuy3TokSLLiL/vvJkAl/VZ8KXcOgWPnIHSsA6eILVRn2ExF69MmR2pNR3sOwO9PbH43CwsAfohIZLKFjj3GSM8y7dZOf66vf14mAqrIAVGJy7tTX2ws68ZyTAe7H5AEBzZnI0F/2uqWIxa/wfsUuJSG5v1sX9BAKiB/vy1HtHHvpM+AaHlLWIQB8r+tJlZnLF8zaKeFgluadTbLZaPqycFlUl0a/q451/1yZ+t/XhZqKOUEn+BgLn1rs0Sfxp60TAt9uA4xGtmwmd5usFIfe2ACil66bXLn0mBLdhGzEw3pkmw1f01+jHZEGufavlSbFUsolqL6vHVePXzwewADh55/hYdstqjVXx4wcsIdiD2OiWd89spl1FYpyGE21Iyz9jUty808Xr1tHR/fxxzjxIlZrL6a96MSgDSD7XKkZQrloc5MbR9sxin4UHiddMutWlLiOJiewsPzaqqOD7McSYAYc5/llWlXTOSNoAVbOm0dsXaSP4Tw1fysh4LmsCHi1Bcrq6cXcwqGBD9Nhk5xPkLem7sNZFNoyVeUluTQqZaWxlPWXwtakyXxeovSxpqu3ZjlJGivU/MMNBHSeQ452ckaV4YMOa8xr3lF0XYeUZx9pZ4deJXw6VdGWdd7fVhrVVNkCaIs4wPez/DeZxY9wKJGSbRFrjEe6ff131gaoP81DsikXypF/Y2jttFOj9MWHuFvrPtgtoY6bRBsbrNAsQwrO7dNO2gzUFJgHRx7kIDo5/V7ffQ5t0qxIYn5F3rozpllo/h+v8NXv/4N/NIvfx5vv3MfF4cZFcBbb93H6XzE5ZMZYODdR49x+QQ4nBfsDgtmWvD46SXo8oTdbhZHlhftv2b0SYlkxqmYYZN9iUQFXFkNCycfMD6L0iook8zIzWXGfreLrJ0pexAKn0FHCcZKOaFyAejgDkatJ8gRbgW8LMCJMbEGpCT2fTkTaAEKKoAzQBVMhB0qrvD/sfcuvbYlyXnYF5lr73Pura5qvpqEaZCWRVmAZMAWLMAELNgj/QBPPfHYY/8CT/xbNLFHNmwOBBkgZBmwDEumSNoS1ZTMl5pNdndV36p7z9l7ZYYH8c61zu1WlWFeA3eh6p69114rH5GREV9ERkZ2vL4+4tIBGgPcN3xy/Qy//J1vgZ7e4H/8b/8u+sMnsqLZH9AeHvDptz/D3/7bfxP/7l//ZdDOGE8yBhbyYiteBmKIMmyownfhphDQ6SfSObNeme/XcLXMf6crNM6PMjYEBYrrnDDBHwdGAUSRxKeZcvAOwWdRms/wtsm4BY3COAsnjpkEQZdSvL4n/JhozEFTEzV5LocBF7JhznlMrpNoZKDw6ByrEoENSKV3E8FfUJ2hyF0WZIMJcT8cWsuqQfrm4PAFPb22i6ODp21dnW0FkCPzVZKaaSzWul/WJQpu8jMnOiXarPfjCf1PzajcrjSRsvwN3jyuHgRSP/LdSm85zYRd5NtWybIqrt9XY9koPVOCkMMYldWHtEorPx50Xw1ZzbJEzbTl91gdZxHmPHwOvbzfSHnfHV4pq6sZ2KQhdEt4vNGv6j3FBBpOv64aZlpknokfK7OVEbW2mAHbwqA87Znzi8q81IhKY6URWESg9avQrnCoPh91tFaT22S6uIMlLecdsAEoJWKIeSnXDNxgPHLSX0NZk8SqL3tBF2PB5oXJ52ZHR6nsZrBnds2rXGJ0WbnRdm+pzpvCi8gOTiD2Harzqlmfc7G1h4czstmyv678kstaZRtQ2K3Uac6II21DBtTL5R2SzkI4XKRuc1Rw4jUcPousPmLIY31n8y3Jy2Q8nr2b3yNay4LP92ly/BQpf7w+xOtrG4u9t8PG3lXJZ4+8AbACKPQfW6UUmSurbrkc2/skIkL2XXQX0g20MJoB4jlntNEmjK7ESJr1BCwCPWoHUxuYEzit76VKtX3Z8Ew/N5nYriqI0+/HFdYVeGXvUqb1akjahuom2i6egawmbkRg7mhbQ7sA1HfcWsP/8lt/jN/+7T/CK2yg2cDbs4Dj2dAZeNgYr153XB8/wTNPvLvf0Z4GeA5cHl6pJ7XLXr8J2Q95n3ruJalhJOedPT8/493brzCH7v/qrY4xEXrvaLc7mBn7LkcujPsz3s4d9/1ex1oB/ADJKiIRngej0RX98ojrY8f1MvH2KylbjFvCmx8/Y9+fMccdrW9olwu2S8fsAxtkNXqCMbnhy+eB+9bwH/6tv4xf/eWfxR/839/DP/iH38Unj1f8zf/gV/Bv/crPYZt/C7/6S9/G//5//FM8vP42/vTNM756yyD6Fj792Ve4vXkLfjPQW8ftYmNoq3tZqC4gQLOqBjL3R17wNKa9bvOo1Ftr/r/dM3AVfKRtaOHuFxbX94ZlgMu/RdtBEbpnwNcMrdzuDLgKYNXfmGWvrW5xkncJniXV9H5PYMHeDRCiezRIeM0MLau7M0s/1UNs5w5Kdsaga+yZU3CiQiE8+blP0X5agKSNVX42ZAAOlz0r+4zDG16BvNHkaAjkMvI4B+2P9a3OKTMiDryaDJAz+bSC51zumXMsv3tow0KcF1cClPBkDgqfMAlAO9/aXlGV63mozLgjOz6iQyIZKmB6adysTuE3bQOnd5SHSUOTW5Nw/WwQtmUbR/5+AHcEX1po1Jx+58bnGcCLctnKZfbV7OyMEENU+Y3ivUOWZEDBrkWODACtZLuNjJ7V2RFjG44Hh8+k+74zbF32WtpRBxPVwF7ryFECK++V/+dEa3IkTqaDyXFm1pwAlbb+rMpwX822+YTmZ1rCDCFo1wzkU23fGCOrAZfrmW7eT85YQ7eGeXxhGIomr2AGibbExHqE/nMxhPq2ub5w4w0WFaR0HlPox6xnDjbMNp33M833Edmz89gAig2VB2Yas/x36pEeEqqrY5P2NHp+CtMXnHWV8pSz9TpH1KxLc87HNh5x+eryGNDM9c0/++PEJYtpM1llyo2TAx4JLyzyu8hQVPlUVrOZEUdsmdM9vXB6ERrZUgC8DaJnm4rLMGSzXF7nFqC6eeaZqX08F0kfrw/g+vphqO/xBBiwOBv5MwAT9ygkUy0tPr3A0Gde1gJgyJLgHNb2/H0CSSjMewDUmbGoppxkwDwAxgQKTTlM8rj6F7vEEoLmgDhTglJ/Fw8fUaJ/ap+/p+BnzokrNjyg46sf/hD87oZXRPjZ1yLkJ28B9ljeQW/adgZBzg3sfQP31FcSrxGxGmVau52BpuIcrW3griDKFJD2e6oQ3++yUtmIYtc6Ee734UqVCcA+AucZvfqGOSeent9hTMJzG+DZQOiyT5EmduwBJAYDgzGbHAlCW0dvhIaBeR+4tIZxZ/zL734f251xe97x/HzD1u/46ouv8PQzD/jsU8bb5xv2eccnj3e8ur0FJvDJ4wVbf41nuuPe7qAuIagrj5ThDxiAALeZn3Wsc/gPjMfi/+kKs9YRyS6iDQ6g4NUp6KLIhJjmgNBcwrLC57ioV9dapCIhFHp1wiRO1bKzsiYd71yGgdoKfMNaNAPRQMFKZgNGMveP8zDHCJjneSrgN7pn49TKMSxhzrJD25Y+yS/ZMIuGHgBRw+lZifE5SqnPnN/Pt16S6V43COvZgHksVtm+hiC9zzhZ/64hhSgybtEhXCV6sFygoIN+sH99358RIQHTckA0i/ONNemYWJKnuuTQvzxDk6rzJq7z2nii6LC8ohBtMv5zoKmr+B6pgTROSg4Djis1apvtw6qj18YfgaA5iNayMmDNfY2xPjMUT3j3J5Dct37EIX/p1+oUi/JTvYVO0femXiGJxo25LuMws/0kMkwhdBnTBV0Y8AZV+Ytk6AUPx95NkcvwhECnwtey7BwppP/GdocIV05zK93Jq58EeGZVbazMQbNxsnFnPNHVYJszwsDZzt/NxtBLV8hQYzvLvC6VZgeLKBQbS2+iy3qqRVo5DBBFbJg/6TpLs+2o3gpdxi/wZJL7lTmU9laHhSNbU6i8WwxmBzpIcq/KNxsHchmwXgwfcZV/BGgCN7wwB7OD0fg28QNXzhG6Lyv/i7wkopqJ9QX98PH6MK5vlA31zPMxlIZeAAAgAElEQVRbGGh5flU8RQEZ+HSmqwrNJ3lmSlrqceEaX6PO+L+cW57bBwDLvpt4ILVhecDBuIPiRYFq36CJWNBS7D0QiiGqQha0OZzL6lseLvWZEqtGQgajYux1EK7Y8PTmC/D9jguAbz02DB7gvaNtXfat8MRgxp72pwlpJYHMoBn7RQlAG6BJIE9LBjWQVKlAs7E2BnOD7OG0oyUIvMsJzu5x7pKu2s4+G2O6kmUkj6wpUwJa2zB5Yr8/YwyAaODSriBq4uWkO4b4wvwohLGLATd4SPKF1iRp6WBZaRw7/tWf/AifPlxxfSTcn++4X+748sdv8eWbB9B2w+35Ge9udzze3+J+/wJzb+gNYHRMbJitYXY1ThZjTVnBh5VcGxvIqqwX4CFzjb+NrETWVZjwLlaHClkj8hxVb3Bup+kzqymATeiz4qSw+ePvL7CJRIEaMI7dWKkPC6/7itpiqNjvMylYd+LgBWcRsoFxVLA29y1br8Mo97KnuQeGJVdZBaH1r1SM5bH8JT/q45THMd55yZDIPFbC4jhocmzEsd4MqnPjiiNtbetSzmo0nq0ynu2/zMa3fXb9c8K33lJnSypAB8Z9pU/Z+A9HRIAv8uddJheAtwAsqz/d8Gy+gDszQjdl0B4dD/BYuVLEXnYmZj20OhEz7xznQMgjEzLJOHDdzIVcuUVEEV7m9DtZJXQdzEHPGNf3WYG5nDSW+ZD31b/gbavlFJ6xftt5rmwA+4R2rKuV6V171leplEA2FjyrC4b8fA2k5CGBGRgqUhYsY/0o8yTNuXHijHkfNbValWkMOxx+NWoJMU4e3kkNnvQausLnq5m5vTG+ef46gJj2ZwY/vNRUev/fuawqa/fkt8SvZpwpkTG5OlNDj4XsXn/LWBZKrZWu1f5Zwz/ZHwNRSjaEcj8iZTiNh40FFE7FvIrxk76xtyU9Y7yF0J2AOZUR8jLxwmnH0hz09k9tq5LX5Ew4QZK8XOjjdPmJTPvx+ou6vlk21NAUaETY6Bi6yd02qlWFUIwlCi8ENTvSPQmEJMKodU/Tbu/C5y4DPYCDhSxkyW7hZJwAKUMnXYuVi6K0G3lYTzaQo6/6WDMvYqQDjix2Nj0tpCd1/wToEkUa8iaHIBZjwZSLGEcpbXU6py71TtsHEB5k5U/DLe6N8fDpp+gPV3BvuA1gzgsIjLGbOSVFNDCoA52BzoTRGDt2jJt4WyeZOGliXN7uMX77IgfUG3e5yJmLr14/iFE6OM4DVi++GRX7bcdg4OHxUejDA+AJ3UKRwAmBaMOkqYcAExgX3PcB4mcdLICo47E1zCYJvZ/ud/Dd0rY+KwU1eQmLoh/tju99+QX6VxvatmHs7/BP/+DP8Mc/+hH+5I//CJ9/MfDX/51fxI9+dMMPP/8cD68e8IP7a7zZ3+Df/M7P4dNXF9zud1wvV4AZY+4II0mpRYQI25oesrUafPloiBxKmpmygvDgCkmM46jN76NRAbQrXzIA1r17BGiYK0riFdZRa12VcEnZnr2kKPPcstTahG7GtNq/Ex9PGKHaPk9GAUbrFh5lSi761VukQR8s/1NriNgAC3GVtjAk3HRrF0weuiKtMqI1dGgYLBLASn/LWJQ/VH8rF2PTsbfvDGiSJgUQFHx/DD2tz0q/616oUlsyJIEI57KkWRbOKMMSY03p+3F8zmSb8fYxFD/a8RIPtgKaDaNkHoiD0Cdg4Zh6dEckCpHfIxwrxsG3MLQtDCTogeDv6Vumi/UD0EPLfaUIJcnatCMiki4JGU+wvU4D9cqyruc5paDbtRfVdyysFuBYhWQgtnMwRKBONw56pzJ7KI1dCenTmS/hv3AnnOtc7kpv1ggeeYZ4ejsyMA9DgMv9fAXc4PhLJAlPmrUXEuaZwEJvDTyngGQLtU+6Pev4rFsJy7EWFHqWVlrz1OigVF7zF9A1/JEJ4LbUreO4ynfHT0vSu9bo0GZPopcaJmNR9z8yAZ02vaflg8E8PGEYdUD9ZGia9Wbm0NlsQCMiInLfWwfmjJXkgaF4b4NJEmbWY0ai3/mvbWWw9kt/gNZ6maf2xbAds+E91r6h6C3K48Op/ZP1N/L26eCIQZZ0WRAZCy9kvtVye4p2mfaOuSBVL9nYdtPVIjtMJolOUv3koElrcRwqco5I+oJGaNzAqcF5VdO2R+W9u6Wf2rqeoy5UbNheUtO2oq/iuXC4Rh80e0Ng8I/XB3t9bWPRmLR6H9MMUcaX81xsZS2exwIuPJuVzssCCJayzTu0slYBQKHZlmdIAXGAZEoFEBAT15j7Pe6OME6sj2oomiSg1BTrX56btJZVv+fVgPzdPE5YlEb0iP2zLfhlYNm2hsmyP+AXf+Hn8e2f+RZef+sNto0ANPCUEFATwfucGNPydJEfQGxgpzXC1tVQnVM3r0+4mynvHU19tz5drpuerTXUSxjCVPaDyBEd+32X/bKtidifu5fHAMac2MfE1qbuU5BfiAjbtgnXKDE8jMfGWx0bIsxikzxAmPyEtnUADV/86C2YGx4fPwGD8d3v/hGIgNtt4r43/N7vfR9vn4Dn+w3jh0/gf/C72J9v+I//o38Pn/3aL+L+dBNjETqPTN8kg1FIZUArnDC+ckDVQJbzQBdHRp5Pi/Jd67P35ZVk9DjPxf4TJCBnK44NiZdNadi7RnPjTZtk/p3Kb6Ew02WGLYQfRFdHXzKY5WXGxmyo/bYWxBSi8i+lNjsp7V5Ob17eOqNvpbufjZmuw7gwikKPlqzfOORqGuf8ICUeWylTakjjD+g+0DA7vKxCT2YHlNlhkWW2JTPpehZfdbZVXs/v5TENQi03HWDV/op/p7nBE7ImwF3weCQtyvrM/jKrYZXe9bqKaljkd7qTWH7pDMr8jnNmUxnUgGl7hJMRuMiNbGSb7l0B2MofMR+1RZ6xcRkHQ9epDU6vA5uGfvUxheqBdHw35U6qrD/bd2fhnyALX2Tw4DoOrie1zUleCJETVlmaC85JVk5WXenwxuHKc8RWGl96L6/orw6kWq+0zeT8YdUZ8WrurxSp2bY5rTwbJkN1anTIPkxjqZiHy/7TM3zSY/t67j/FkMFWg/lFJ6TKOp/bKv9OdRWKnHA+UQZqpM51sOZEiLab49IqItLcCO40YV/5XVRCzGM/zismYP1k8jPJTL3r9xZ6amv80xnatPnDMF4vKrHgAn8+0dMWQ2w5uzhBTo4WWWX02cqod8UEHdcn1n4cy00UIgbmWs/H60O6vn6CG01Icia8gDQ5eRYAsCavsWf9HYgn9mB86lXOS2sxsai1kjM6C2IDztSyV1pDREiU8UANL8irFHqjtjO30dJYk4RXyqKZgChPYkCacXgS0sJjKFKsE6iGDlo7Q7woiFCwHSBB6hSneoQuEJFtrcb0LG8dwIa2AYQ7iO9o7VGMRDtolsT7fXueGGPg+nCBwbPJjDufCGRBKohU+lWwI7rviVYMUDJPbFuHSSALXWBmTB4YY8hKznWTdNws/DRZDkK+3Qb2ccPT7UlAoHZjo45Xrx6l/wq8TPnOOSQr6+A4IJe0XyxLoo/tAZOBjTt4MOZ8VjqJUmYGLg8btleEd7cBbsBnn73Gu2fGmx8T+P6z6HiNDeQe90Oooo8juRQmimNfpNFh7BInb7wBNC9KeZmqYjgcnqyDkY3DPD6OZQ7JrJp7deuIpk+CPLxPXiao9Mk9qZwSHqTnTZH5sTjeP93zklaHwntpq23yy7TyKJd8bHu+ms0eAmxFRLyncfxLVtakAKIe92LvHxVwAKlMV22PskVeQVrnmYEwZQfwCRBYV6PbAgDts9HYSiQijDkwhszHpqvrIt/YO9599SIAmImAAlLtpvd/GYFVPphBoq06M4Iz0Uofk/EKl3o635rNOQLQ9X8zfIJLTccAeoQAyemyxVMOKokz7J7RxoB3woExH0gTe7ncqyvu5tlvJCsxjSP0PuoxetjKnchRC63rdqwOUlIV4ws1pBt1TCtb6STJoXk5Y43qMVCLMUb2fVmlyzwAkEYPiLx0wwRNI1NitcjeMfqxyhIb/+YrmirmGslec302123zxOgvHwaApONtxX2GTLZ2RvuVkyaOjhyKugiRzIu1ke8FwWz0Vpma20rBxydkj+pZVuTCUPWGB09reSYHvb3UQBxOVyJb/RPZyiNkSR0f6zjDVp+qwyUMJ5nPDdzDiO7oMZ91zCw0FDZUpqDssYO81Dk6dzCrnCfBDz60pj9A4MZgPVIFJJEWUJ3MEChHgO4uEAOr+hzYGgLPUUNVo+TnXXTPHE1BLhwlrJlTFxmyl1M+O8uCl3Ip3Qu5AYTzzhwN/jxTysbK3h5ht8rP4aypulXuZV2U5lmS7+RjV0r1cg4yj6W3P+msy4/XX9z1jcJQnemMASl+YeXXhmQcUn356MW1FQpT5kt9GXQaQNYmOCjWJwJQemWHIk3YHUCNXqbyGTVstnrOl0m29M33Kdqjbt/lvi9TdZm8TcNjTaCa4eg3/bWk0JuBvkzn5spV2kqu9OS4CTjwtBCT1tsy543uBElyY2FMpmQImA2EmjEuE935hkMoNSIMFR62Gi2Goqsb34/oill7kDBWoouEj9pqAAOuAEORClC533cMtr0XQu/WLbZ1elsmEZ7vu/SZJPPdmADtHUQMahIS22YD0y7eWlbnBxrsOM7Yj0dwLeWCVvtGBPJEJpyedzSU+CeT93zu5GfphJcFOFk7TBEpbzpAqcaKDOFLwj2Mofa+NiEbBaldJ88l8wamDOO3AEdn7zcoTRfDwvoT3v5Ep9qA1MbK0/VsxqUfSYHWOrXlFAZhjLLyufG+v1vbv66mCG8fxzhfpe8J5JiBbe+aDJia5djBPsIQDoecOS3qyocF/rUFcDmP53v6wykY9OFOdOIIbX6pf5QryiDJ5Kd+PePh4uBcuNN1DAGGvqLe4woQmT7M7X1Bv1UjOfpkx0tEL9awxyhjzaBK+qyyVJQtoRX1vLoyF2wfH4oeORhzx54g8+L6S6ZknmsWHpff8yzHiccrjtDIpYVqbvCZQ4V89OOZ7IPTf7mxnjVYf1k7kc/1A1Y6BHIgN1WqlCvtsBvGGqYbKWhvpwXZ91ODcZkrJl9yw8+NreP2Bf/EeZwrLaOM4AdZuEudcbxxIo/qhDKtU9GQ0qbYRMnQrf2w4mwfYirkvUZIRB8AizygND56wx1AFHPEHJPsZycfp/hKN3NQHVbB/TxEFOPO5i9gebkIS2iDdeCU3AfG81tUeOqMviE/TdtWnfnTmHhZN53JDQKqY/zj9UFd3+CcRcCEjAnmDHiMWcXQyYqTKyAjCmHCFajZ53WFD+rxifpsFcuelzN92MpeFLUZdI26K/GmQtD2crkAmBL7n72q0uZmCCd2UOeMn94/+cc9WSZlDkK3Goy+WkSxcmBABI3QWsTuuzGYMqwaOnAvDwIgyasNmA2TgcvDFZfLht4ati6aaQwxFi6XCwYDT7e71LU1XDd59na/Y+ehIasNrRMuW8c+JmgnTF0FbA4o+QDS5hwYo2GMjjEm9vtAm2p0mthWA9nO9mPofho3NjmMRF/haIi9N6xOb/XuzRmKmWQfxOQB3mUVfNs6+kayaWsOoBG2Tmij46svnwBMXB8brtcGzA6eXdsy8PQM0AS4T2zbxNTkOohTI2Sfhh39cHbwko1/0/abUDZvutLE+EO+cuEZu1fKS3/zfWPjdcWqPHOi8P28K3kKnKII3J/tIOBcocT0JDlDlZZ5kP61y+aqt1Jlwkxt8pWAmfuBU/p0okjgs9LIVyh8YmtZMqCkXnyXD3y2d4lDtiDjAgra8nF/B4jQTpIQ5WwOq+F/dhUgdeJkOOcNiS5obfMwSQIw0+ql9YmZ5dzSBZzaXh+TXyvdC50WvWBjm2Wph7E57wMxHgHiVi+5/DZUhujvXnkDYXqmRkKVoeKwilU+s1Wyg1NWNPSVVG/TOWyGdIw7VzqcASTdr07aX9ezMJBpjsEIUzQam+ffDD0gn0+86tmYoAwJt0Wb/putnFr9DJboDqzyQkBvIzsqJ7Yd+DM2FwycYu237g/LfKIyXOgckRQesYO8ooUA6ST8QqmdMnU4+mtt8jHRFjWTHWmAE+vZQAdvkkV8+thGX0weJCNMx3WN8HCWtKnuR7dQ9FMTco4RvG40yatWpLrQbTdk/oh7biA0zfLrssXmYwOTHK8RQxPj46uV2uam4CrZlvWaQQt7Z9r2Fpf9QlziWI3D5LLtwGS88XhPe7u116GZGHGWc5KlUmck09JZo29zTTptn71/7IvockwI6RyX47Z8tVDphmRQBx1s7JPkV9YjQlnBJtL9zUJgwa5gMI+kWznamPABsa3iyXgKS6fjW2Bzzdp7lKN13OFRTVlDe4RuHtx0Zf9Klh05oqIfGObj9aFc3+jojMMqhjFefMVynKHfdyCA8AxymuC5njPGg9WdvDxnlUiWzgAgIeupJsrJZVISRhpKuvbTERCgESIhMKtxLP9UD94JOCiEK38Q1l48HnJYFedkMWz7sR0VGNpfS9gjfZ1Tw/nT760BrXdPyABX9E2PIdFma9rwOe3sIxWWppUohLFJQgKpbhLwcb9LyNs+hiYLkTMZtVoALFlQXfBJOVU4kQvKEGg66irYRB4mMEVwOmRQ1DQsyRTx9dowdsI7BWNbJ1wfOjBIDRLCfQC3oVlVWzgUmFiMReOjhZ8PYJ7kPCbp35H3A1zQoYy8MvHTGA+HR/ikPe8pq/6mBoSeBXVcIzl5Vypx5fHeK7WFdO772JssMOOb1EmyeF6NLutKYqnZFWMYO3lqcv4tKVFT/OXk5eKdtRmrBojJERCAcSQViQCj1D93kKVGZaDIa52oz/heJeaT6gIRMQO9beW+hwOv+8197LjeQyttORiLJrsU8NVVBdMLuaIUdr2Qya8SO1lXF4gQIVrxQpRHhxoP/XyJpTMIWvur+B0Wtyb7s3zwan+cDqljyYAqxtdyL+qs4WkZmJ3rhDC8BFxKP8Xwg+vDw/O5fxztWOt4n/MqXxI6mjiAw4EQ/bE+H99d5R4lHg+VbTogilGKeQZHfUOPqYlwXDesrT7TOX4/9IypIkrP5ysPrY9T/Cq/uXhIc1+NELPFCydz0mHGI8zwaAAti8gMkUpTTnUVok2bR7yMpRqgRcTF/F37bU6ClQZlXHWyGK3dEPYkLFkf2cgF71qdzOzzaPpWGXNSVF5u5BVVeQWTeUdezf30tiqdGgN2vutqYEPLTCLHa/MQXoLPb9MYRO47NgjlTJznvODoSmOTNybdqpM3XXm+aDuyrrShYjdvFzm8fj7BEexzJM1TxND+JAjw8fqLu75+gpuy91CVS86GuAC7o4I/KnxfrctS1h8IBuf0m4D65oCqaWEh4GtZLjD1mwmZCVkd4NylRtho8/4h9av2wQCd9bkqdJ/MyOGsWWpWRWTlNn9Lf2+kWUwhWbGge/6oYWBomGNetQoPNBnw7Iyhhwq/vj7g8XrF7/7eD/Dnf/YOX/z4hgeSA39hSV5Iwmj2Xfpye7rh+d0TzEgXj/sGMOP5+QnPT9JaZsbjq0dTpYf9DgCJwtKVv6enJ8w5McYEYWrmwgEiwuXSQSQ+p601jPsOzCm7TnzFV9szJxoxtk216RS+6F3OjyQiT9zBDMmA2ezMw6ntaSBs2vcJTOBpSB2zEYANtF3RNmDSQIOsftPOYNzwbgxs1yse+xXP+xBH/WAAHa1t6D5GtCgFSdAjdNI+KcBvFPH87zPmmGvo4oo3MzDmfFB3EuZlJR8GsOI90no61XltXnAvP4MOOKwqv1FuOx3pYc/kPjZ16fqzlgFYz8EyQ9KfXS8SQ43TCt25lkpz0jM0kzsijOfkddtPSb7vRW6Y00N677ikx2qd2ErWbquWjDjRNqOj7wurtF3pBmRQK7/neUjy4QhkBSXEilc2bJKxEqumeW9kgFXbQ2s86aBX2380KprzVkH59tHxnLVnGa2W6Zn4zcdBHAgGqMwxY3u//RB34yNtW3Maq0uT4VkJrf3vd8wwiLqs0DjNyYMNrL8TsnpJIgFdN3Fn8JjHMbFjXCiTKeun5m1Y28rMElFhGbd1/PJnA+qEHIZY9XwA0OCjdZ+s06GdOw8Oc9X4hKqDgznKHSmrSq4vR0U0OyaElXcQblodleIEAlWgDRDQ8lPKO7o6GUZIWrWj7vdW2seV5U5MY2bTQ6GvTf/bubTmxOw9yrX9rYadMownIt8vFz2Vftmt1hrmGCBq4BbHWBC12D+YxidnUgX0COQiR1qMnY+Z8Injn4TnJk3PJ8FmaHHFWk3jcOccYTPaMSYsdUoblqgLzvyqc505MrkmrBkaScpdRtKPuslX8LC9SpHtjSsWto5TGg2fQYtT88zp5G+T4SYnpfIeeQKfleUoYUcgnM1HZ7A4+uV2SNB8jqg/2jTMOo3VQQy5qJZ2WzZ0Tr8xHZ//eH1419dfWTx4aPW+f1h/W728XJmPjIVFybEDtWUyLgAjl0sAYCteqfrAHQvost9dcC0e2BWoLcArylyNxfittDPR68zDm6gjHxqW/ROIpD4z1UXkRlPQgryD3g7qAO8qMCd6B7ZLx/PzXZK7TGDsu6zu6fjOaYkVOrZN6TNzOFHQqFEEETADUw8i8GFMgsCMRPHokoTVmFfUVqYIACbmbCAlxNY7wBNjH2Iskq3mZJoCG0nCA5PYcw48357FUMurr5ieXMfumEeWWI1AAjou2MceIR07gy49sgSSGKkNhK1toEkY9ykgz4Wu7GBxwJL5W6+sTJGNCKPfi6A0uOfcoZHBVK3VeWhVSsfmlbE8guQY5OpRrg0953mERXtoO2L+LeXkZ3xv5PreobIweurtl/ZgycjBvMjgRbmbQjXa2nhzxOboZVi2NjHgnR/27TJg2cNjpLCnEtDJn2vLos8CCinkN4WBpQgt3nNj0J6z8TFzK7csAHMZcZOtvJT9EuhaPpd+nI6l0bSG/wah7TGZ88SRJGlpZszHlsaAsFIfBE7jFPMn0+sAECm4p7bR3heMbOF0NcSvuZWT9VOJOkgGtAFoE00/dXRAnagw0Bx9cndt0DAo8sL8iffd8FxlBNkcMfSY72uoqZ0vgKTjkgww56kPrBkoRHrMjdFC3rVtDgcBp+3ImOEIvPXvPNLTDJ78zrlIUX7MVXPuH0dhhVHNCVMxlBn2MP1se+c8Z8TRKRR1ITldbX57K4VvzRh1mnD5XHkrmzHRPtYcB/bEC6JW3iOXcPW2Riuw9keyohu3Wh+9oS6nrFKTI0QSiWXYbzqtq1TLLeClr5WeSXcZI7pMOpkXFFoi3wt6ZZpmrJznfG0lJb5NQj1p5USHk77E50PhMPluvzvWyk/mqA7fb1tpZZlnJ/uhbCltQynt4/WBXd9oZRHIE8aAW6SiPzeaoKCi7i63vQryrM2bpuVFOQaKKCqp+wlbeIRj6Rvxl5AOsyVvd/aqRjsXQah/7RmVwQHUWLzmMtcYrfdYBbD3+Ci0HZqsx2DMmfaziOFjClfT0cIU49Y3lBRUJBPTPNURcqnhZaSZLPmO3q5gdExuoE7gHZK4xUAsx4QfQ1JsR7IZ0tUwzUrJ4r0jktj9ri7dse+JJ8iTZ7TesW2b7hmS/8eYbqSJoJ2Yc+g+zeEGGLWGoR7tnviJABBPMcwuArYIwP78jHucHOm8IPql4fpw0X4MPD/vUN++tJffQXfL6argxLt3dzB3EDZQm5jzLfY7Y/AVzAN3esJOEzsYdwCDGLMTeGue9CM8+Do2PbIz2rjbAlieQxmAZX41YLBeAazCJmOu79uK4vS9h614aq2c1kgylx4Av1H/2N78e17dSL/q+V0x11allfd22VEf+WKvRNUmkZ+1ar71HK6ZAeFKW2+/G1MMk0W8enAcDBiIC296T3KS3fHirUm06d4v43lZtV+y1zL7+ysdz1Z0XF7au8uPXNpAxcsrqxIhg+ycLuh8cVBrzzqgNLlqJeu5Ws28+nFWY5Q9gzEX2q63bHXQ9mTNJZOjwlI0nd9gOzetwc4cC15lpTfQ0GWP/Vz5Svo45hR52ppnRSQFRJbheEJWAPMoTOPFpVSTLTkSJFZrNQtqlyOOmvIHUtZo6om3vFjlK7J933DnlxJIihknK4oUn6O4GF/joTlX/kc6SkZhq28hUKCbgG+sVKb3m8xSi3bwOeT8bbpX6T+XkNfese/7oqulnMZVh5qRkZ2EhjGmyWMgnMhuR5hBDG1LkzmB4zzKn4XtK64gIq//JfkDsO6vbzWLdbN9duKMLUYmWQK1BPyL3JV70/bts/DC1rvLdPEls56FnORl4hk5J5OLqsmyaIyhn1vMVQb6FvptTgZYswKTnVObcE5eRFe29tVNfbKx7kNVvpIoHNk6c58jjsfSNlEjdMtvQTZyymdEkkU+8Wbkn9eVRpKMCXmsfOwSOfyeJpAq51q6XiM/0N7MWiY4T0Udsa+T0hEwc07fw2iDL7wmdUw7z7BpGyflBhZeK/M7ZcA1/ne8ru3z481Qn8ttptWhame3ku7z1Nt2zTHBR9Hy8fpArm+WDbUYXXYZ82jazzNPgU0WNwJR/poREtPCvNvsiqiCTapFezlWfvpb7qW20wJKTOEfwMtKBHvEEL9NHBMgIcxr2IMZfjLlFljt32MXRxCneDst1AaxvyO6QA66mQhdcqKLMhiS+pmp4eGBsDVGb0BrGy6XLAT1/TE9PMtoY568SNlvoSSMoYpwknfXh4TBmLvQ4vXjBa9eXbDPqaEhwNiHhkjlsCTgdh+Sb2bTA+FZz4gCO9AUhUOagVQHiLMCDg41g0kMgFhxmBw8wWgAa+gRWDKeGthnhmQc0GQ7IFBv2Igwm80BKB83H1fx+gtRPJEPG7us80XCGI3HDMhQ5lefL8g3Sm+j/BUoZ25D+W6hJzLkMW/cs+jlmN7lw9EcgJ61mp7N/bQxILwVcYcAACAASURBVJ03k8UgIT0Gh4fRKGuiMIIMqvl4QghMkqPf55jUzj4a9uxqn+R2FrkhPQnHkM1cA45JXpCGnoYjIEBeHaMECpVG1n5WQXYwlFLb1rl+Gvp32g+v9XTsM/Atz6lMBp3xKRZZmceEELIu2r62B+necbXCSrTRMd5N88xELthyjUX7yUVXKVC82lGWy6naC8hc9cNUnAZFBxnvGCIHarlp7h27Hv2W76xgOQwcvxrJCqkyjOsPl/eVqWOeHOsrBpe2zYwz35dk92FtP+re9Z6NUZEwyUjKbUitSrpOCeYrZEXcnIaX53tBgliRzHhWQCu7QRL6KcbPm+fJS4IvPbTSHLxzJJoFPaqjJ1WEOgYrPdyI1ER2MTZI5bH3qcjXHLWVbkfFGeATWstHp8g/JN1WI0N/pKay03hVH+aow36rocwq2ydczhHp6i6TGAgpA2jmwTQcMKVf+gYZm6mGrf/QsquXVDfp/FAyFH4x/qZCCXjnONpH2mdzvpFiuanhs2T8ou/JPlN7B6nOpW82POqQOy7ELDPLxsd0Ub4PuEPaz8vM+z456Cdjqfxtv+W5mecGQp56exFORncgeF25GIK7bcmGM6/iA2fnhn+8Pozr/xVj0ZgxzsrT34rwTIDV3kcwfwak9ltsz4iJYefgmdGRNQkDddJZ+xBhMuGVi4cd+J6BE6oe+aOCcx0NOGipQt8u2x8SsClqkp40/41aEyGq3vxqlJuhw8mQ0P4oKCttMAXL7F5u1qLRGq6PhL4xWpN9LAS4R9ME5ZxqAHY5mF6E41AhDeSEOXPIWV9jj6x5IqfDEz7HBDHweLng008e8LRP3UPGuD13XC4dY6qwa9Km++fvsE8JRW1aTwMBKaSBwRiTsF1UERnPmdGhh+46qIesADsQgISKmnHH1DEB9J3BbaA1pbmDYdljKVcHtY6u5101CnDZ9CiPBkk2aEo07wNZw1ps5S8Myhjrlsc4yeAwMlbQlctewEkyqICs6ANUnPG9g3tTPGYszhmaUO+viIU5jEBraiPCvuturQY5pFfHwlau2MpzfvJE6drd6YBPMgfWNjQFLgG6Vp1WgXyAaQMpWk6re2TCWLTMyVTkYS3L5IKFUAsoZqdZgJqDLFnk5Nl1Zgi88GBBL5n/Cvg3g2QxQM7e898zMgfKPsXCdyZfl3vEFn6Y0VWS+CbsaT0P18AcO08GLZS8it3sBdv/E/NVCwKnLpBnFF37zMu/rYyx8Rt7u3312Of0GhKmhgwg/GwgzJpl9EQGZ2GA5xVKWL9s9dINNZxexSgxJ6BV6vy/8Ic9YUaWtjMMNm2HypGa1TLNP2ZN+mSrPYCcnCdv+2AsTinji5ZWqW0axb7CRDeT9RR04Bk4xByO0M8WfWIjHOOUiTgj0Vzic3OwRkZwmztHOZPlrr0vexOPtAzaJWzEMZ8pDRmV4WPHT6EHejgYIowl6tJEgHYGbjaUUiuOfSkZTEWPzRnjIcdTSZirjwfkBExJW5yDSOsCAdnZbC7eF91pbbb3mTRrKfvKXc67wZp5lj0QjAp/JQp6LgniwHMmq41/mjorB8V42PTJ2pgVkoCD5HZWdzyoeHCROYpAar/VcS20bOoEbwJ7eMjccobS84K13Kyms0NgXRCizBuwYaCyyp5lQMw3SjfzuHGh38frw7y+trFo4XIh7rO3mPxsQMlqqSFuLyooA4zZ+MweTwoAqIqCiNCpqyFlcdDFR5wmciDDLCCo5XCEACveLvub36H0m4dinQO4M7AWxmJc4rGmUjdriObLlxmKYaASImQ31ehCWDKlEtAIbZPjHm7PNzxernj10PF47diahI8OLc+EVNO9Tq9evZKxJ8J93HG/7biPHY0aHi8XUCPsc+J+3yH7IwlowBy7AhXxxpJsPcSOiX0O8DCBYW2Oleltu+B62fD27Y7b2xuu1yuu1w2YwPW6Yey7gAACPv/iDW5vb3imTYTUELDResPlcgURsPGuvELoaHi+PQOtS/iKCtKtEXhODL6jtw7ob61Jsh1RJhZai9CrYsV624EiVmEKLozE4XxSQ7tNcMbbdvQHHEiF4J4K7sKgNFUSStWMvtUoWI2ADFZsH+AargLIimFXUDA1RM8cIiAfTOcXQzyy2b6uDHQiNGY8XK8yNlP3f16uPobUuzg5LLyOQnlrhQo84KDIV/VTXQ2sGr/FmCXrMTBp7rM6DxaDp2kCKCZSAH+kbw5bZxbHQLP57kk4MqiIlW7wOIwPvL9HI+2nucJLrdyU+in0aAF0T94LoznunxqMWZ4F2o3fk5xe3/akRYEG43uoHK07vlt5dZWpGk/WeSuroRcAk2kjf22OxTFFkq2Y/UicHJK30vOlaz1/MnSbGSIBarMRsTbUzlV043DZIlIcPwtd0pdarKlrNuPJQGZ6xvW1gUIZCDfCvB9UEoq0dP6t1X2QL4ckJ2bkQeRRNnoptncQAX6QOjF66zjjzSMNLDkWbFI4b7EanDZrYgy0jWB3EOXkP5haPzhkL2U+DOMiz/+e9P4cI97N46MrNxPmhFHATij5c4Ac/RC9kGyd7DJQW5O/xnEwPr85xnq5IodX4s88TxG8Ym3iMdTIj9BUZqArm2Xncog9K0NpN+XztgWUnXOCJjsWaotR6SYNR7KeulK+zF8TOSk8ncGgTvHuomdMB1q4ZZaR8jjJXFDjaYIxmAHNKF90iOnvgk0j83xpPy84Wxgx5KfKRgbAjlN0bBoBkBBey1XBPPDSdZD7JyrJ9NmaK81k2Sqn5xlzfbw+iOsbHZ2hH/TiMonJmTu8DeU9KycBr+zBiHmagacxXQpT0NJDSeW0/TqpU/kCaiP2xDHGSQbI2sbyo7+fZ+aZYZjBZ/SfPVENeR8rYAWFp/GICTOtA/i9ZLiStsHOoTM3KRGKt5w4QlmP8C0Eqwkp8aJOkdiWuU0VrewznKr41GhpFB7xMQASD9rIXSRNZtNIojsVmBavOLQOrX+MHR2bOg6MEPJ5JoPlcukinMwo1NF4ut/ADIwhFW694dXrVyDSPZoEjJvQZkwNu6WG3q+hJIx2poxYMxtSClWBrCpYBlsD0Hk1wOZNBZwcDo+FD5RoMU+W8aP0RGKQxDN5vlGAaB+Ok3mhvOoe8qbnms6hc0nLXMBwrhcL6GBAz5EKQAg1VmejokhaMg8zFaSK5kqLZzW0fKxsTuafMk1QV7QyWAmDyRBYCzFA+hnrPqSQH0Zz4xEjS5n+S3ujb1U2OhHfY5Qc+CiJGHn1+C45fTI4yW0w33HIv1IKp7do+e34kJdbAZIBxpDwpQ1koiYZUQ72k/RKv+d55oadddPHxHPJxDzg9C3Nj3yY/Xs6+p6L02uqF1yu19X9gx6h1DZCoV3oUE4rXVm9BB9xogP8dyuLbULInDDZXroQWRLlOeVH3z/PBxY98LefSQeAY674CoU9nshNeX+z6X/T9XlYXrrSfHc57LRR3ZCEBEG2fJkpSETunC4zwOYVECF1yXhrraVMrgH2jys5tZ3xYzZqkBLPkEAYpthT7dtD7A0bl+AdPiSSSTkWvP+cW+qGfOxTDTnnrQtgJfQyRrJy0hmQVbaGEAyZFffzmFgn8j2LDkpMVuWK8qa1yPDFKgfPHQym6+tda6dpJdl3iMQI8VyUm/SLtr4T5Kg24y3TV5ODFumlNdKTVBebfGObg0QgNE+K6DqqmUP+3IEsTTcHdeLWJE+kT1TeXelYz5hNdaTyKld/vD7E6+uHoS6jWo0+u3viCUVlKPFoxOrYIRQKOpnYFGtaHVGQZcYhUwh4spADwFcGXfmalwNZUNqBUuR1uDJdJvz6ef17+L2A7xCeFA/mlyLDV1aGRDgh5ZG+J211IMXqXSTpe9sa+vWqXq7YU9Faw76PUP6pvH1OSXoBYPAAMzzEZo6J1sVoMG+bbMxuqrgaeAit2b6jY3KXlUcwmhGcxcCzMFtPK84MHhPcBZj31jF612QEJgy1DEvQQgBTQ982yQuk4YsiyCREaw5LT06grePh8QFbJ2DewBjg14+YPPDFF19hv93x8OoVPvvsW2BIQp45B/ZxA1FXI6Xh9cMDqN0lGYYeZOnUbCSG5bSvuprDsZ/H+DBWNPI+mqI6dFUUIIoEHmSIOiuDZOitXj150jyMMeZ5RcN4yb2pZnTrUSB15QJlhbx6VuH1gQC0Bm5qUvteLXFMtMsWySA0PFuaUTMgBOBVpxJ1p6OvjCxzw7+nz2zt8u/sssLeGw7q0rzUOer7yRAGjdVBjSKdvGEaMpkAf8/qn6sTK7U1g9yzPgVgyG8HmHrpPZeb6b0qotxcQkLvqU3+lk3lUgevQnWpG4A7i7JxbrxlUSpjpKRZlFd1Zi096SZ3crguqePDiBBHdziq3ujJPX4wvBBzZQVG5rCsRpY4ushJRyDlV0OCcU5s8MmLxiOg+i/ReaZxpgiFzG9Z/ysL2NFCNWTVjYM033JfCYAdHUD+WDhx7bvRzcMB0zaWpBRBBEx0Hysk3Z0+uKPOBB+B/NDwLLsMmALqJE2YYGahhPoeqdLsSsvpmcjhQDhCLGOWWd9GE52JJqGQU6MFzAXYWsioyeRDRFhl9GKoZEyjNJXxjP4WLlHa2OGZrP/6fLazF6nyP6xtlL4l2Z/nshk5bHsGVSgbH8yUOKX3LslUVF+aQ9N0NjV9r3HKCp9oBXZdNYc+MHXF3ufuLEdOSXnGx1jcjkHnOY8raiZL/WnTrcj32Q1low6DnHYgiJGnCWIa1DB0rCvtK7kKFbtJ1Mx5tAcodJyFWjf9PjTMnlrX+Tl91dvHzPVrd1nKzIU+pheY7F5azddDQW0FGuW93F6TNREyzg60T3TYx+uDub6+sWh4HCjKuICKRZBUb7vf9N/yXwDF219KpRwjTrUMZbqJGSEg/hzEGPCMfgE0Mkgrhlyqv9z7KZk6SuKTe7XUDLTPwI5UqSKeq7fcnktILW6lMoPOoQxsa+Rk9nP+pm3Y57zqZWGGJrg4aAgBab73AzE+rsgR/1uz9n3H0xNj6v5GEIOHKtQWK5mjBSiQFUyOMUvHE4QxEzR30GCg1zebn4TVFGMIzuPM00OrrYOtk66GmRKWnxoRWu/ovaP3kbvtK4umYrJRiEIZpH6YyonKybR7pqmPKXsWt9zgdT695E09rJ7l59M7bf3eu4OgMh72/jpO1l+fmvK7GFzpPe0HaXciVErbm40xB2kKzq0eNe6K4ZI9mid9riv7agh6uxWK5fm3lDVXGmZanMkPV8JGrzgyxtrrIgBwWld5EqBI6Ho+HsYni10T7SyGNU77eX5RamBtDy3z8uxdouCJsretCLKgvZV25sTz3/I961vm3bVfRIcmCq21b0TFecnEvuBjQ1jMYbZxOtbB6avRh7SaFw/PXtplz9YfrHuZLvL9OI5H/vH7i0XoM03pmIelOEDs35S5159BcEl1MufCXHo7DUIHRpOcr46EOVCOYMm2jDhRnmdjB5VZQcuHjHOMnjZ2Me8M3KuhzAjjieq7LqsbAZPTOo6SmWOeioxMP4YK9D7D5xkHGxdmzLTX9ludzrxJz8aAOo8HD8BvrHJ/rewlh3pDwzQZl0XH+pzxLmwcpWKX6eDQlTb2rNKR8soVJeeCEyHaToVU75HT2twsixdnZBGjLh9ouWEV2iTm+DWPWcYkiFdLg9Y2Jqah/I7mbQiqiIEX+DL/lc/F/DvTBYkYK6by74m/nIuyPMYRc3y8PpzrG4ShhlIPRRyT4SAcVDhbUovTZW8yEUXlXl1B0Tot6UsysHrruN9u0JwruLx+gB9eauWBQD2n6F4N2NWLnvuVlEvq13GqUsgAmxSW2FPpxnmyqNS3vQq+76vQpyr/M+Fb2nQAC6Ex/PRDYgww3u0T7+4Tz/vEbd8xx8S+75oYQbOaTj3EWfcHumd0JqNTvUQMAQ2X61VqUjAVjkg5dBpM2G83fDV2xIHLDJ4d7ZnRevLuYwDzgtYJYww8s6xOzrmXOPc52QFKTeYgSXAmJ2+itie8s8fwRlHkG8Z+l3MkPYGJyNw5h4fD7vcB5omH3kBdPcbGCPa3NVkxVXBARG7YsStV4QmLGpG0+fmwYThP2ipNS+A1MZp2UMdd23EwbtI8diCVQFUW6M5jrcXzLMb0drmUfW7ZyZDn2dkKH/MUrzBIaQw35uZgPaqANeGT7l/SOeR0BvzA7LI/J5ig6FWPKygTOACfe/yzMUph+ND6DmxccFhFcTaw57FelP6NJp+BlUVaHX63OvMzp2Zh0uoVqNexLsbXT/QAV0PurMpTcLP8RkTgdkKlZC3kxC3lGXUIyupDvEdExYCvRmx2KpIFH8Q8gGkmLa849siPDfK6Vpq4wZjGRZ2ZvvoGQPbfz3j2JTLnyxMMGkBlpx8hyzd9vEXUjRl8R+xHIIr22bPRJWufMTxAsFWczDfZ8KqRRr5lRO/lpDUG3k1nr9eKC3K5LoMOuj2C6lzGaxF2viUR6fbFyhdIdBTYIXJqnee2EjjmVJ9GHlv9v0efaDYPV43xZhuBOHbFVjDLWXbWfpsT8HJsLuUNDG44wo4pincJUF2ubO8OEH0/O24kW0rofZePkkBsPf3AVo4IFhXATnsCHMOZzWBHvuSOkhJwlK0o0loCgN58Nc5bStIec3irkgseE4IGcRIZT/FVcnZGVEfGKCbXg472mTBhq29eLun46taqBoCn6tWk/4lqn8B82O+skMvPgsxz3o/jmqJnpeoGO+saANrW/JmgAUAWDZUnjY7H8LlRNKHid6NTZGI1XZr5w3SqPIuP1wd8fX1jkdLMwsLQ6zN+Sfz5CoKKYahcyoCfh8ilTPlnurALViUNBRlT1TpBALnmUa/e6XNAUx9Znl/uv+QtK6+VRH0mrKJdebXo2JYFcjCf1JUnalReQgmzQkiuayYCGuF+H9h3xj4kicO+S3ip7W+00KrWxFBrIN2epcqT5agAmDBQQ27bOggDjSTcUqJfJNNf7032ng0ArWEflvRADM7JDIwJW8Fk3rF1TaCz7wAmxmDc72kMSHLnyUdZdWuN9KxJOZKDG0SxGF9lTSoDA6Cjt47elambKNecdZEaJFPdlFVQWAIH1PZkKWv1mVBsrUOS5LBmmhNjd2rbDYysAjnGNFKer3OK/RX7sBh7tbDgwxf4i5d7dfXGQtIqKF0FQjZkwwsfNCGWUL8ArwL693S2J8+ptiJLEhiCgA0Fj/u+i+GKjk6LQiULmdbhzuDf+2OgYqGFg1hRyPksrtUAsq/hOIvvmZrOcst1MkJekXvFlzqkiYvRlICR0fSwwkMxJnm8XjYGo/JKnlremYH505f9/mdNzp0ZrmLYKIBb+uP/4wVdldruxlEQTliC/YV4ORmc/v7JwK5GjztasPSD6bx9qLRc63zZQK+8nQ1u56mTd0tdqc+c6QJbjZtBoxf6n0G43flpOeGoC63EM/378li44+uk/KPRKpwir4QerDIj9F5pS+E9IOSwrTDq2DfEXjJ9kUxvrPLT9PYL81zVtWt742PjL7a8Apl1Cw4LjFEdTEELWNnp0xoB0GCaKZ71dikZ3DEMPmQEP7IiI+0dSjov+LET+cLcYHbedB9eKTTGzd5faXw+l4yOy23tmDuJmCUyK89t5VfHgF4kwzIfEwhsyRQZmoDGq000JJ9r0fYA4rECrIZgSqZnT0oVEb5tenEon3tIv86XHBIPRIROHS9KVC3EqXMydT2Pw7+GWvh4/QVc3/joDPmr3xNjmPLlZZXMzm5raWWilGNGjk1+A1l627IPZoBmjNt6x5wTc4jB2HvHzmYshoIU70orwibaEuVmAzYz8vuNRfbFTve8ZpqpELbVuXyGnE0mC3cMesYkLfRdPpe2pD/NNpGQfqeGSQLML9sFDZuER6qSoK2DoCte2vemYaCSnMXaC1Br6LCzoAyohVfcQls7BNBTa2AMMR5ZM2QPo4y+SRuILJbdFFAHoaN1Aih4qDVp69RxbQAeL1cw9mK8MQ+8efO50leOsSAQeteStyaaWxH827dfgkhXDBrh0ghoQwVcw32f+Oqrt6H+qeHh8RFjHx4CaUeBXK4bHh4fcble0boe/K0HlXdVMnaAbusNnTY3vmL8ZcXBkhCZcmjOB9bVusfNmIBpep9XMIWFjzkxvM8BP1D+CErN6TFdybLT1labw6SM9oZzSLyfnXQP6hhAJzl0vDV0ukDdougA2rsdsg+E4q/y9oX1iBIijDQP7DJwdBa+Gf2N/U7pB9/XdJBZNiIzTFCm2Mubx4FAnmG4jvERuCjcKTJkBb/2qSVDcO2PVX4mw2of13eO1xGY14PcMz28jRxH6KwG7GGA9KiBDFisn24o+8ry9N9g8hsSUTBVxjYLox+Z/+r4UeMEYKOfM9YlAeiqjqsRA6T6hJ2hmQzLupqXdWDIedYzXPO8lIzKLYH1pDcO9D+R/SeXGA61nrzyn68XjXuyzJHRKn1D55Lxu5UPd6pkHWv1ZiNhrSs/d3AG2D0+yqJchjuvsjxrre4FXeZTLafSg0hXqvR3MSrl9zEGujkhmWM1hyOCycqK7MiiJyZGHJG1zAeLqDD6WjbVHMlgeo5tSBiHcvwnijkL6Kqg6g3WpNWmA63NEgWUt00oD+je8XJ0jVdUj4KKrT9cnFbBNxbxVC+RuYGBOJenesl1itND5xXrWl+O5Ke6GuYGtfGg8kqs3Fm95txv3ojVOZr3E0rGX9jR8y/IeVm9nTPd4ylRO5BEe4SEk/LYMqvOle9T6TNsu4zmg2CWXBKOkZU/B0/Pk2BGrfDP9HtZ98/EDxbWaqcQeP/T+Bgp9RBNKBhcZJitnjJwsoj08fqwrm9gLIY1EspkRWYRyrYeorsqABKpEyVnUNqgGQwFHJqwMeBghpV5BMVDBQGr3JJglWdmCU+UGkOg13bZ/jKXRP58/F3Utz3h33j52c6byn2nVJD8Fm08AwjvAw0h8Sk3OfbAWPZKiJK7XC64Xq/YmhnbACCpnFlTUDdqnnyFeUoIpoK3oWf7NALQZPV4zolxv0NUjaWDhq5IWgY9AUUiS4ZrIA/LIqNBQ2uMXRPr9M5K+4YxGKENFPTBlItUSAQwd8wpR3lIqm7ho8lTQ5CQQN7E07snUczC4LiLdMQ+GkASxvzu3ZPmYmlOX3fw8cStA/d9R+cI5ZgstLgYYLAkD2pgCDFm4h92JWvzAlnxmCHGKWxIlb82RMvJ/JG+n/BSOcPMukQVfrjy6xES2luT8zOpJSOWdMW/lBZNSYUObe8goG0ddL0AXdKi32/PuN9u2FpD//ZrgBr69gBTQpgTvO8Yt3ew8JoDCHYwYopRp0exCQP0rqs2eSUh7y3yuWz84vVFTI956412q2JdLw8hMpmYQKqlb/H6l6m+lpH7toLkFSxTLoidUqWf65XLMHPMysrj8BPlFnNJ0vJiffqPHz/CMe/1ReVXHUsz4LLO0fom85oI+2DY2LhHVsIDAYIG7zFejkZP/L6Ce7tXQKYRd2UcqkbUGY2ZObY4n7Rt7T/IDFxtJ/OB38vzCAeLwwGwrjpZ7zS8PCUrAQV9jxwcWCDXRT6+di/mmp/JmtqazzeObJHViPWCTq6VfyXTt+kg5fI2YQY5IFEnADBG0vUwwyd6Jz0XnRyYAPBBzsclAaB5nJ/F+EFOiFZJ6s4S13Nc2MnlExY+XuRVyEx2vXm4kkEldJMXLcGNdM1CH6sB6REnOMoCyy4eiViFPjOtzmb6SkU25hBHgZcFdzLGGZU6IgtPGv/aFzZ4scjP6HycsSz0rEZjcTbZHPc3RKVZlIQ0K6+sSl/EtmSdQ2KWNjP+XL7YfEGcocnhNIKaiR5KrA4QOT/7PIzdedEEcdITTo/MH/aOTHd3btRxjW1IH68P8/r65yxuAVqDeVooaRt3PTKIDIyHJtF3RHiZHmv+TEY/WiYRJjoYu2Y73GRlBgOMhrl19PvAvO8YPDEvBB62l4RAuKJhA2/vQLMJ5CPHFgYRkWuVw3ZzaJx17Ggo5lVCuwxAOOBEUmx+X8v1duo7bU2BfSY8pU7zYPrxEd6+EB5NNXXfr+BGuI079qeBf/tXfgG/9quf4sc/uODNm2dc+obb/S5hm7A9doyx7/j2Z596qN/DdcPt6Y77sDPxGlqXSX8fA2/fyTiJobmDJwndXZoPgCceHzb0HkB1siSOud13CctUA3byRGsNl8sDeiNsnTDuqqRJVhZv94H7sGxzrorlSLR+EUOCzR/XJEnp3EGtS6ZUGTSwx3EqfVWzdFMiPDDuLAaOL4Er+IHs7QRPPN8mHl8TNr5IlkywHAzfLEW5I8ZyFmmEOibNlJj1oMwotbewodFAjGvps54b1jYFoWL8d13tHiPOfmTl4c0mSYwcYPeVfWlAz+mUls0m/WI9EsMNSIZ7btnINxuABr7fcdke8b3Pd/xf/+L38T/8xt8D3vw5vnx7w+dvO77znV/Cf/Vf/5f4R7/1v+JP/tXv46vP3+Grz2/49Nuv8eu//jfw1/7yr2CbE23XFf45cbdU7yAQNtCcIJrgNvU4mQwISP+r8D1PUbK5yNXLX51m7El6ckg4A34mJfmzuaI6vychsubqo12FUcI2MIOhzH3nkVy8lquuautLAXUwAL7sm0uAzuRhhI41v+99XehyZjRZ3f6sZSJ94dgVAL4Ck6NWzEyX9vYyXVgzDzfSMCsjJXHs3WIBonI7nC+uy9TQ8CbYqtiIfT8OypSv81mxOvKQuWiHxm8SVk0dBqwkq+a5USo8V8P+hBwiSwYf97pn8vl+8vR66wY6Y8wC3IeeW3GcjVsOHXTwaG21FxmYlqafU+g3FKQ3a9eMeaorUSaLMt2DL8nbzLoSK0PQVI+o0dSMF6Pvh5WeaZEdQhDi6WfgHnS+jjNj+vYD2bEQsjzaa22Mfq3nl/aSLbqYZMZICZCnOUnL48a3zZ6TLOTFCEuGqu2kPQAAIABJREFU1Srjcx3OMyyOMQK542raubd5ZdhokuayJ0BjiIGNFN5Y5rwirakyM5HHozCsTMNYzK7Ho636jvPpUCOKvO+udznmm8x1cyCaS1FplTCu8bfTz6KtiNx5EPrcXpP5lIfcHTuWtyDxCGsyHssuzmDJPm9Gsm4KtQUMOxrMjfGd3diOozECCYkUMiM0YV3XgSpfGqVjd8hCy8KAhcrACWTntvfRWNTkodMXkfypvDHLGH68PrzrG6wsnsSWU4ZYCtCSR8SZByYDVVjpodjuMcwF55cgYSADkSVQFJKs9jRqoN5AQ8KPOgjDDU8TZkcjL4AVnFursjbkEcpmLSM99f5Li3EDNXeVIgyD/F9OZKDc4GIP+EpUAny5Tg8dAHmprN3dLoTrNtOKnRp4ukoIbxfhoklM5mR0C5d1YGZlJ4WULBfSjdwMwM7nEqOvY9vIyxiDMXJCFy3VI5RBaF3CHP3EExWu+wDu+4DwZ1CRQLhuF2mdH/khvPl8u4HnxEjc68mGFMCIMW6HdxudSI3PUM4mSicmxpD3GqiG+RZmCGHvnl3jLUqrFQYakOkc8yIDtTLuOsbuWU8AIenyIqWtbiINwy1zUJ5tdgaLz2d5tlvSBzWWpPzkFJHKpO0U9TFNNGLYNo0xCV/++IY/+P3vYfzgT/HufscXT4S3b4G3by/4nd/9E/yzf/JbuH11w7t3E5/97M/g137tr6H9GkA0MUmz1auF52GAzHqYsipjsvTg1vFMh+izj3qad9YHp1d6thhZ+XIwXtVrcNXpEIYHXO9y/tGMnMwf3t2jsVhX++T/OeMdqv8cGlNlp3w7JqXIHSIIGMgUkvsvrjRaf9lC8EoQ6aGqkH/Amn3UVuUELMXeOpesaigyKD3PZf5E27gCmjRvE4OXfuX3y6qNOpmK4yjxoM3pMoZa32E1I8mWc7xlEoNUD67vO9dCjC0uz52t1MPeIBzG1vk7AUMHi0h98GliWxBMR0W5zrN1Eni7kMs1mtnziWaJVDEW2taavj9xaJGhSOXJTVZMUOYM23w+Wbl3VjkPfz0DywbgiVOb02SMFUsXxmm2LMxq/cs4R439/KTaU7DOUby48Lb28VxUlDlj+scEwxpm/BJNSplEaT5FTeaw4GAHbRPDDGbj8FgQCJ4/tDzNB6dlkQP1HfJ5G1gpM82ZnBPs64SGOUEiJJR8kcXHC7rlQsOYGpGfn825Hkp8fBAVgUJeDMFO/1g0nkMQTv+/T9pQpp/Jc/uA/OHj9f+D62sbi1V4kht68mMIGl9N8DcpKYi4JwbK0VB0QQ6Zhr0RGBuo3YLh9KD1jSR0jUDoA9hax6ALRhMPE5DOjkkJckpLUv15Je+wepDfKSsHNSzk/DoKW68vGSKiUKtQpJWexYA0RbiMj/9s3nGG7D8hAB1j7Ljvd4yxw8I6tq0LeT3CU9rZ9XgEk4XWFtK+MANjDs2mqv1oBNnJL3+ZNSx1Sj2tN/StJ8W8Y2rmLqGvJXqxtH+ykhFAYvqxDf3O4HnHnANt21yYEgHX6xWtAWO/Y0yg6Z6A+75jTHYvdtdMd4C0cc4hewV17yARoVN36Td1T5bawhhjYICxbcIRsr9C96iWsYsVCgMcpjZi3NL4pusoYs14DUgtrJSNkygrgFWUZLzoHmHVNpSYyjyEvaunUUHXJMkA6AmGmGUf3SSALlKBni0lZ1+ZzFDVSAxqd/Btk/2VIHR0bHfCfr/isRFuDzc0Gnj7bsPv/s738Vt//5/j2oB5ecTrzxlvvujoPEFtx40axo3xcAFoDvS+ATRldZo2DCbskltIxinjh/xlkWm2Ny3AYA17A3PJtHwM4Qm50ohKJt8zibHem5kXFjBChxfZZUjhKQjdCfBMhK1Ndyp4Fe7wSaSJbup7YnTb6kC+ElaH7Z9Ze7XK2BICugDdI0SrstHKM7nhTgrlz1K7nmUWmac1bIzIVww8VMz60cjPRzsP+aTD/AXqam+V/an/jnSNd6DzlLFygeOtla8MjDMvtFuvVftmI5VcPlgZvm2jnfA0IfY+rW1iwBOfwT7nFug7igdsj3w8FM6t1fZxWae8HWVzAFolaUMrq7EEQmsn8zJvlUlju2IC+x2un+B+5ALQ09DFa0cHyfuuvOIDQFeWlrmMBMiJPPPk0UiQN0xH5DbZdxvr4H1yA8UKMd+GrSrbeYkWURL0CQOnUfM97VkeHWiR2nSQnauMA2D5be35gnnymZWZ7sbmSWdm49toZPox5F9REvpRnaplP69XWtti1KD8e+zt9CQzVk7qO3ubrS/+C1xUk2AaO7/UGm5REtb+Zjk8OH5fV7vDqdZOeTYbm0cJZX1OMpeojGXwwfLOTz81Pl7/H19f21jM6epdUXgWR131kZthMIR210KEaZsyjpUp80QEFVPsRQQAHgzQkPTTLCGEgwauDx3fen3BP/7jf4bP/5zw7svX+M//xl/F928/wg7ScEjZPzfuDUQPoBYrUKdGYAJ8WWhTe2HiwIQOHYQTxS+arrkCp3i+fn/JeHXJAQAsiVHMWMRKZv1BwnUIW7tDtppdQbigbQTaNhBtAA+01rDvcQg6CJj7BE/Z3/fw8IAxNwCCtuecuFwuePX4Ctdrx22/Y8y3uN1UEI6JwQNOBeMD/TtJw5ohY0qXK8B39I3cIzmGhGJMZuxjB2GCZwdNYB87xhzYLnIkBTXgenlwZdPIoigmgIbL9YqL0rO1htttx7gPzxZGIFyuF2xb8zCV2/2GRg1PT3eMMbFtHa9ePWpIqYTE3fY7BiZe8YYdEw+PG27PQiO21djecdmuEdZB0BU4RTi68TzZejEXFiPEBTDFKum04z+cAcQQkzkkhXYL9UsCPPNW1/2pc05cHjbwZOz7QG8d22WT3/apoanaByLQRTKQEsk4Nj037NbuiWcpzkBkW6WRxDtzAO3xitkIn3z7M/zML/wivvUzn+Ld85eYz8DDfcNn2yfgnfB6n/j24wWXhw24fIrXn3wLnzze8Bv//W/g1ac/h//kP/3PcMENGF9ie3fHvD0BvAO44zbvIGp4vAKEBswrfE8JIvRIIp4UMEPWpUw5TmSjRhwkRstO5wp2nedTSfKSI8pDnGweGwDT5/yQbEqOAATokD3GYawgv29K3uQq1/olCZYdkq4yKXnAD7YIH80TD6fUYv3IhhfsmEwz0miTHKGQV7eYVdsk4GR0sOMxRO/YPiMNV2d1djXlwTH8yIBiKCOAp4HirXdMqok7CgmAkgjqSCdt34yEP/5zAVLeiEILu6fNgx2+nnB5ocOxfQl8quyjE6fp5ARgWztkq3Q7sVE5D9X7lAzJyTJyCZuKzEeq1x9PjgPzzvgGlWoMSvIsC88W514NCTeyTk9EI99F9wpbx/NbtyMvxGEoxo28TxrmC93Swqa3NMyVTDa3iNgIY6hYv0lm6NEvJPPk4KhDgPjiUJmsW+QTf0D4DrYflOAOODAjpcYGEFEzNqY2rnkMt940ZJYxxq5bCbrzLJEkIwOLk9Blnx2rNRkSpaurr0ToKfJGDPYISbV6M/4jwJ3All1UMqEHPUSX6L5UIOaeySoSR4LITq2fzemsYd/c0dpQGpnBliLXtD3Ca3U8fZy97nrfjW5tsx2x40bWiZ7oath5ySQrkOucYa1QxZmUOZLhZ80wo1/1svfzRPfUFWdth2IQG5Pusk1c39bW3I+zENeDoMk0+nh98NfXD0NdDMW4TSpAW5oy+luyZkzgHgygpXxYGQqmiYBOm4Y0TvTW0doGcMe2New7cNsZT/vEpTFaS56hMSS74kbQ3WZR98LHRRgnQ/Hs97V/ToP4IcAIvfT+Wt/x2WxMgtIEL2VzeUdfVOFMou8WWCfpkjuobdguer5cU+VlXqgWx1gQyerbGNFy13EmvADMsavh4z1wccxEknyUZS/k07upXRYlso/pT5tRZ4bf1jv61nDZGjD0fifNjDoEsKnn2MANUSiR+10ypW668ih1BJ08nHQy9jkwx8C2aQbVhVfHFCPadESjhssm50s+XDcACrZiOBUoKFCc2W1AgGZRs8ljexhsHJnUWbGANKbmtA1eQRRkpzlS/T14u94jar5Xxdori9KipbquyppXVg5CMUSoq8cETViiKbtZVAuUwuR8oQYZEe58R5uMP/zn/ye++9v/Em8/f4P96QmTB+7jjs8//x7+p7/73+DPvv8HACTBDfEz7l/9EL/5934TP/rBn+HVJ5/gj77YMS6MX/z5b+Pf/yt/Ff/GL3wGHozGjG57ybQvQ3y5iZnNQEwSzIBcGf24JBEWOcViyF5aSeA8GEfjy+4DbjCWVcUY0Yqlc5uIPBlOLrMRYXp2QqEDJZBa6mfOTCV/zIsB45lzZZ8NubqaULsfv6dnOL+jgGQBlS+t0BRwnYyH8rzyYm99AYBhpFu7eqcClpDLn/mQAPsNbrys5Ge2bNmsR+ewO4NOuUTBvq2M2XwUeVINzGoQVXr459T+I/kC1B7akMoxAwd8DjKLwdjgIXOlvCx3zKg2GlAk+DhzmJY+Fh46gs8s085WYlddeCg/mD/mqU/blqaG/G5vWdbJGh6ZDF4AdTU0tnwgAfS1X2sfGLwcvQHXo0GjatCoveztXmlTDCSe6VntQ3rWyWFjZzrXEwDVukxfnIU1FxlANt+CV6J91g6IQ4ttaIx/1NCEOJuDyjZXMy+ueKjip/gtzZfcnCRHMy+sK3UG3Rpsby27UVxpkOVcGNNEBJqsDsJ412hv/RRnQ+wfz/21f8VQNZ4q1et4CVjx55PsdR5XvEmAO9vgNCPleT7QsSysuH45Rr98vD686+sbi8zutQ2Di8JwAJyBCPCz7eBzKwElU+QJlAjPmuK1Z5tm8mTs3ND0PDyCgHKA8fvf/QH+8A/f4Uc/Inz55tcVkJrEkOeIhodqVG+mzxOEsUde9dKjaL9Nbt1QHLKtCpCq9CrGs35WwB7lINNLW1LqMZql+gJLWlksezo1ofO2idH1x3/6Bt///lf44vMnXDZgv1OAGmtTaxhgvHnzY/fOMgbGmGhbx+SBr776CvRWM35OOaSd9Nmtyb7SOcIQBVuSiDu+fNoR3kZp+BhKlxbASNJIT8wJ3Hcpt/WO1i28QkIxgFDSRCJEN403HGOXdrcAks0NJDE6zcC7Pd+x7zd86/rJotxs4/r0MFRZlYKH5bRLQ59d9/dBQzbJUL+OUQVoNZAtI2pb5RVeME8iFh7R4FkHDECAAwIhs64xjBtt+tu0uF0ChngXcFEPrzzbxMvaCMySxZZ4SN0MTB6iuJqs7LRdyrMVO26yCtdVm8lRBxPYCNdPfh50eY1//Nv/EL/59/9n/ODH38PVgMB2wfPTDf/d3/k7srH/UdqzYWDeb/jf/tHvAJPQ8Of47X/yL/C8P+NX/9Jfwqf/xXfwS9/5DMAAmuxlnhiY6JJchEd0PtGoJWLZ0A9LDpTGRd7alIcACbNVoGVzEZV38pVpn58jwA/lXl8LXrSw+lKkSypJnpKHW5wwFhaWO1eNYWeOUq6tfBzCZ7O1QscQW6QU94DjmXSt4ByH72Ek5qoiHHE97y2Hkvp7qgtYk3K0poYYpyAztvNL6/h7Yqykk2xfjtUpYMxWdOu4S5kMc/RIVuZYPT1bYZZbC03YwKIm19H3mh+w9v7VW3lWZE8ZI1SdGPTQhFcOygH41oBa7kmF8seArbW7GCfNMzlbO8ye8e0GS7HrkVyup7IhMJUWNn2ykaO84Y4jvTp1mbtm0GkEiIy57Fm3kObu81KclTa+DEQItxtisV/Ujsay5ljmVC3KTo/x962fUzOTU6I3M4cjgRS/2EHoZ4ZkpgMFyplz6NaQnF2G0Llh9ljhBKCOWJ0PExppldqjeimRRxO8MM6yvMuWE9PXYigKrWQu8YgkWl23x7gRW5wUyjfpxBvnZ6MHkuzhcIiLWB+hK81pm0QkNTs+KWVzB2IOVXYECHri0xIeW/gxnKbB9Uob7Z+tKBLJHnw5H9vaqfUaHbDIcS8m5AQS7bUl6bPwpotrwxEUmb1hx5JIZSEnnVaGtxOTw+SvEtX3KMRpA9mJ8fH68K5vtrJok9ElW5qctvG7hZdjsXAOQAQI6OUMvQpNMuUeyn/Oga0D20b46qsbPv/iCT/8IYNpUwNAlSBZ1jE7buMndlL+PUvekNuvSupFhelFhVDPKzqlf8vnXE+9V5vgkiOBqHjUpF5qI0vSj74R3r17xtu3z3h+uuP6rU2FnwEG9fA1oKPhfr9Ffak/gISDmreLWkfvTVfd5FgFEGmSAtKM4Ab0psuUqcduSIa/RD829iFNRqNHfHT1qg0NkxpTsrap8KHEk5YFFF4OizFUULgJ5QQKycbEVo/gnl+iJiG9zNhHWq1ORVaPMKmSjjOKwpinLM+9CMEriSc48UQZY4RwTxelD1oN3M4sbKb16zyVfgyfK2Y8pVZr+Kve0VAuUk+jnWepmhY65DEXSPZXeHbg/4e9d+u1LbnOw75RNedca+3LufZhd4sURTJWYsWkKEVRJMUxkAR2ECRIkAc/5x/ECZCX3Jz8hbwGAQz4xXCgODEsIJAt6K4olqyYFCVRlBVRTZPdTfblXPfZe605q2rkYYxRNWqutY+kNmD1Q0+JfdZea86adRk1anzjGiNeXr/AvmR8772nePr0OUpZQHFEYFUCEOHwcsZ4vkGgBNAAUASHDKSA7RSBAlw9m7EcFlw/PSAn63WGpGfWg9NQFvtYJTcp3Vz2YCboZK5lsdNn3Zov0Ct+a1d1Qe2AoQgs1SV1JQhVl9UT7Zqo1lskPWBcj/UVl5d2j5lR6wOhr9G1bqO+jit9dl1y4zhlMWuKPj45k7TuFzdeLWTI+rQDbJ6n+osd/Z9UyZ++jt2s/Jhs363BpeunszDYu4gZpRZAOnaZO+pf3dhoZysafVvkkc2LeNw1XtnlHqD+KK8WDv86GwXXlrsz0M9FTYTlAGPjie4sW1m0uo6cANoGOgrBza/vs/HiJlATaVF5tx7Fj7dYnG6BBreKYkyVLyyv1YQ0qGWDekWxtX16n1mm6LX1187jeg7qfPl5O/rsPFMMnNRVd2ARQcIGSoDbf/J7LoxQnGtnPav7WFAbuz/HjNVaQkKUpoSo9B4KzKXarJlEBA4+btG8i3Qchl16Ax7EPV3OdG/vYobgHW7za8RMqgVhDi34jwh1tr14wIRSVOZgW6+jJaxfEvWJ8wxDgUnzIFCbi/ZqqIkDZoc1makWiAkWGgGlvybveK7soFrbsH7zok5EkwlkBeXfWuKrPctVZOoYdf3bx+8KLfSWR38vVt8fBzN8cn1cro8MFodhaAeuFUf2tAizSMF/Wf3tO7nXCFkpjKrrI0wkdcKMiK1jlI0ojIVxcRkxTAdc3TDmOYA4ApsNdkNCfrqXmrdjEmLkSbSwldO0A83YZ7CaULXz7qpMxm10/X69KQieIRgTOBaeXgk07WHXtO33Zp3yB+CpNsXKI+lk5dSTQwtImbBksYoNMSJTYyBLEitRDEFjLVusQSlZO6IMBeq6SABRQE5JP0cwZZuNyoi8/DAMk6w9BXCSUhakyR4snTwIGEgjH3KWeIo0i3DDSWlhQAxRS2lANccAc8H19XXHJ0vKmJdZY0ZEayfezQnLIodKCIRx2CCn0ukGmAtySpKcJ4g2mrK60gZSV2mj2xajEGIADYMkamKJRfALe4oMglO4iJa2L73iSYTX/Ltr22vRS01D3lx1w5GAFsnqlMmzGRIrF6dJngkiJKVSQNMgkKyoK3AR8elmljjPec5IzMiFsZ8TcmJJNBQitrtzbDcTfu0f/u9469vP8fWvfAvz8ysMJWIuAPICIkYYBlzeO8PCjHzIKGNEGSYACdvAKGlGCAH37m/BhwFjHLCkBFBEDJPWopCxBY4QBpXaVLmzqnRF2AW4VaUH5ESMZMIaudO5jz0yQBmorcWpy2uoDdC1+rRcXUrXYE9eGbo07ysZyl3BuaY23gG0NOxg006Te8pf6puw4km9lQoitLWCaNq2u6dpc3q+ZS/kJrRIE8c8k0yzbTQM1MyWvrZvdfV0ybHAcOURdDq6d5hwrT87t1GRu91vbkh+tmoLq7lp7Wi5Brd5q/tdfXs53tea4t+J/kdzs36fl3BNCPQF1auwyUBQCw8gSkI7Emw8NrVF2yQ3RpvrGsu00kyZfNBAgxNYlc9XmnDzy/JC+bkKq17h6AVflpIDIFWw6CnvzuxK4UTHZUf8+c5mca3NOyDSgJeTu0VmiVCgzd36V1Aa5Dy0sh12nndWw8qb7Vn7zdag8W/rxprH2G+ndQhiQgoUgMiacdVGqCEIKK4GcHOxrMpDRk2qQsFqMqtVsL5blL+inLW+WihGqHPHaG15IGxlEIuG0bCYI1cKFu2LyaP15fJ9TsmBxRZTLGy+la+R+9V66uCL8Xlm9Qhg12m9oZ7zyv9LzjV7fCXs2nb/jE1W0R+pMg/LR1CEziGimOX6AKxuNkARCM6LQ7eGzB2cHKHz0ssMXN3/rT3AKbLrwhWgeodx7Wc1HsP2qynlzMtEB8XtxrUC55Pr43v9CyS48brrFjBcXS2UTZsblTEGL3ADRjt65FlSHCdAyMHeECdRREFBoFnZ2QbTZsL91wY8ffE9vPP2Uzx5coOSA95//ASffuMurp7cIC0LpjEgRAaXBCALQ3eFVh3rb1YH+9Hx315Ad24immE0nNCadAcB98LRul2zoJr6i1wb3TMkX7a6NXRC4LFDl2B1rAKFGgtXWILIg6qm0rIgM4MQa4ZDwTul88OXw86SAAiDl4B/uGLODCjIMPcNkwVK5tqX3hOIm5tOFRoY5AFxIAREIErRYwDqbhcqvZSyiIsyRa3VmPQQKOpKKGuRtcYioFlQGUApmOe9CqNCHyklEBFSkuMjomDJCyiTS/YkB3dORbLW2eqF0IRafW/TLDst7Anaqut+C53437y2+jblg2mkW/yl0kW9nZBz6hJbMDMoGqvQWKtpwoEDKA4oOWGeZ5R5RuGMw/4aOS1I8wElJ3zw/jX2+z2ePH6Gm/0BNzczPnz8HId5wZIEAD589ACPXruHX/vl38Nb33qG/cuE87MRFAdRXiTGwAGRJqSyoKSEpTCQgU0aAAoodIPMEO01EShGPH15hadPXwBhRAgBeT4gWY1HziAUSOIbd63zwANICMhEkDQNJiy2PRHCUGvDrV3ken7g16Z3F2yWxFsshE5qFPbA7r7uDUf997+Ji6i9UwUR5vouc8KsvbB+nbiiE5a8A4YTxVevd9LJ+nsPznh1HztZnPo9UTMe21lTnHRdhWwTYBwfrW2qdYlLPavsGajwadY1S6rDzqTcWfTQ+Lxp1Ndzt7Zy+UyFzW1RzwdutQz9u9aXCNDsnvdT6+didTYYr4YTTrmfXwNmluxMRytnh7zA9IjwbpE+1snmssoI7urmx7oa2vrb2jWhE0eKAIuT6oRPo83a57VVq4GSrq0VeVa3SvTr6oaECpjRj7FZvt0Q/Xde+YA272z/x8d9arGQje7q+b+6TrneMpTWgU6TVfus7y8l13OdofG99X2NknziI2JROplrOEKzVntlhI2xoRY4WcayFPdylIR8iAxTKlj08qHNYUDN9qxJ3Zi5JrWq1nNu44WBY+1czrrfjG/YHFOzgtcsAWRTyYqH2IhC+mFyhfVN77V9xSsaCBYmWnkUN1BmfbUxFANlluzHdYnEql0sPZsDz57HNU5t6ieu82I8wfyH/F7tLP7tSLPlQlMIUZ3DU3KvKQQqiP3k+lheHxksliKWiVbugjoZS5hFBqurRiHbpCumbhvC2lGmAaCm6q4MhguIFmzKgHw4B20GbM9mlPkp/vb/+jv4ja99E++/f4UhMHY7wv/0P/xv+E//ox/FX//PfgSZGe89PoCXASUuiDwhmF8CoJ5oasWEdc310QO5E9fabdQON9sbVVNJ5lbQP2ube9WoCi/tz9Yl+RBI3TUUAAXLRsYtOUAT3IyZmG0hAjzg7t1L3H9wgXv3L7EbIjIssyMhJckSakLxdrOt/cwlydqqRW0ap5pJ87A/YF5mQF0RC2cVylSDV8TSeHl5ic0UQINktys5I8QJ6TCDBxFgxZNxQMkF+zkhLZJgpWb1DCTWRDBK1liUGJGLxNIR9PdSVCBUwAxgmAaQJmQxEB8CgYs6eDEg7stiJZFEcnpwzS0DJmCKDcYHL/ZIyNjsBpTMePjoHpSbO3Ly9CQEbiKWB3xETZNrh60l+zklGLYX2PcWc0PyGWJdMcAIoFpiJNlHEPfhKpCoOzEKMgNhswWNA/7O3/1p/KOf/RV89+0PkeaMw80B6RooGVgygRFQwohcGMOUwWhJcgiEIUiGu1ws+U0A5wGPXr/ExXnAxfYcKREmFOQSgM0GCxVcc0bkLYYh43xMIMoodI3AA6iMGENWdzDJenv98hrf/uZbePb8X8edO1tguwE2W6En1XYjlG5zmRXVhEAQYeQFY5mB/YySFqBkBFArqA1JFAWo5nq9j91VBfMqcrZ1i2ufpiz0YDIVIMZR0Vxb3U+gEPfAEseWvkDk/Gb1OSeAFEatByrPiojQsqty3xagCZp6QdU8RWJnYXP3nGChlknbFPBRFVre2kr1s0oVKqQ0JdYr2rds3CZ8qyAXLVUyAHBsYBLqzqZaK1EsOWWZltfx8iwzIztFgbdMeRqw7scQkXOuoIqZNZFWAwGAxED3ruztfX3sX3vvbVp6O09LKa0MSI2hboJ8BVlk/FC/LOyscOolw0B1w1GBUJwWXIZSajF9NREWtVcxhBapCH8WXYHOSynqdi2eDoTqy6RkQA3QOXmhUAZlzQQNabNmOzXhuaggHuWcWtKix25oSdVCqPQWbBQk8cshABERBtdM1WLW4DWw68Csel5YOSq7V8CX0EnlxaVZuEJVdrW2zT3dr/OtNACnrBhYqyWIAAAgAElEQVRUPtN5rkoLTczGAaDi3UG5i7EszBK3qHTSv4h1D6uFVzO2V8xi+1DQS1W2WZkqO/uYGWGIiJEQMco+y5KULOo5viyL0AOjnrMl69kfR5ixwgCL33MwUuwURW1uTVGE7KyASn/CUl3G3tqmev8E87cS40Q9z1Veq+tHAVnlLIYCTC+XaP9H9Yk2uS7RgqJeYQyuWbvtnhgBcBC+yv3ZslY49Jf1C5BwIRmiSG+6l/UZVloLYBQNEbPLstVWxdkJHtaRzCdw8WN7fWSwmAsjRs1YWQYAhDCIkCAEkNqBakCSmkhhBwiAuknJQFllqErI+lEsPQPyGJCuC7ax4OXVNb777vv4ja/+czx+do1hChgQEXjA4eYZ/u9//Lt48427+MxnHuLy7gb7/Q2mMB35ulMRocEYiW2WKsSbVkQPi16LKUwiavwWcTu0GmBsG+aoNhu1sXaCZZ2T/hb/YGM2Tdj1TK61z2pFyCIca5R+QQajSPkMy4XBLlsnEZjEdY0pYCnAOEbEEJDnIkARGYEChiFiHAbklLAEsfQFtfaZy2POEo9IEaoJF3cV4lDdQqcpSobWwkBUoZNZmVQWcSNEhGFAWfYITCAaQACWImUaCtv8AKQul+MwqlBqYose67moMCPvybnURDHiFsWS0AEMIt0yZYHlywxhqHNPTOBQEAMwhBFLtoMQsAwRFGQ/BANgELoWwJEQdb0t0UJbd7NcN2GqWkVWcrIJ7Cb9Nncd6g4JWWKlG5JZKZxBbIec/C4VFAsCRTBtkfcbXL8YMNIG46YglgFxZ/UUA5iitFcyaHCCnIghYJJkAqW0PgERMTI4E8zNLoPBQcfNBSOAISooo1jXsqhwL4IqAVRQQsImFPzu134Hf+t/eYlpGsGBQcOAYRwFFImEimGImIYJFAi78w3GacTl5TmmacB2M+H8fMQ4ET7z5mexmQIw+/3INSufOvf1i3Hqaqzk5FWFGBPA2LuxmoBDzponc2AO4bLf2+Fcwd2Kt9rfoVKS8Wa7x1sV7V7XT/1vpyh0txvPLHBquB4fdxNils4qsuoN1eWLXANhPaZVx+w8YZ0XVt4LgxnUzf9abJFzy/rUBFMTbv0AqlWA3NpX5UFzJbYfTRln9VzrHiZnvSKC7xW5Q8BbGo8xweoZDTeQr0sddw0Lp/VwuP5PrNd9/2MFijbHCvbZCXsKrmyu65mqnWIFEAW8nniZM6Aal9nFr9dExoXbWnKRuEcYkDKXziDxcG5sfn1sfVn7K+00PgUFkyaSVCFZ24uWDZVQ329j4urSz3UgBlZs3ShEBRdqJdN3w0qFka2P8ENPdm19+zPfPq+zcdq15v11qQH1onFQyDRJrs82eRVMwSnMjoAAdFxCby3h17pPqpyrmYjcvjDZEE1pXuWS2htIgqFC9RkdkLavnkTB+JGeQ8xVGVfA/doYuLcdYS4flV4bnXdKsOoW37wyANQ8ClAendU1NVQaaO9upeKM4lRugNI0DKDKOymqZR+oSeasc8JmVEFj+hz72ZRFoFZiyIW41ItUQY3myWIywfqsAKQ/tQfFzv7eGu0/tbH39PfJ9fG6PjJY3Iw7xOEA4gAupkGcAUQFByPELbCd2UA7wyszJWUMWlsOsO/qxxbDCCDnhBEF47ZgmCJiGLEbB3z43h7LoWA3BK3FtwBDwh/8wdv4tV/6Jn7ipyL+rb/8abw8XGGCxDhZ9lIuUqewZUGTdxWW2nLNrcD617aG1+B5wHZKk9iYkAdyPec8ciGl/hZy93msSjbXdPx+D2qJWAuLB81amjWZC2stSkbOerBpnTwOATlLhsdihwsRYghIrGHWRWYtRlaNVkSgoQFmNEYublxiDQ0AhmBAj1FSBibNzqZ+TlFTFDGL5o2i0laIJtKAoriSssafZT14ra7RSAHnF+dgzsiS8aTWB7y5PiCXlqAmRsIwjdLjJOAyg5FRcJiF5rebAcMwOA09wbJq0hBRwJJZjxiSUGlEoEnr1xGIorjBxIJSxGGbgiocisanaVKYVJJmcxUwJi44cK6tSgSOnnpFQSMeql8e02WjLYvpkjULNTFU0bMyIpYdkAMuzu4iEHCIBxBJjU7iUWF0wkABTCMKJ1VqSHxWyjMIETkHjeeQemiWfVDqaTFAYkEupYCYMYK0lAHVOJAQB4RIoExIOWmciMQnn03nePLeh/iVdz9A4YKcEwpn5JyxJFmv7Tg1gZ4IYRSt+jRGEBjTGJE5YNpe4L//m/81PvuvPMSOpZxHiLL3QL0l7WjuT10qA/qrsxSBKj+ybMwMA3buHmWqXfZWtHgjTw9ciqRf9/RhSgXA4bCe8RTPA72AVAGHWQbZvnZWSyG86jJb22lAQfrb7os2KBgNor6/tbPilTpmszr5Q4TaTRVQr7MYEtw0KI9rJRBcbzopVebO3DHN4mNwtL52BfzsDKhWIkLLGBpC1doXK81jZ5Wjj65OnZPEmFfvo35xq5KBj5a9oUcT9I0+CLrfuBfcXX+qMhT9RdawAq/i1t9cEUMHNK3vCpBgMJNh1SW4s2JJf9ZKjBgDKFKV25mBSC2TJQVUq4hrqR9AKG69uWa/rfKxAgd3i1j5bH65zbm/TywxejYrfy01Q6/ODfm1tX3gYgr9/lwrv0+Mi6l5o3TrBgBav7G7RNuk+6cRWI8JvazWZI1syd7qGA20eZdab8nrY7YrKRgoZYtBFkqoGUz1GsJQS3Gx8ieKSq82TQrMZF3knDPLOFWFhlzNu8bRRCmVR1nfZKtQ9zfIgGtbB45ucokwINaEe+pkpV4lwuGr67nOfZ0zdc+2hDOBqNEJADIrN7PWuTX2zi4LrNJfNFm0TXrJLvadudYxDTZXOk4iCQEqxrOgbr2rEIxiiY0sOVHpXW57Gujh5CfXx+v66NlQAdWMqdYHxsBNExIAjgBy3UCAMVmnuVFqJmpurOTvra2rILAsWHLCy/mAs4u7eHj/HK+//hB/4QuP8P9987ugZRbAExiMiMuLc7z++l1cXk5IhUE0etaOeoASuexv8lvNyqW3eQGc0I/B/NlPuX4Ys6muMgC6093uU6baCSTrdqyvJ0Hl8ffdeNStityciuAQqgARQlsyJmGaYCCRZTXVg5lYkxUBFitopQOE+/bjM1dVG7sxDUBKVTAVAWnDUO8htS4R2eFgB4UefmzIFe5f/UsZbbUeQlxZODNy4ioIDsOAQ1hqUXXT+I7jIAJvyJpUQTSk+3IAmBHihM1WrKhFy/SVIswwTgHIEcMgh5NZNUT2KWBkOXwp1PIwYHHpDTyokB1ApEl+uMViVi09ccvqBj8fOEkDgKc7gOh2xtzqOHqB05Q62gYxAkn5i6qkUBpnS0KAjKRBH6ybKJtlQ8X6nFkzDzJQgHEQ0I/U1rCCWVeEvGU2BsyNhwODk9BVXgqWlLHbBKmnGTJKIaTAdZ3AYuW2+GujyXkRoHnDBZwzYgx4fpUQxxfYH1gFgQwgaz88sAGOEOBt16tuq9I4apkfOfWPeZcJjh3PIFJjxGqNiWo4dlWWV6Zja0tHpOFLYRQCLMO0CbGvvto9a3n0aMzK/Kzf7b8OJDmBzX6x7hIM6HqwevweggFarn1qaSHQQJJ7r7f4NWvVavyOf7/KFbCujLW5OpW80mftalo9CkzIcv89shpVMlp5E2i/TdlQIQi1OwASF/XVXPPRjPsx9nzFK189wKyiQkfT/qlXX/UNBqYC1cyrlScGA27WY640YRkwad2ibefaDwktcGyw76nfN3ZDBbhc96E9F+Divlb82tNVBWh+mnW/VVpxr/R0cjv/F5oppaxWCT34LtwsTYEUJHWjhoEwb8WyIXk5pFmeIOfXEWBEJaGmHxLeTCSx/n4j2xhroh+0vWCqNYuZDe4Ms6aZUJMk+b4Z22vKHj9clSWOaKA9zGjyGfh2+rU2xNPKwklMDmrj6pfQra3+p3kDaUbk2re2nxuIX7+/gUiqjeq9QayUFbi5NCLteDBFl/INfTfn0pLxwbalAmCd2wJdXgPiRreV+f0pz85Prn/p10cGi/v9AZfTAEUOCIEwjFukbAKg1CwCWTkBPR6NQXsq1T19xOScoGi38TLj+sVz/OEfvYNHb76JR699EY/evMBP/tufw3c+fIz5gxkUBoyBcLUUfPb7H+Hf+6s/hIef2uH6sEegrWyuMIFI66Ep++5iQ0hiSoJqJitzVbDVGKD1zY0Nq7G4e+uzBNRTxY9X7+9n4pZ29Ze1IIAT95obAKBaLxZFG0WxckUtAREtro4krizEAaEw0pLABIyRMA4a15bt3og4REybEXEk5FmEbUsmyUANTLfYE3BRoY9U6VAQY8A0TTAgZHkqQiBM0yhW0KDWK9W+EotLLWetBUY2xuq9D9MaEghZwYCsd5BakLoWFXhAAQYBRd2aos7ffr+gFMI4jtjtdkiL+SMSlnlGmgs2ZxFxCdhMG4SQJW6gJDDPKDwi84yJNzLuQiB1YeTEIEjCnZpAqmiNSgQNZodJB2jR94DFVa3Xvj/4TECyf08LFYBZbSTWtLAeAkFSqwMFKc/IqSAMM4jF8jzuBvACsRpTAUJBTgMoAjEqsNZYjTiGpn2FAPeg+89fklGv8QEDkEHra1btrNX8IovpBA6HRWp0RYktCoEw0AjmEZuRsdvKnIyj7TCLqSmQeLYI5iL0PuxRMGG7iQhDBuUkcx+CSCDF3LJuF3U74Wy1Tkf3Nsm6k7+PwEDXegMgt721ljKBZnJdw0lqQvcrL/K2hnZ5a58oSYzfnQAFRMdDgCWscS61zKAju1EDLkbHK1tedcM9tkDa66VV61p1ee3e0ywnkuVRvq/gytbHLCB6c3Ml88Nte9KUK17qb7HIrZcCIE4oAd15uV6vBiKpCl9VrO1wre0/RwX1NQwgmMn0JBAxrFfXSIHRbVc7Nx34BTra8PcIrzMx3IR1AwL9eE7FALNa5xQZwKaDK3rzbv6Njtpk1QdaXWY/frOqHPEsC18gUCnOaUr+W2B0Y1YW3YfBt+3fYe9VimdYCH2j5QBnsl315QQdrpU2xI6/OhkAQItb9OtWFSdtH9he4CrnELxLrNG3B2SVr6PNByyhzkCgEiQxDRMoBOS0OFo1IG7zo3NUmetq/JpoplniTDoAzI16DWQtVpQAN489DRydWUZX7hlb76YucxZZI2bCis4bh+3prvEQUh5a40VNJKC1Ldv2mut/Ry5Nee8fqJ4PsHVBnWurRW1KVkvkw2iiCWxrm9gXWNaSYToBxxO4xaV/cn3srn+BBDcLrq5eqoZehLPHT6/w+ptvYHe2RaSMkvcIcVSmrazYErD4ZAHoBQ9zr6sWhQrIAu689mmEz3wOIezAIeD582vkssHDN3aYzkbsUxbmHAYMmw1+8C98AZ/77EOUUPDi5TV2mwVpIYAsnX5EHCfMy+whGaJmsCzFx0F5k327fD89qLTfuntNpep+v81K6Ns/uuxwJOqEIZWlQQq47Oam8QuVKQ9DwDCNuLne4+r5Hs+vDijbEcyS/ZPBoFSUGWR1UV1QSgJBXUW5gHPAnBc8zTc1V0QqrLEMsvmnOEitRrNUZgHh+/0N9vMBKAkxRgzDoAxYrbpckHJGzEGys3HCECZM44gYCTlNIBC2241YKPkGh3kWLlSMjkwwUo1YrYlFHdO1+QPUEqrxMdEy8wUtk1Ek0hORQDkgaoKFQ14wlwUX8TUAGbvzCcOcEKcRm+09DOPriNtzcJnAOUtJh8MBiAFhGjSIf4eFi2pTC3gouj+K1OsCIUJdo0tBVCac1R3uVTRTD9RKw0atKuDqPGS4epE1Y3BLPkJjxJ27Ozy4v8WzJ3MVAN5++z1sQ8TZ2SWGacIyZzx9/CHOz+7g4vICL69vBMAx49GnHgDq+gwIP1mWXIs/D3HAGEdQCHj65AlKKbi8cwdnZ+eY5xnPnj1HCALITZGQcpJMrrpPSxbIUZLG5JJl7oMAzXlBKQWbcQdAU6vbaUtQd+WCxAFAQtBECcgEzoPuLKnlGmMBp3Kr4uZ4/3aL5T6Guh7t7/pQFXKOeIxTVHXtHX3H7jNVIcgUJvK7xm5XgUr6Gr0vaB2GjeOEzbDeruBqNS/GwnzSAyt0XltTmrX3WOzi2ip4/Gp1w71VKaLtQTK6erfNCBOM5TvThgM+bpRaf6rs2oRY+/sYTKikDwgfJbPMt+QXhDYnRFTd3Qkk8c42F76EDnph3nXD7XdVm3FzF3VGs9o7EwiPyFgVWOYsV5MgKRgjhisFJK1Vb4CGwNop66crSBbxBnSFZmzc7OhQHlK+jaAKpdLm21gha+IPPRSpvvMWGjYQoeDbexKJrFy67zsgsJpDKO0h1pyZ9b5hJRuklFC41BJMCgkhrqrh6F1B3fL1iwY4g91TJ0nfIyvmawqv3YePysxo/7yFyTwKPb0bra55nmUrBUo3512GbeW1lc1oeEFOucpSbalargF/fJkF1DyDZCytD7L0GkluQNYAI4AWzxhkM5RGl9XyeAt+OQUU5QcoDa42Vw245bpIFmHFXETJyoxAkgOBXXOmSPV8QZps/I2ZUZJLUmS8Lzj1l5N3cvalTfyep9pFkydDEOWuxRITSVkQ6IpI1lSRL5lbH9itLwWRXZhLdSEOJOCRWTHBn+bs/OT6c7k+MlgcpwlEGWGQbFMliwVAmLNsbCJIsGulVWqbaC3omFBhwMuBxXofAEZCygW7+xMijbh39xLDZsSz954h3cwog7pDasrn58+vkLIkHEFhlIVB2EJqNMpuKJq4wzO8xjxNwD6h3cUx2Fv3t/tcAd7p++Rvf7C+Suh39wfP4I/75J5UgUNEIAHVhLRkzEvGMhekQTVIXDT5gCWlbwxG+sit2LCzcMntQYrGBntjVVCb7IcBUbyUybS7onCoyWacT1hBwmFfkEuuYG0+zDIODaBe5gUUpNYi2NJ8O5ctADktGheic0xULVMirwiQdEq66tKGEmq9oiooan3G0jH/QWk5a3azjHl/he9+94/x1h9v8Oi1T2M7Fty9fxeRIsI8gymDBtFsXl3tQZEQCRigoDEEQBNC1FThKoRlPXD9mbamPW/xaAAR2l8HADrp0g53stPBUREwRMLujPDkwwxmiVOeD4w33rjA5d0d5gwcnkIT99j+aVYt1VlIj/RQjiowEyTmlYJoL2Nsz8mBWZBywkRa61X7KMoJYWlhYGx3W8QxYD4cgMwYtc0lLUjpAC4QV2EUpJQwL4soIVRhgZJVKxq03YhAo34eYHVKQRIjG8iqU90iXdx2mUDm92wHJv3NdOK79Y0rCccrk+puWAu4t9mZV7RRQRUf39P1u6ez/l39FWCWRPrTzZxrtrBm5AaaVltverXY0eZAnvFQ60QvqAe13hVWfv+zCDnUfTytGEUTuE5YhrD6rvK0o/u1JTsftK8+Dr8mFqH2XptPAL0Fy/3PO8+eoilp0NOBCvsEKW+ifREWJgrlvtf9XBGg9/QKPvMFrVZdUnfTU2tCXkGyPnsdOKo97nfGn6gIcvNc7+QG/mQtTp/pmUtHY9wY5PHMVnmovbOLYa3/msXQK0QbwISjJRt/tYJVxY22WxUBfs0B0ozWp/rnQejx71z779mLxPed4FHMLdO0nsPSjPfocOtXFQrkxowGsK19ctPAng4aCZ1SAp723tF7tX9NEdF2TZtB+dC5Yfa/WvfAJVc1i+1hTxe3Atd1fz3rcXJON7K6d5x7q3sngJUHAdc5tFv6vcSrxtHaqD/zye36yfXxuT46WBxHDCMjYMBmGsBccO8mYwgiZIUYIIVYWbRSNTOVEGyIoR5SVR4D3CFwfIACQMoJoQBTucbTZwkLNnj4+gNc3r9AjAPmErChARERJS14cf0CL65v8ODhOe7fvwtOBft5RimDAEjT4IW+Xk/TNAf3t/utnq+uf/rPOjlE/U21K7692zbISQC6EgCrJhUO1Jxos1mUgrjvDCYo6OFVgHIoSAdG3hJKKjA1omk6Y5Ai8ynPig0N0BdkKmrNibK+TAgRiINmqWPAUmG3cg3SN+YMEKMU0WYtplV0zNoSFIQQVMuekTlVEMpEAggUOIRA2EStr2jnBzOuXlxBwKmmNSdCygtK0sxkkFgsLgXLPMs8Bpm/gSWZT9FaZiVl7F/ewGIZpbMBgQrSzACkVmNOjMfP38Mv/sLP4bf+6T/Gz/+jB7h3b4M3v+/7sTu/wPd/7k08eG2Hz37uszi8LPg//+4v4Is/+mV87vOfxufe3AH7K3CxdO4MiRUU+krOQqCzid4S1da/hX44gdfotAMT0MACpad6Kgj9QNdmXhYs+6XVFswFwA7bzTmGIWBJCZwDKEyIQ4Amx+0xB7imCpd42dhZlGzvh6ixz1CBgcTWkHMWN9kSJa5GtbJEwNnZiN3ZDuM04uXVFXLKwNkO23FEyBkMSXRT9JRbSpJMvJtJXKAzg4ZBBBFNvU8EcVuOmoCEGIgag1MY0FqUzMfCExjdgUyaHa9ZzNS1zoTZnqXIvNcvVi6gZuGBWyv3sacFH4TSBBkCa/IEL5ybCLZmLPa9CQmOfqzEQgcvVIDoOtOD4aD9L1w6/lnvcv21eTIFVM1+U61C1M9NB8L9B+oUQ6cv954TVy0rYveo4Fe9PVi9A/l4QSxTuNUvtGGuhfG194PdsQYHdmYVy67ixrDCp/1nmz79GCDKVlIhvib18IATQCQpCACjEOqtkVWxZkvHbs6ofUHad1r1mZyAXk9jQltPE5ZJ5rK6Obq2iFpNUetaI3sDUk6AddOlUKvNey2xshLmV+vieXIFJQz3nqJHkgNp0LO2A2ps/1+vkrMkPNL5XrsHd22WNj9w02ZfePrx7s/yT8vIugZJvKJPUsDvIW3QUla+Unun/FAgwQCYNCaSzLImYRgpNauklPbgmrugAhEDxBxgJVRsLBGDyiNBlYy50kwFkdWfWWiSSWIl11lBS2GEAf042ZK6uLnhlruio1x286xr4bZE47K2N6g975PdNKTsLI8sMotD0d11ZHBRQ0p9HSrb6hW4cG6o7NyHub3KZOHblC9gl1uh7gHnburYM/tz65PrY3d9ZLAoAuigm1QyK+02G8QoQm1AQGJIdqQYGp2jMWnR+ADwh8It6KltLnEFTPuEd995H1dv7/EDVwfcuXcH0ziCszC4EE3UWfDtt9/HkhLu3ruDOBISH5CWdrC1A6MAcCmv1/2h1eGxmo9WeRXHQhrQMrn1chJk49++yVc31/lo37Ud1x1cdQNT7bv4WaAKWMzQepkS2w8AVoNQXJ60PArZYYbaZrXOcSdLiFtKYYkNcUyinrWAHmSSJCWQxYpx+9cNt4KG0NBGsENM62Q1JixlOgwgNWZfpIYWWF1Q9HAosjBN8yqMN6Xi5pvAISqD1aQQqWDGoVrmSNsrpUjMZmQsKaMUYJ4XPH3+HN9973189523cOcs4rU738R4vsO/9uUfxOufuY+bG2D/kvCrv/p7oM3riLv7ePTaJTY86PoUcC0e34Txzr3Lrf1xbTylzxNa7eOrp/li7VFAACGDUJaMeZ9BNKigMyPEATkD816s1MzqKmgJZFgsrU06ZQdF+pgkL2zJWgYV2+XvwdxySkZOqdaoGuKAEKQ2JGkyJtMKEySZ0jhJiYzrm2vN2hcBVkUHQj04h9AE5YKMSAUUGRRUmVL9skIn/J5KWl8TetjgKiAy1yLW+omrGfBgZ80P2P1j7Ke7hbp/joDTihfVe52wepuymruX+udP8EZbWyf4tzbsa7r1XUf9W0m/FVS57zph5uh5oFqj/LNOCO5jffTvlUuwdfhEqFj/Pjf29pWLRev6aHz8RFNrYLPi9bW3ts/1bPDg+dYpXi8K2rzamcUdAbuxdX1z4zQh0m5cD5PX8LAHHH4sLWawvaKs+0saq6702x2R3TvW5zqtibHd64VgPnVKt3fbtXbxXN/DNhU6P8Y5aLXP5T5PmdpmKS0mzYFDc7sEteRN5sraQJQBKjdnbO6+7T3CaRvQbfe6fugPkqHVgzcZHAE1wyrbN+Z/6PdxCOoVoG2QO/N1fxCbB1gAgoVkWFIqB/ptLDaHRDU2Xko2lQZc2spoH9u7j+WpUsFPaUNEYM1c7hbJ+tWfbRWF6euanNnWnirPouqe2c4V62WlGfY7G3VPndrl5Ppo60+u26xdO7ZKN17qrZcGMvskarczcFkfLRGzmhPPuz5Bix/f6yODxTgEhJAxHw5YloIQgSePb/Do9bsYhwEUCkIk5ApBFCBqTZ7qGgGAuMihoPEPQU0Qza0Eja8ACOMOu0+/ge/89nv4ez/9a7iz2+JHvvgZvHh6je20AJRwKBEDM95669v4m//d38aD+3fw7/+HP4bP/6uv47OvP8SjR2eSkCSJr/swjrDMriLAicVjHEcFI23sJtw0QKnfHVkm0f/N7FId23OWxqHXEvaaZNnOnbURgLlZNAHDuzFRnbMWOF8QSVPnc5D/ZcLZhvGpuyMe3xtAU0DAiP2yiAsiCR5bloRlmcUibAeYvjqGja6h9rswcp6RElW5uGSGpE/WxPYKvC4vttjttmIlUi7cUj5T1ZqFGJHSgiXNkngoRJkzSa1VLQS5SF2tlHNNDGHzLJm0iwS6k1gKiQWsUrDalxIvWaxYvAqxHDKIgGkcACaUsoCXBURBraIkyYJAuN4/BcIA3AgdbTcRr33mEhQHXJ5f4GI34RwJz19c4yu/8lvY7xf8ffyMFOSOO/zmz/8DfOO3fwlf+/JfxGc+/Rl86UtfwP27O9zZTghcxPrCBUOMgPbTDiyL0fIHuV1y0JqF0R+IlaLkPi1CbfQYiZDVIlvkBkzbEXfuTnjxMqGkjJRm0Jhw78EG48Dg5zfgFwsoFC19wUBBLZ2RixylzV24WeNEMQDlCYzz83OQuimnJQNIODs70+8YNzcSC1lKEYUQEbKWPIkRmMYJmCRetj2lRWAAACAASURBVJSkcxG1PmaomvoYJfPhNA4N2LJYa2McEIIAdxRGqFZELVMcpFxHqGN69WWCDREhrkBAd19Yuya635oUJ3vFYxkiTe4S6t0C+nt3ShPjQGQhutbBuhZdn6HgqPIU+TcCjn8CBqQNZDRvDB0F91YoG0Mo677Zz5rIwQRSagKQd/Wi1d8d9HDzbAqQ9h13KP9ICAOhKVpkzqyNCMBAM9f7+7GtvpBb8kqtwGi17ryQSP3ZIG9fgxDjXXqfWx+RIUs3N/Z08/Do15sIkowC0IQw2hL302D1cyXfWHN5b/TcxmG0mBX4sVmRpQPCqyveVFHbu092CZnM9XA1syHUuFdvyfGKCknw2UOFKq7W+ohy1cyhROIC66Ts/jw2uherSVlZpro+qgQuVlDnSohW09M8KNjmRp8Lw1BjBMVVvtFQ1Fp4TaFrMoH0sAOxSmPSRKkAxN4lOlTnvnk0ClRr1jopCbvxt/dB9mMXN9n/7p8oKg90rVh5D2YgW5gMKp14MMM6xupSjSolGSbTysG2X4ICUnnY5FRrxZKdASJH+LkMoVndDNbVjKK6lQurEiOs6IHU5aYCON0HVWtIaMBSxs7afo1fRJMjS9D14Ar16yjamd5qOXrA1il7dFOYFfeUMcdieO3g6dyb3boCUsJM8ptA94/1sY6g3v3J9fG8PnrpDBahmgi4uTlgf7jB2995gss7Zzg/3+HsfMA4Rjx5voAhWnMhCQdilIkRBRR1aBG3HA+UvFAhW5y54M3v/z688f1vY3MRcHW9x2/87h/jZr7BDqL5SUiIFLHMBdMU8PL6JX72Z/9fTL+8w099+Qv4q3/tx/BDX/wBvHz5Ao8fP0YIhBgGmBpFy72h5IQaJ2Y9MqEEfgM1wcMY9Fr+6w4XPXxP3XsaLPZMxuuL1xt5rRnz7QQQMkGfDggI2E0D7t+7wKMHd3BgwrwABUXrVGlCFwXV0zjWNrkULGUBFwJRxDhKuYi0ZJR58YsMIo1F1FVmNjengCFGEEltppyL1tEDhhhU40Wa+TKDSsEwjBgHjTFVt1Ap8VNws7/BkjLGYdCDMMG0dWMkFShVmNAYREm1LTpKyWXD1ZIGBOSiVlHSmo/mFhsHWUNoTKe5FuYFzKMe/ATOwDIXgGfkMSGe7fDw4TkuLm9wJx0ARKQDY1ky5lQwP3+Gd558iO+89Ta2F2f445/4S/jxf/OH8RM/+SVQBHjZAzFKIhp1o4ncu0tXwQ4dYYnAcURPPW2RJa2o88JemgIAzIeMm6uswF6SJYVIePlij3GYkOaAYYyYsJF5KIzd7gzjVFByUXcqSCZbU4YACGOUQyrn1rcqQLX9EaImymIgDAPCkoUfaeG/wzyDS8bF+QXOdttqFWY9tFPK1d2JQsF2O2LcxJo8ggBDRZo9ONT7OWQB6TVhQQNk7b+rmV2vhVftrkGOXoVZa29JSybgNXumaaDbvLTmuZWyUYWHWdoNnJbaB7XC196Wvn/aXyLhHYBzezfk4M1r9Z0uCQf1PL2jKf8dOWUU97NZBWQyV7ljQdx/1+Sr4zbqHrBhcBtC5J6/6ozIWpgAiOPkOVwBOfU7arWutqJF+xoUeIIMzANd5hnltdZ/GXvLtmpniLnIr8dqYLijy65GY7/WQf/TSuAYgdm51dKN2JQ3jtLT4Ppcs5ky0NsWSRsyQVLnrTsC9TuuZ6c8Q6VovTcCViRB1NeC610urfPOwuxkZQ/KeNWmH6N075jP+ns7MFa/a0CUSGrEGjHa91jRLtxYuu+tT6WtTFW2V2CgfQqEUDxAcHKCL6uhvIFRFBBSG6ttbQPtFXTIt924jQ5Wck6lS260wAwBFa2xNrMGQq09p7Uo9T3spkLPFef90PeTqpKVLOymrHgft/kUi6We6V654mi2t+G/2hPgqA12e55RFRcd+CZqyg6fwdbeYT1xtOJ5Jq/ev7Yy2z1c2nfra31OeevzWllUgab9Dr9Fyb376DWfXB+z66ODxRmIlwPCSDi/eIBAIx7ee4Dd+QZzvsHvfP0DPHn6Ej/+Ez+MeTlgOYhWBmEBqGAYI2IcdUMwBopAYURMYEpgSIrkXDI28VxF8oDtBrg4z/jZn/tl/NZvfAucEi4uCPP1ASEX5HABUELAjMIZGxpqTMhyc4391Uv8zP/1Hn7+l7+G//a/+uv44o98Hl/60g/h97/+R8icMU4WlxUx0ABkVydSfclzknqDYxCf+MwHjHGLFGzTeGZt1rXGaG0sgDtQiTRusjHZUAUCsb6YFyap1ZNVKDsKIicAZK4YXN3xiALKEiV7Z1jAMSFrofcHm4IHG8a7+4KSF0yj1rGrYCsCPGIctthspKTIfr/Hcp0QiTEMA8YhqIte0jlUUYNINXOaoVXnpRSu2XQDFWRmlJwRh6H1GwBzwDgMWOYE5oCkNRvHKEoGzuJ+OAwDxrjBcrgBDyYI6RoQcPfOfYAL0jKjgLEFUIjx7OpKVHKQLGRgY/kFRQr+YUji+pNZM+jGAeXQ4uhAkvQloCDQVg8cY5yExAIplyIxs4eckaeAyxCxmSLi/Q0WBuZ8DfAZCBHLYcHN/oB3v/FNvPxLPwqaIgIvKDOBCmHAhBy5E1LhDw9qBxGr305LNiM3dIKQHTCcq+CWAiPmhIgBGQNAIxKAq6fXeP+DGUyMOA4YeYvvu38HL25mpHKFGAJ22y12OwYvBEbSkjYRPGgWtLofZP4oFBAGMFjL72St70cIFBEHKZkSg6wBUUBKC4gXxFBAuk9yyXh5c4OXVwnndy5BzJLhkoGcGcRRrJ04AEgARoRomdq4CnEF6qIdZO9nFMQwIGJEIcmcGoJmSwaDtPAyrzLqVUzFjg8QAHU18oKZXwqx1jVhv/IUXd9CWtg7mIsP6j3kLIpN5GJkAwfGTOxvA6OVfzSpWWpJemFUE3FwsyjaZdp0SZ5l83HsmGvCoXfzquNDnbyjdovuq6B8NRfhxdnS88O32QtKICeImnDciL/FL9resJ4xS8urDN7deHRAmke2vdtdob0aQCvCLcsrFFfYJZ0i8wJwiUJMMOU2v5KQzAn8buxV59WdNyvAsQK35lVBgTCiCcc+0Y0pHNauZOYCGHUvtPiyNtVU0BQVtidicyMEkeP99p3MeVHlkreMFBJ3xVx8FmcHlrqFQpdR1sbP4Aq2q5ufn3edlwZGyG2flTWFjXt0r2g0aCBxiIgg5JwRmLEZJBN5KbnuRVKLVgNCVGmcqdHoGtDWsxbQrNBGH7bd7XPjLcwMjlRLsdham94iKO0FBJTA4KLhI2Q8pD1Tcu77HEz+aSISr5SA4m4McCi1P/JvYwVsrrarsXreyax7KxTl+eUIFAsAy/XstvADlAKEgLIs9X7AFHeEnFm8l5h1nhpQMtfTOtd65pRSwCXrWawlsZSfccmVD4sltc56tSwbUGzjJBCVNqcG+qD9r/TfEsYJfZeatRQI2n6BZFpfeXNx4z1S37nRYJ1H2xu50d6pkkHeLdiuml268noHcj+5PpbXRy+dQQXzHhjGgO2diIEiDtcjLu+cYZ9nvPvec3z1q+/gL//4v4G7FxPmswNyITAiirmzIQrz0NSXhcRtr+QMCozdZkKMO9y83EOIO2AuEd/8znv4tV99C2996ymAgpKF+CMRAi3V5D8OI4QZGSMjBGKMY8SyLPjZX/oKvv3BE3zpyz+Iz//APbVKSUmNwFJQ3TSgjFBBRIySSTTDXBQ28lmD97oMk1ARoh7y60DgJlyQCpCwp0lcH60YPVFUAcLuD0AtCl6bw7EogyqQSE0f6V8gY0iExMBcGLmIls606+Yyy8pWQgxasw51TGaJEbDL9f0KVRtD0LWudlkTCoK6qUIEIAuItpTfTUNKKjz7cRmj1INJM2i66sYKknRuneAxDDKfUeunmZY0xojNOIgwbxp9ZDAFzEkE5ZKzTaouK1d6SCosCOAWYUvcoxhcMg7zjKdXAxICNilhHBOmbQBFqXVJIAwhgEbJMvx9b97Haw8uxBrCAlAQ2Gn+paapF5K4nyRPHN3nNRNnkCRqKQASA5sBhRiRBgStn2ruv+OGMS8ZJRfMKSMvezAyQiQAEfOScHOzx2acsNlEHOYDcpK9eXl5prQsh1V0lg4RNkWQQgjYv3gJUMBZ2CEOAblkzIeEQddomjaIcRS9jg5HRVUdU9siQWt2BSIgy9cUqLlrOjAWo1PeQIQFib80YV7W3wT1Btb8jHoQ5sGgWdn/5Mtb1Pzl91V1PSZzU7T1bHe4Bpt1y7dQhcfTF3NN99XqIDqB9fg9OOZ3XpDvBHb3ofI3f4vyACko53sF42XrTJJHcsftQ8O6O/5atw060RS3OakclsyK2Y/x1G70tcfLqY6szoxTgzllBVhTgoqaXbtHa67gMawG6ltfAyTfh9P9WN9zYr5PggB09Hxbe3/S1fZQ+3wkwJJP6GFr2PrZFBmmbPMr6XaaKtpWg2l3U7v/aGz6Uh8KLJ5Xa/BJXZs2gLra6711zD4gZ+tqfArYesO2G48DbtXV2WUut/EYaDCg4Oe/3sRAP3fmMty7PzbFm58/PwqWJHPUf+fnam3t8t9382i0v1LAk8odVRlWp7a9d8152d5lIE9lMPaDcZ/rLJjiRWUamcN+Te3sx+qZ9XW8t/077X9teS3Du50RvmenLOr2MOHVPMH+PaJL0Rqc7Psn18fr+shg8VMPdhi3ARyAr3792/iDb7yN733vBq89OgMNhN/+nXfwe19/D//gZ76Cz37+IR58ijCMEZfn9zCOI8ZRapONQ8RmuwUAUIxgSKH0UhLe+96HePak4Id/7HO4uTpgSQmbsxE3+SV+52sfYH9zg20UsFhYXBmRM4TpuPqDLBoVCoQ4EAYKKLng//mnX8dXf/8tPPi5r+C//C/+E7z55l0EBpb5gM12J/s7iuuR+KBLsHRBru2BCCUQgIyI5ue+PpSFCVJlLk6ErAyy2zKBJReNaeVYmAdRAILEfXHVUqEJWNa+uXMRYO4KEpvHyoTauzMGPFsYTw8FKUGyhOoNkizGsmex+OejT5wiZ4x837m+QaxGFQzDGK5ZWyWGcVkOSLkoo6IaT2JlOoIydHPvGmLAOA4YYwSWggIt0q59pVDArBpxnaB6+AZo9jUGRcm+OZK411mikYEI59tRD0PRNCJnIES8vNljWRJQWMvHmAsMKtPb72cBzUEA3LIkUGLESTSCJSWkJSIzkDjgkBLy/gpDHHB3G7GkBYUTEpLUYouEzVaSSQERJY4AFQSta0TaTX9Vxu6Fc5jGkJ0Fxh1wOr95yAhpALFalAIhI0EsxgHADjQAu7OI5XlCLuJCe319wDgM2GxFgZEWxvWLhHgZMW1GLOmAwyGBmHHnzrnJXfUQljVUOBIJIQwYxxFPHz8DM7DbbhFjxGFZ8Oz5c5xfnGE3TdjudmAG0lJkbThjsxmFlnLGzc0eAQHjOCIEYF4EtBYuGIaIUoDDfo9cEs52Z6JEYEbQbKg5i4u8WJRjLTtgYK1Aa/OpUHAslDmByANGrIUatxp1afz6KQn7F7B7uDvMV13QeY1ogLy2pcJIEzYZoFCtcNKWb1DBte75QkBQV9xCjRANTFocVyc4rSXxWo+vf88RRoJq68GImszKmluDVwPxx8DOucnVho+laS9XBaKViapdZu30HLwKhLZ+OBbcLOrO+GUwxR+3JBrGT+uzOvb1xJCbz869Dep+SHUpYFQm3I6P5kjCE0xYZDWoeqFdFSReUK79abGqp4RMOY6otuP/7dPONitfR/snMsA2qnXCM9p8WZPe6mFAwQuwogRtFsv1epnFvndlbH3trnXdQgfkGjhogrrQh1rHbS2BWnsuSJ7u+j77X6eH0cYcdmt9WAGpRvfoYhNJZQpWOrSaegSWMGTdslU5pR0mkISErMbv40f9ejK3eNeiXhGWoROAFnj3o5A9xqpM9UsjiWsgFmvdS9Y/kZ96mvE0YBY5cWt281Cfkb4JXZgrv+0fA0kyXwauGgRT8gvGH6klYKoDdXuDxKqb0dbeRLsmU0EBZ1vFY35mX+sO7/iBB3W0egL1ezuDHIkfg+7CKiO1fvmxmMzm+1VlDl1flUbrGrxKWfnJ9ed7fXTL4pwRLwuuloKvff0d/Pqv/yEeP77B2fmIcRyw30uc16/8kz/AZ99/iE9/dofdZsJr965xttvg4kzc73bbEXfvX4BCwBgjQhgx3ZmQS8E777yPf/5H1/jJv/JF5LkgpYwhBNy7d4ntEJAjpDRDJgEABDAHEJVae9DSk0vJAdQ6a1yA3dkWh5s9/uB7T/H733gb9+6d4/5lVBcEYdKZxLktQEBojkON7xEmRWBIwXrLOrbegPXq5feOoVelpf5AQBXWYxBmWKyAbKfpO34X2f/V9lRbpkIBqdpQylowUil4MTNe7BMYUdwtFag1YCiMISsYD0TiDorGGHJKkjenuENQwVpXM75Kw+KiMM+LpKDWO3LS39RTLqqbnQllFNWKqYeUMPAWw2YHXXdg2vw6hleZVFAhqrryKejUPocgSgIE0mQvAMWAzWZU+awoYJQDZlkKiDLiIJFR5VB0XgScDoEwaj+GEFFKQslZD4kRKRcccsIhLyAkvPu9Z3jx4qDCoMT0icOKWt8AMNJJbXk3306A8wXNya2HMP4FJRKiWnblvRkMy2Q61Hkyy7ZZG8XXroAVUMQ4NM2igkGCCfFyRAR3AHk3mG48ftMQIWn2UxN6DIewAp9pHBHCAC6M5XAA0Siuq4GwLAuWOWGIA2gUxdHhcEBKCzbTBuM4aKr20vYzmcLFHbB13nqgcPpqh6Zv4vRdNre4ZX/3f5gwsRKtTj8j0nr/vWc+fnhkgOZEPzshwIOX1kAhtWDqnuzyLTpalL9VYF+DY6/V6gaNniZw+/hp/fkk/yR05pwT8tytCnBjFLU9GacfrwHZro8mdJHzxFiRUtPKG+iAZrpcd+42IUv64M8Cm7t1YpITj9VnyL1KX9jxmmYFIn9La2v1ZwXyr+RZvWDv2zi5LzwwqUCIT+619s7Tc3BkBekgwJ/uutUS011NmdqeA4B2RhmQsT1jCtA1LDHLZ0Df0zV3kqWtO1f352rMeqZ7TiFygx2Wbm3slkDt7Le3aDt+TrzVXxQYBuzarRYT3QFzcl5bpZ9TY2snl7vjaf2arOmrvsvNjXXUV5GyO9zHFbmTjkNW0GpVFk+jK/BMds7UtbF952Q5nYeexqEKTJ3zSha30Fx3trpngNqXVzzd6ITsHO/X2N3Y9no97f2s6TdGz3+27fXJ9S/5+shg8ed//Rs4/9SIDx7f4CtfewdPnmZQJNzsE25uMlAituOAD549x/UfHvDWtyVOaDvtBBTGgmkkbHYRDx7cwRhHbKaMcdzg4Wt3EALjD//wXXzznz3Hj/7EF3B9/RLpcMC02eDl9R7LMgOFEMKkFJjAyKAhKWCgfpNBY5AgQmDOjE2RYtoRkoBk2k147fU7uL66wdX1guubPYa4Q+EEBjBMERdnEy7uXKBkxvWLBcuckSGFySVJj7mqsW5K7UeQeKsuCYHj4p0V0gDLEAUgxYgYCMRZs4lp/cJOrdiErPo/uPaVExdmKQcXJBtkpID9fMCHz27wwfM9LjYTaCAcDrNYToJwnlKkzf1+L2UcUJASI6WCEDfIXMRqBIk14wwMcQBIco6Zm585dcIxzbwsKBTrMKrrimacKHKiIOcicSmZkRep4yegVjR22Q49y5RqABAAQ6yYBMv2qXA/BFAcwJy0rhNhDAFhHLWmU67rhwpkuMayyTDMpixZP4cYxFVV3ZJjlOQ/0ZImMXC9JFzPB5ztxC17GCN204S7lztstgU3+xnP9gmpBHzn3Wd4/ORK3Nx0PY6FaQdGsBKqnCxPRA4kGv2EBnwIoEJVORI5g8IGmUYgQypGcEZJjMM+o2a1DLK6AuCovlqaD07YkR/YCQLmbgyg1ZSCWERSSihZlCSWettie7kITSxLkniPJBrhQIxhjBgnUbSEoGUxQkCMEk+VqQFMCpLIiXKGCWmlFGSNu7FSLHY4drItufk7WWwbdV1qEhMAx3f2D9VEG52EfdsDx2C19dEJ2SbQw93OJ54mgiUVevUB7lCUfgwINUuhxPySghtSpU/zSgiVDzAKhJZqVl8+NUdUkUZNqV/7yy2mcKVpb8LbyptjfXXZTvvlfNV69Y6xdLxWzDV5jo+pLA4Z9jRRD4W2n7mNwf7tazw26Vvu7wX0egsXVCuou5e65qkKlIC4/pIDCAZmvHBv51dnvVAA7oVh/0wPoLwFdQ2eji0Ot7meeiHc+lWBUTcnx33w7nLwALu23e5buzWeBoM2oSuwBAUGBqluGUsD38YnzaKLRvduzOtnTwEhALBwC/1DSZYcHegircE8r9x0WeWZmq5ePltZrG7Sap8qeTXORB6UyrydAtrdd+7vbk+vlUvcaGDtDtm5ddbPjr6hsocu4al+rPfaWulVKpA65tG1LfUCszlrSn1vgT1+R8WH0pDvkayp1TFXBlJHSBaf6FQG1PfOd3ktaqznQAwrTi5xQ22u5M299dS50tboE8T4cb0+Mlj8W//HbyFOYimhlDCGhHFgFArIpWCZJSBozAHL84z0HAAYz/DYEWEEOGDJ3xF5I5AcqnESmioLmAn/zf/4d2DWolACqDD2816tbVlj70agRCBIAXNL/59Lae5tLOCmFCDQgGV/QOCIy90Zfvrv/QJ+67e/jv/g3/0yPv+ZN/CjX/48Lj8/4GUm5CRWkqsXj/Gdt76Ov/8//yYePnodf+0//nfwxpsP8fjdl9hOG5QhqhWqVFfKqC6jxjNE0DUXB5mT9VlhAv08v8A4RszXB6SUME5nuLhzB0mzhorgKTFYdkn7gNeS10OHgRDF/TdnBgohMiEVxtW84HopONtAgDhLYGLJi25iyW7LJVVXDxAQBnX/hQmiMldS53IANEG1XWZhYBUOSC04OSUgaKKCnHsBxwXHT8OIojFrEitpgrwyWxJXYyoKMKDZNTnj5YsXwjRN8KKAOEjQOjPV8hkZBfvDjYwjSOZNAEApSMp0S2HsD4cmbJFkbBWBqCAoQwYRIkUAEWlJ2N09w6dee4DzzTW+8a138d41gxAx0IhnYcb1xR7QfTDEEbuwwXcP15iXBWxa3XCQhBAYa3bKoaub0IS3XoYhx/zXTFt+kDkcgTAAYUQ5HIRuxog8EHLMSLRHyQOGMGAON+CSkTkhZ6mBGmiLgIjEM3K5AeexgrolJQwUkJcEsMQmAowlL8hLrhk7Q9CsxgzcvXcPIQQJpSwZAwXcubjEECU76cvrl7i52QMccPfuPYzbAcMg9C77sFkrjQpzZkybiGkYxTWZZc8W3bussbvmciTCORBiBEhApSTWaImFyKT6EwDeSfzitkr+B7j7+lObu799JlRUoa4mRTgtc7rlFy+JU2KLgUm5LfhOV0GiHfaObLj7C0QkLrlg62XrcFA7uJYkKUQtw6garb2LLcO3rX3jlaDihSQFG+sEJm0FTWHonvUCst3fv7K711++py1+lOvcBccnG4AU+7zwYihvcuuq/Qjo+18zp1oQufu9i6k0QZfgPDxI3QJZYp31RjsmmiOn67+CPQu1ALeMjM0NzU0CHYMom/kGeNocNR7kp7cBO9/WEdhZKQP8taZrIuoyd6ICJR8IYvHwOPrOwHGredfoz4Ofrn9o1kB5pCnDWr9DpY+uFm4FtjZPxsfteaCWE/HWHba5brOQs4z3GMj6/dqtSre/q/useoNY3GS1Kul5TY4ImLlqT9jAo9uzUmait7Cbu3OTi7irGUlEmiG9eZxIdSLbCwSrSd+SPvUx1R4gNrfjPhlLncMAcNF4Y+VPDRQ7a6S9R895cvTLlQ5a2yaX2viMHkQ0cwC5onFZjsLc07nxLep54RHNGPjUOFQDjIFISgu7MzHqfFruiAoeXZK+NZ9fz2elPX9+OV5lXSWmLga3Us8xi/3k+hhdHxks5uUAVuFwClLsWjaOLH0JRTLUWQreYIeyxkFpNkFm1uxoUsC8gMEZIAQMgTGOAVdPrxv4YUltPm4CMnyq8oIQza9fNzUJcMlZslGFKPX5MosFgllSVY/jgOcvXuCtbxb8+maDb73xHp4/f45Hr19gOptAw4jXXruH58+f44/eeg9f/do7eO21GT/1V2bsvrDDsN0hQ94hyTNQrRFEjgmRZyC9hcJ/No170LGVNOPm6iWWKeH+g/sy/1pP0OvKydrp2m1WRmEipRaptTp2FCPiKFYXExrr4cn9Rhfm2w4HOmIMVdYAkOthVNPoK5OxBN2DJg2JTtiwebOU55XJ1P844Ta0d5v2kCDZVaV/YvMrNhZWYYjUGrno2kBcQkFCasshVbdlyzomh1LWWkystfwstTYBueh7AUuO06JXZSKGYcB2s8HZLiNA4zIN0IaMlzcM8T4mDMQYCiEME5acRSS1eqR1/k2YcJNT11oZcJ09J7Cp0GFxZ7pj9LeAzAUl7REpatxuqm5rDLHIJytKBRG9AoUuZhUELWIv75RYQN0bYLT0/+pSs1JwQOl7GIeqXeWSEShgO25AUfvPxR2+4q59mDNQCOMYMU0TGAHDEEHEGOKAcdKMipA9No4jzCtAOim1rKqiRfeVKQDa5TZc/Z77n/XyQr0JMMKncHR/dUbqM13U07SCniqI/xku6unArH92nRbAb3mP0VHty/qZ9VMiXBnA9darosC0f4hWT/OJb9f39lYv65sJd5VJVtn4dGvde18pxXjXsePmquLGdpoHB53fHPVg1uZDf7eQvuqd0b9EyFbHVTyIoZZkzEAnVmSFKtPpH9RCF9a5bL0wugYztWcnvvfPVwulx5BVzuz52JFFaT3sfqFhAnPzAhE+o7YiWJyaH4s9ysWtTdfn/t5Tls763WrIdZ+v2II5H/eumR6gHrffWz/du06QmP3BMQAAIABJREFU8PEeWNNie/54G5DKSMeO6J1ljdBlf7ZnDTDrUSuv8nLQLXRh960B+G3vP2YTLLH9/nnuPx4rH9r6mgLa4ij1B2k3oAJdA8z+5VTpDo3eSN+6Wstjy3hrz9jzsdVb2zT55dScdaDOKV/a5loNvA3h6BjyvHhFB2xdtvOQUQFpfbDjCboH7F3U2vDXbR4Dn1x//tdHBosXuwljYJTMKDkANKBwBhYhjDEExLAgLZJ211IVjsMWQ5zA4UYLZDOmcZJYo+KZS5GU+cy4nAJCEC1/YSByROGIOS9gBYmFi6beBxgFlpF4mjZY5j1yyZg2hDiOIEpgLsgxg0hqCN3ZjOCXB3z1N/8ZvhKAf/jz/wTbzRYP72+x2Q544/seIaWEt7/zPp48L3jy9EP84i/+Pg4p4Ie++KYkVZmBtCzY72/ALO6Ipi2z4PlcCqjuMqA7GEPzQycAcXeJEAlnIHAJWJYMKqIpCgVq6Wgpm83CRg4xHjEmygBPqs3LyEy4d3kXjx5c4nuXG1AqmIYAKkEyfqqgnDVGLIRY9zerwF/r0hHV2MRcCpa0gEgzFRZLkGO1EyHCzTQiDhHnmuTItF92oNhhRURIqWBZFnUdJdAwwFwTzZWnMCPlghgjSsejzY3RH7QCHgOJ9a+Vyysoaenq0DFlXUvJuuvdg+zoB0ue3xDGlk6cGYCAUAZwWGa8vLnGEIHMA2KEiuoZIAHROUmJjWV/wBA2+M//xt/AD/7g6wAncGaEMqGo604sEje7Tr7RCUN8i4jtTgSLLSnMCNu7ePH4Lbz44FvI1xH3f+AvYrebQIW1hMaEvMzY///svduvfUt23/UZVXOutS+/27n3Od2dTttu9wVicJAiDARCIh4IKBIgJKREiP+EJ5QHJP4OyANCQJQgIRSI4kAUHBu33XGM3e3udJ929+lz+f1+e++11pxVg4cxRlXNtffPjo6Dch7O7P6dvfdac9asy6hR4zuudwuSJ7Iocy48fTIhqJWxSCvznHj85BH7aY9I5fryiro3xVCA6eouS2bFzaQ8u0DYQbjRcEK1mH5AhN1uRnKiFKOxacpEfch1Wbm5eUk5Vd559y2uH11ii2tJbXb7C3LacVxOrGXhcr/nYr9H59kBpWxzbbggkSSZO/iw9CFbZheaYLACjWvgV3IabUK7bIFagAVtwOFhkDIm12mZXP3e82yaASDsDrNEbpLkuKBSz91otfcv9mB7yyDYpAfkkA1wVMs4PJatCM+qcMVMYgojlYgbHoQcho9C8A7BDB3wdAB7o+Uti/UXpj7v7bsmFPk7u4bFPx73+cNgtd2RziPQXgE05aE/7md1HS3IsYypPROmPTWLo/Q1atluN/QVT+tm+ODRBk0hu22LKCguQ2jHYFGwLnahNRrfcMgz8ChDBzaWmjNhsSlvRgH0FQJlF+Bj/WXT75Bo7UzRsdXNmsmWcAaA5vurJX66H6c5lgnYWkVTf6fTaHcxHyyF5/0Y53MD0h7Yq3QQOPbtVcA2FKwhjjRZH/+7WcH0QaBa29l71kdXNve0JTE3HVDEHuvv3/Z1TErTLGDSt69GzKKaRVKktnPOnMrC0mcvNjrQDd8zd1k2CYUiD0KwgAZsI8u7eNvDntS6ZSE2l43hYhlutlb586Q79C74YULLwBosSmOFRcxauFlTnJ/S7o7/anNfDrdnp4/kPiCD9S/lmFvv+rBf+pXaXg0erO2tZ3xhoBcd0WF0eDwbgx4+vz6T16e3LOpMqosLANUJOoR7s4AkgTonai1YpQElqwGMKc9mGVS3LFYl6WR+8LqwaqVgcXXoypzVkshUARVy2pG1WB282ol08np+lWqZxNTr46i7hxYXVFXRZHW6yrrwOF+zS0qeZ1aUD2+O/PSjF3z//Q/Jmsj/8J9aPUGEd957xCc3C//n//0dXp4O/Nwv/kVee+MR8/U1dzc3HA63lLKgZHZR0sAP2ZTkPHFeu2wDBmBUdHdB0crjJ8+4vHrE7Ysb1sUL16t4qRBhudfIA4cJzsiSW9u0UmthBa6vr3j66JpHVzsOLw7Mc4aSzFrqde/WurKWyuyJZIJ7GfAzjpbyZMl4VFEp1OVkArymdgiFIFQdRxW1Od3NRortsH1AaJDjkWW1Yug5J697ZMXYvZgYZVlY1oUsk5cB8XpJqky5C0MhQvX6ZZ3ddddWo2pLXW5+cha/k5qwC+3VnjkOpPYophhnrRWqubd+8tIyjZYqZM8AUrWgUpG0N2PDWilV2F9d8J/953+V5fBD5O771AJZZrNgqscMaoapC8nnLngx7+kBjWYXXhzsqCK7a9bTiduf/YiPf/AJj15/l/nyHRLCrInMRKpLA8JJYEoz07xz6/oJxAD7Pl1Z9mEXbJPXCKh1cXDVjjAkJc9OarGIoB432q2GRt/qigtphbOnydJQhUBX1sLhcHIrJqxr8ZpWGP177cRwb49izHG4NVchCf2/vz8lhPXe3graqX/MYRdAtG4/bGsEdDA3Crx+jcLO2IYBZXv7K5J2+qsSSfu42Ny/fU8by32stG2z/efs3vh74BchcMVXRv6y4Sne0fMuPTweRu35Qzc0hngmi0vv1x8TJ9MFwYegRRtWe0+/P9aTVmMR+hraedkfTvd4tgzKgF4XRlrbI6jZumuaEPcwLWze0qRiF4YVE/4fuoInDuWdRmvpBiSxgXhtbR9yKeXsnvNXhiv5q65zq1sDrBvAFMqn8WWjK1xqbZl3SqerJGMmUqz8VO281Kbl1S6zrY/2wf3+D+86b0PPePYIrpoGZWjbfKyGxHMPXPf6dL7P41mRJtON4LON1YX78/eIiLv2s0mi1KrexDqh90peDJPi8c8dZHVMJ20eGtDd9J0R6bWhnQOXZgkbgGuc/ecuqtZUhL2kPl2+dx7SB+kG/HTeocpmHu/NXbvTRnq+HTeKDo0x6PC8P6ndi+2hPo76iQZyRSAptdDo4byfrTxa0L92TrBpd+CFbUQDmG03n93y+fXZvD41WJzKHWvTn1SoK2pSI5bWH0QTMwuk5MlHlLIeuTke2e2mVqtPWckpcXsspKRMU2KSzPV+Ykozd7cY+KCgIixU8u6WWhQpisUQWRKLnV6w6orqYt8TMWexoSrTZNawXK5sMKlQ5cgtUMoBVHg8z7z5KFEXKxCvebUYqpJ4fmvZT3/0+9/je7/zXf7O//L3efONN/gP/sq3+NNf/TK/9K9+k6urC7SsHA8nSims64m1mBuiVV7W+xvFBedIBaPrHUkLL17ccvvihio7nr3xOofTwnFdqKm7nopGPSMwwYFBp+eMJBtQ1LoiQBKzxui6oMcjkyqvPX1Enip3twvMOyRlA/vO5FKyODMRWOvq8X5KzrnFkK2rJRzJKZtmS6W5Im5iUrxrtRbmeUepK+u6eHbUahrDZEBgnmcvbmuCrtY4SH3WzI+VXCampJzW0kRISZCnzMV+Bq2sJYor24l2dzp5AiKb+UmE/TwDpkxQEWadUCwDrFmxDbBqqebn71NdFe/jaBmbEFHmfaaUlefPP6GWyQTyk8/JnJFsSpRZQItwVEGWCw637yPrT9lpQmSiZKHmYtZqtbjfqrVZrQbO3+Lpkno6/jPg0QCYDqLTRz/g9dff4+nbX+Hmqz9g/+QZ+6wU9cQwCBevX3L1TLi7Kaxr5XA48vKDj8gp8+jRnnmXOSwHPvroltceX3P9+IJPPvmE4+nElDJvvfUatZjDgaIULbCuRp/tHDFg/sFPf0JKmddee8r+YuZwt3B7e8vFxQXTLvPk6WOExOm0UpaVqkbfVW2fr8sKSLPu1zqckoStTb3mpwmWkeAGGfgH6hlikysNzB1XFcKkbvJQBwn2irFUSQC27opNPM+wPufAZhDK1OmsBY1JCMhOhjJChBASBrGwKUgaZzB6IJQKlswpNYfH3pmIkR0eA5oynBEw5+YjHutpgK3U6tZIm8sNm4qUhvFRYL3hc9UAYEbnoQgpPk8WCti9NEKQrk1dHo3r2UQPQkxL33y+Hg/zMW3Lp60/bW5S6s+rUtzDIfstkSF47OvYJ1MiJG+3zyW9yUEA7+5zo/Wm96mPMcdzZ2saIHa0BMtwX3P5b8JfeLVsQWNz62uAna6EGV/pbQtiluVhXCIxPxLYwPb2A220PRCJPRpo3UrEqSUrE1MgDYJvGkDjaCVlfKcIbUnd+jP25x54IpJ3gJWGGvw2Naxw4snvRvfF+/G1jWX4GowKhPa79vQ5IgYe79NV79uoc9hujbDUNeRHQ2j4XEtq1qcGdEUQVSsDUUqfEyWYjJNoisUk+JSFGxjfXuraAEzOBt6aElEEslBbGa4zWKKVVjImJ4ujrl6moeWTEDdGdN5wHk8YY8rZjBBG8sO7TJth+z+WNYAttt4xh6p2zonEwpqr7/m6nIP5HobZF0pSVwpbPG3Qy7BPfU6qB3SOFkBJyePzbawp2ZlWavFtoq09o6FeUqYBdN/f7RwbeGIbyyAYic+d+ASIy+R6PqefX5/J61ODRVJqgRFhGQjBVEOacSuW5xppzH+afGdhTLtWRUvxmjzKlPfsdjsuLnasy8JSFnP/8gQJCUGqachLO7D8kMvmGlvW2kCN+qbIkowpF7MwZEs1gFJJkpl3VrbAwM/EqpXDcmDKiTiDwVzDajXQOc8ZauVnH3zI//Q3v83rr32X73zn+3zlK+/wla98gW9+4+eY58mTcNxxOhyRmpDJ5jB5DKekCCK2eLtE5VhP7LLy/h/8v3zw4xc8e+vn+YVv7TjpgpRCThO1piZgJclWQkQrllwmdFDagcGQRTXcx7SmpgVTrWhR8HqHitXRq/7PSs8VWuxl+N41mao6eO2arJGPNwEumA6jGx0NeGqhJevJUXLBmUxgnihvAFhCG6HFHPTA7K4tnOYJtDbgmnOm1sphMQapLglrSuzmC1KCdbUEP5NcIkm4uVuopZJT5vJq71ay4sUiFKqwVCX5PNQKa4k1yORkpSTKmphUYdqbMCdGn7UUt0ZW2w/1jh9//w947bWJ3X6mqB12U92xTjckX4eWGEO8OPh4UATdNmEx1iVOlfYBpISWhbKeWE9H7n72AfPlE5jMkmnHxsrFbmY/Jw66es1RKEm53E3M895dmOpgRU+NH6Q8UdVAWRxgBqZSo4voT9SRNFnDXaKonJYTec5MNZkG2iQ2xEvY7C72pDRRivLixS273QX7ixmtlcPhwOm0gCR282yZje/urLSGJMviO/CVzl/8MHbaa0InlrQkXOZSNb5SO9JxbheuZxmoZuHzqzbckhogaK5GJgluYI25TpmrdcRt1wZu7guFXQwegJjSMpd2+kkbkHUOfBK9BmB7ro57XVo5l76O/acQuYO1fzB0WYJGN2Us7IYmRCUhYy79AYwqYiTmPK/HYTbxqMfftSyA3rWYZDGhpap6NtZ+l6JmUWp9knambQYpvs4MIKV9LZt7jYdZf3qsoW7AW/INFOWLqp+5bb9g4Dy1tZfN8wHCGocdaSyS3ai/g9BZ+DydmSU7GYhbjM7epSPPhQCZYzwe3LfatH4KaHXhP4V1v7sgRgvdjXQrYAYw7S5+tQEUW49oJACWEIqVENo3Xhkj8MXzMgxz0WMfHZoN3dENPUcL0p7r3RBPYOLulECPY+1zHHqN7t44AHRooAycR6Vzy5W2Ei0b91m1eYykcl0hpS1nQOCZcL/dADMZ+kSc6+Eivr23W0Pbo7b1fR5iPpKjrtQUzDQGKdrXL9po7x3e18fiz4lQIuncAG43bpN++8ZCHR/GZm0luhxMiZVXE9VuqUwWJoR4jdJRQTu6n0qETZmSINxtt2OJvtQGFrd0draPIn5C++6IvciGp1nfo34kdH4lQtN0jgrmzqa3CpLRWisbC+boRaKtZmcA13Es2rJlbff059dn5/r0bqgeb2Qmec8cpUpOqTGBJImyrpsYDJNH/SDQiuM1i1dyV7XldLLYv7s7PwD8QaQzxSoOYhiAhLDUdYivS57FyjT5kjMpZ47Ho1sRzPWs1ELOM5TK7BYl28ATaTY3TG11GZTL/UzKF2gtrKVwPBzJknj5s5/x0U8+4Dvf+T6SJ/b7ma9//T2+9gvv8e//5T/Hl770Bl/48lvc3q28+PiFbQ21moZlPaGrld5YEsiU2M/PmKeJL/3iL/HaO89RzRyPJwNSTEgVcjUhScXSz1uikTjcukXEpUuDWpK79l8SOe/J0w5JU2MOVf0Qq+EH7yUJPPtpEs+2pi6sJMtYlnKiio1JT2sDZmgEvAdnqg4EpWmoAHKegIJhaQOJOWdyuPPiTG5y4T3nVj7Dvi92qGUX2Tzepq6rxcV6X6wLI3M2wT+C1Kd5csHJGWKy9+dU0KrM88TV5d4UE56ZNmdFC6wqTAnmnChV+eTlLWVdbY6S0e3dcSWjXF9dQhJKtay+z5485rQcWdaVlG45Hj7mr/+Xf53/9K/+J/yFv/jLZDmZe69eMOkF5uKrZzy2HxMtN0/blX0fjlf7JglVstVHRHjy9DHztKdEgKxYUY0XL448/7hi8Zhm8Z12lzx7/QmwspaFlJV5zgYc/aDv2lKDuarVLC3Jsslq6bUTLezTrM6STCA6HsxNc7fbIXhpjVvPZKoG4vM08+zq2mlKWJcCujBPmbSD03rktBR2895jcE35czqu7OYLcrKdsr+4ALV43aKFMoFOQPYYMUyplElQVroZzHhibu5rdSMsd2FgiEh0wSTi0cKVayzncF8AhRzxKCIOGLYC2UgOIjYWMBdu6LLFRuDryHUDFO1Xjy3Utkpo6i60aaCrcL8UoeddEFppoA3hbeiU/oDHQ22G5BaJRHrg+Xjz+LF1QDf90/YaGTT2fcyV3mgI5vf7u91HSsv02MIJtt8HmIw5b7DZb214NOLjHCVsFD1NoO8xSlWsAHpY+zqQcZjiZ263VHkCMGoT1iPnJe5mb7VYxWe0gw4IF0EdPtJR8usQ/QwsOD45swTpdmgDaAurW088Y+uj0tc+2mollwbA0MoyyBl4ctDQ6MzfaWemPZ8HsDwmXRlj+LpV1GfzFRZPGf7uCXdiPAOQteET7vF4u4Gz4r3n4cXJeey4JNvYsQCrMtSgHtxXR6DogC9tgMc4r+7u73Vug7fZlPY2eu2/4XIwJTUAg9H2FrTJxj21gR1AiNjE2A9BJz2Sd+QVWmvbQ8Fjx7HWGgGHbvTISr/dlJlRRkiTDvMeMmxfp9Z3ESK/8agswBUCPVZ0u6fC/bmyDRNAIMnknlWD0szv3yilxo00gs4mI/jvTn/qALI5rDrAjiy2Iw+JPWOZauvmjNKBpwd9qyu2EZBsarxaov9xEFhOE3VZUwbDwefXZ+v61GBR6YJ2I36MRuOf620eevh+a5sDxNpcV4sNyrm7Kt2Tg/xlI6Mc92EPkNZ2Jjnma65GIaKVWmC19qpnXRQRM6G398BaCpnYbF17SbIMl7oqp9PC3e2B3/mdf8rHH90wzxNf/OIbfOvPfJUnTx/z5HoyZqrCPGXYT/ZshVMpFCppVSor+6tHpJw43R44LVb6g1RduT2BFLMqEhrqAEoap9jALGIWXZOZhWVdOJxOHI8nMla2YFnccicWyB4F0MNVRsVjQAcasLTLlVoqESMS4Au64Na0qNIX63C4A9yCWeIYqX0da7W4M3UbnqepzvTDPeghRMvhU4TuTtMEs2TurFHXKKykglmo7GDtiggRPzcMXrTRxFGfHJCXADzCZoydXrVZ/0rT4NncoYWdl2A57GZOS+GD9z/m9nmhSjKXNhcWZIgFjfEGKdq7+j4Yf+93n12D9BKuJbv9TMMfTV6W5o68LJ3RZxGur3YcT4W1VPNOd0HESrwQkpCtqQYdOmgs5iIsSNt7osI0z76u3a3RFD+29muprT5jWG5FkmnEm7KJeGmLB9RI3KF+Q0gJ/pLOQ0BJKAlS7PyQHLUJ9u0wNmognFIJoOHtVh1xoNFVOlvHGOf91X1o0bSdvcOZ/fC9jfkNe1LbfzYdCLE3nX/xim6lB76u4h4MzqNt7P39rZl79Dn2cBBsz18cbW8elgd+feAF/mIXIR8YWFw6fPZQG7SJl829w/ObOe5WFNBupWuPCQ+g69bfV18uqLf51bPbgw613ScuAI5nRGcD/Ty2Hy6Ye9vtewkL6Shw8kf+HoCy/x3dc44aYRUpIarU0WIRfT5biwYYBnq4TxvxvvF58f9vwY3w8LMNHLXpkdbUufAe947gsM3FQ/0ahrj9Zfv72Lc29e5h1D0MglvLWRNbYWp0Ex6nNCF2zo/gpClNZBhDW7yBzcorNrXz3kQv7bKh2zgjex/1nI4DObezo8ue47i2Hz44RAM2tbYM59GfTYzrBnT3OYu7O9AiRKt7V+vy8Lw1re2nIKazrpuFts8bCA135jNuOAD/83cQNNnYzDALQcswsKlBhhL7T1cOdhrvoUXxfV+3Nr4mupvcNnozWW6L7hn1qnqjn1+fjetTg0WRZEk5qmlTwRd8ICoTzE0LFS4MhCUqS2cSTatnpBhMP2rtmADsGqHcYwlS1IBCWFaLoIy4o9g8rQ3faMWtS02DkoWcJyv8rQYYg6HWWizhBl3GFeB0OnVArCb5lbqyumby8YWVERFNnG6P/Oi77/Pffe9HSEp88Utf4Mtffptf+NpbqFaSCNfXF7zx5mO+8O4bXF5d8vprr3F5uefiqnI8rEiakFQpurCG/Ilaki3XgItMqIYriWvCBubXA6A7881TJufMJ5+84MOPXvDR81su5x3CiupkAJDqQrWt+bouNI6o0hhFrZXj6dDkYlWYd7P7wotb9SS4ja27gBZlXRdePD/YvTn1+l0LI/8hLGi1ruhit7QU/LXSCsOmzWtAzMpttBoWcefeVcmC1zozcJsccCYSk5swVa3dnIVFPBMcmEucH4wpJxBFitDcc50va3HLmidhkVRRnbi5s0r3khZyKtzcKpcXl1bnqc7s5h3vPHrKnC7QZE6g1JmaIXtc5+aUHpKydO2hts+00X6/AmCMwmGtQC2sy4GpFiRN3RUIm4d5lzgthaIGLSMLKqrUImjNVuwec2mecgKdmvtvLQ4qPU7wtKycToslL8rd5Ss8B9YS7p1qro8VCtWTHNkGLbnCUqjlCCj7i4nr62tEsvOOypwnyhSuNIWUdux3F+ymarExFKpW1mUUkiaSzsx5j3B0jlRpqeOSO0moNjfMfqCPUowyWnvjMO6HqA636sMI7Ozqwl4Xzs6FkXEuH/o8ujlQSe9LtLV9KaCbZCijTSVtbt0Kjp7XaNsvZxzbTK7d0sqZIN/H6OJL64f6c8P+wxUVG6VVf8fDKs0AkUHxMTcdDGzAxfkynQmu9wBEU1j0c3G0eFhZGSyRlbhr7L0+bt2Po1fNnVRGyGC/iCtGQlEDqS9Nly1tuodBuP5/+HoUCntMrp2Haaj76C6rZ0Ls+LpQOocFbVQMiLdx33V1bCGmLG0+EZcRmrVPtWX41GHuRSJjd9D5/f6GQmu0KMb5X4czJYTp6kAmEr51g6Ormhqtx9/jmGVYtOAaZ2jD71PPrm7xf9G/cS/cJ82hgU7Toa3ym1vitwD1Tg+DrN/kjC4PaDtfzHssNSVezGPvVl+/yEBqRsOBbpyeJIAFg0U31snncuhuu+KZRtYPoEmRBCmRanVeZm2Xs8lutN36PvBx7/95H0PsaqG/6jQjEXKEy1d97s9YZT+/nV7blA/vI2RjlDLGezdlwrhvXXk3lDtqhh/6O229g4+E3Oy8IglSB/Cq7o7rjchwZgWANqPK8Pkg2MVcVXSzfp9fn63rU4PFJKkJ3uAbXgYGE7hAwkQdhGuHbBCeXYN/+ubUcqZaa8sKFptWDTGFLEBoRYMGm6uru6NGLItGXIP3n2qxCKpKypkpZXd9TB52qRSBZSmNCUQh8VqLg9zs302gQlkthiQn5XquyJQ5VuH2VPnN3/oDvv3t7/Ho780sqzJn4bVnO9778mt841tf4c033+DP/ivf4ItffJ0n712gaUJX5bgsHI5HHs0+i0u28hkcUfZIM+fTGGkDxGLumqpmwQhhTbIldbFi6ZW6ApNtessANzD6lMhipSosZbbVBwy/d4FWCiV7opsp7xo9NI90ZwxRI1JLHHDJ//W4y+JWoaCTti519XGKxfSI+FqE9io0kdpISbCyJtUtWOIMO6yYMU9xUGtVNNWBiQZddnceJKxCnhDEs6ZaGQgLymdwk1Y6Q4zuraUChTRZ+rGlrKSlsCyF5SSkfMmbzy64vHS/WxVMLStunho2ZUghg9IGbL3z2a3nJ5JHENuf6oXVRTjeHUiPLAvuWJOxLCunZWm8INlQub05sC5WyzNlYbeb7P6ykqfJPFlFaO7B7lpDwrMm254qYXLFXdtH8OPCwtrWpbs4r+tKTZa0QlW5vNpz9eiKdakuTFn5l3kXyYpMwbPf701J4P0Ium0rr24rDOvuOsw3HjtWPW71TDqLtd5O/StFuE95NQk/JLnNGPq7Rz571oIvbnP3/iPeFmAgizycPVP/CJx7Bvo2QOPevdFer/fXi/dIvyES4GyQT9+zm6Q5iP+5FWLHc2oYBgQAGw8crbzyCroYujEKt9Fn13ncnyd/h8X/eGZxtkD6XumTAUxE+Y3mReh3Ve1JdUy47X2JnzViaUV4aFk3btLDDD14adTU3I5rq9DYehWNYHkE0+PZb02PAn/vyihTNDASzw1gvFlypHF0czQa9ny/zoTf+OlAeFyJ870TgHu0i4ctKiiU9nsHP6ONuuOnM4vpiCYbaT6w6QJUxPZoVN3XfgTj93jE+RJr/2zr2tuJbTO2837Hdkx9jhroOXuOmJfhnfEC2azvyOqCRmjeFls32648iBjE6Hs71xXjKbXLLWFlPVda3PdK0JYsz/JHDJ972z0hljQ63E5wzCINhA8Db9TRgCl9nDFn96yP99o/ov8bAAAgAElEQVSIfXB/iTc0jYXORGm3mC4/xonSIxYKoliySfcOi7hGj+SuoXCxHefAsnsnPuiJ+Pn1mbg+vRtqaCaD2W5VTu2ekdGdM432gOLZL2WzqeMKd4fQUolII8IQHCev/9esR2rxUlZcvpOguap5zEu4QqonCCgVlV7AN4sgqXJawke7i085Z2Yv9yCSWEuhnBZSToiYZaJKclAqJBL7SXh2PYODozTbxru7q/zge5/w4Yff5eLqfb79Gz/kyaMdf/7f+pf4+td+jrfeuuT29sDzD255871MloLbwKhFqCmsobHxzWIaQkMw0rYesT7V6wJOyWpCTh3MVNSf1bZGxQEVXhuvyUMOmjLZlQTiQKi4lY7mclLNvEaXU7o7k7myFpcn7L3ngoQxmNQYfsSpVHcbSTm3dbIDtWvRl2VlPEiWxeYtt9qP7nqiyvFwZ8zNn0juCn06FaoW1gXu7u5sbMXeNRezKC9VyNXWwtx0BZLRwfAKBCVLdcu1WQiWBdCV4/HEcYHLPPGtb36BN96+ILGSKNTkayiZ7u47Bths904afh+vEa+MAmspmZR3gHD78oA8WZmvzDJbc0aYWJfCuihFIU/C/mJCinA6gpAtq3GqXE57ZiwbbLeWxHqOB30xZc3UYz1kUERshSBt34E274GUUxN6ltORZSk8fnyJJdlSEDv5U8rkJJZ4qCmArI5jb7fa/IaQRSGnitYFtKAeOyNJCHVvimyBXvOrx62diYUyuCFupZxh4c7HzJlA8fAlbMHbK0HYA23aNhn486ukCD17hoiN3Qqv995x/lPd/dYFWJFwceT+nEh31T13VxI6LbfuydnLhmcMFD+gxW5+vP8sl/EW+WOfGQTpEbmpKSbS+PUApOIdAUpwL5Q+t/GLNj5fN9MSdEejxYRZvwNEQrhH9jM3OShPwzmBx2E2HrEZy/3xN/dEkeZyXc/XLMYLWzIf5GYjx/v02AXkM2FYzn/vgujYt3MwJJt57fJML+NE4xMwJPYIACNd2T2O47xvXTd3f9yb7T4w5rAqxZciY7/H9wyqiHECo4tngOTcstpIxb681/cRkDZLWevQcLb4vRHLFzSrYQyoHZx0+6Bu2lM/dcOjKdo/f4eRXh9DC2voo2QElX08thi6oaGYrA7CYsxD7zbXhq9uaGw4i+MdbY6k8x7t637ex5bqAd1aLgdgLpx7igx9aGiwt1/pwHXs+3AytfCIPtc+h/4+776DvdgakVEVotYxcZaEO7KfSyln9zSz54rX6Ojy4OfXZ/X61GBxWSLFrjRwUKqVtrDMm0bYpY6Hk10hHDYi8QMgu9pzXbtVoTEJB3aNgfu7I5Nmq0tWzX1wnvDvBEle28zT4UfcV3KGZq5pVgy8lsKyrpyOR9MOiVkjc5o2LizNvdWBn5VjEJovf4KaYZnCjdPmaZaKTolaYE7K1YW55VEzp09uOH3ykh///o95/qLwS7/4DfI3L3j65ruWsfRQyMViEu7KEZLw7NET8jyBrAiZ4+FAWRdgQimNQalKA8455XYe1ao8ffKU1157yqPrK8ppZZp2VJK7TRhTWJbKsqzM8+RZaf0QcEtNTkJOswP5wmlZOJ2OSGjuBk38yNbmnMnzzKPrK8Bcekzg744gwbjXtXA8LWa5zJODUAOKmdzoxNKTnxizh4lrvqwqQHJ6rZbMRJKVREjhnlQ5nqJ6penwUoqaTxMpZUpVXjx/2VxL7SUKtVIkk1DmbM5wpxWohboWluMJEeHiqluCjevGVpyskIMIkhamfOLdL77Gk6dX5lqnK5aAIlM0rJY09X0HhINwfCaVtONUWvoe20PO/kvKaJoRhP3lBWme0XlH5WQFfoFpl7m4FF7cWA3VUuHu9sQiwv5ixzQllnXhxcsTT6+uubzccfPyJaelkFLi9defIFV7wH42C/80ufXfExvtdvuW8jyEtCdPrpnnqe3Dw+FEWc1yuZxOHE8Lp8Wsz6dlZZrEsx3aPrByLMkS64Rcmy0Zl1TfqyIWO5oTh8OJ07Kw2qblpFbiR4BUVyqFnM01VxG0REba7TGs7rIqxrCMZoZMovcFmyYd+1+xboPkGX9vnrr/dxMlW2KQ+yKQdhvQ/ef8PSEwnFu6WtynCxfj1V8nzT0SXCHlvzfxKn55yHB31u75LZ6MvbU9PtBrG/p/dHShG1+gZ3MC+PmxsbQ1wNHPqi5NbZ5uf3a1jQvIcrZSA7CX8e/IwriZgxHsmodFHXhsGm9u9GL/zE3VzraqofOPWxNJS2sXnxEhVGfxuWzHe5a5N59ZqvIg2NdgesmF6HtS4hbc3fvY90mzrun958b+j200UBz+fA3cDMpqnzMdzL6u+uv9daJubo7S32kAPFz7at+/fmMocyJEZxMuEhYW3Qr04n0Pi018pkk2rGCMn+xuitsrPCjGbyw+0feNGKgb+60tE5Yg0s8t2Q6emBWbMQUHD83VUKNUSTTv7r21x8Dhylfbwn20ofhs42piRQd8o6Wt6TrOpqCHOfk7q6KD4QBXGIaLZZ8jUFI7s0a6CkVbfDTmOWrATbb3hqImZNiwBnZlqitSfay9TfG7Nw0GtW/Pi8BuClRxDBdhOCNvb1PY+zvwrwjt8hWztY2yJcmVZp60KJLYqMv+YY20+smWOX45raQpMU+RUNJyHPxzd7j5/Prndn16y2I9Z0LahIegM0sJbUdMyBDN6e8eD5P2eWQxDPC4if3ppxrNfYCzY2I4TPo//8JdBtszg0pDRLw8nvtoe6asiKdpjbv7ba3FGERkXZXct5dYVlBxs7tqZ1Ca+0bLoswZ0pQNsEihaGI6wI9//BG//dvfhSmR1gOX1xdcXO1YamYttmnLunA4rsyXUI4rOSd2FztOh9GFM3WwOGT/Uveh3+/3XFxesN/vOCyVaU6UIg7gjdusZUG9vmBY+FqdHr2vpR/dhZrFwtclUkyrgkzGLHe7HYpS6kK4pMYiuXMptd5Rq7LfT5YUyC03tRiwFBFPAKQb7WcctCl5KRWnwia4avU8JXYchNasH7WjRi/+08GLZWW0ZERUNS9RVbd+ey0oVWeE5qIqkixNvIjF7Hmr8zyRs1DrxFpXqlbef/8T3r71Q3ZM6BNud2cCaszFsBj95OzkToivo9AtIlaAzU/Ji+s9ac6eoS2smDDvMpd74ebOaiWWVa2eYRLmOhnNFTgcFx5fWp/WUlnWZZOCPg6vDYDx/RrZY1Wc1vyZlGJPGy3mLGg1F+V1OGjzZIDydFyY0tTinctaKBV3UbfP1rK2Wlsh6JkdRqhrpayVWgWZdlAjLSqN/hEsI7GXv7EkRpUwAschrCOYauvSx90mhYfXa1io9tyD1yu/On8u2hylm4dP7PNP7xeR96bvyf6NafvfNgcW9dnPjD9iNPc6Msgy7fLE0AZA/xkaC2Fs0/kQUh8CLPcPLbaD1c2fkRg/37tdOs3Ag+6e4xo0F/ENfcQvg1UQaWdwi1vc3B/7fYQbw/vauI2nDNFeLpren9ih6bNfxrX259prh3bGPg639fCH8zG7MDv+3gDXtn8GxGT4vLfV7tz06f7vcZ/pWe/Te1eo9Pdv5fCHHeu2rs/DVITVa3xDsPtGX0P/Nu0M3w+y2Kveb8dwP+Ns8rc03Po0zt+4fmd74tyKu1mXZmXSgSecrfPwnHgCwhjahmvFPW1utoqtUBCHQeOhfoVYN+Z3ijF1xdwwNhn26hnvu29lPKOVIIpz/vKKBTr3AmqvHOdBcUXScLY8yPfawO4pvDr/GWfvfiP3rbPeHn2uiTColEIP5Mm7HAhWK7mGYorZNPkaq49LX8FjP78+C9enr7M4skl379LQLPi3tYprHXDwFq55fZM30DdosQIkpjNLXgjW0JPgBHOM2EQCEFHJedr44u/mCdknTqeTxUYly1gY1iQUt0Jm5jDvezerF4O1eEWzngVTCOtiyh43JwnIFrtIaGUtm6LMmZQrZh0STmtlKUcQs7SKKJoSj19L/Ld/42/Dfy985ee/yDtvPOFf++Wv8HPfuOPi+oKnT5+QVfmN7/wO3/vBC95+95LnH6/8m3/+l/nFb3yJP/zhT80VNRljVvXkK+LB1WIMrmJlIHa7zDwn1tlqR5rALFZHUkzgrnVlytm0SAJSihdx9RgztwraegBeQ9Ju9hiU4C7hxieJad5ZqmYHX7UsVsJEsJIMIkzTxN2daasuL67Y7SdUV+Zsrp9rtRjHUk8c7g7gCVKEEGqFq+s9qpV1XU0ASJl5ynzy/AWqBYppF6ec2V/uDGgsq9XozBlJcDqtVLV75t0ONDR+Q+wmCbSSYy+skNLEbkpcX11aFtH1RNGV/TxTsVi7VSuiFzy6uOL6YuVnzwuH48Lf+dXf5c0/8zFf+6Uv4uIwNXkingivG/ZTVbUajb7GbY/FpdavcL81X1nwqvUkKQgraOLicqImRdcjFFcWkJFSSTVqIA5MPqtnG5gQCjlbNlPFeEHOE1POrWxJ9DkKl6dpakCxC6dGt5PH3b58cQNYAiURnPbMSpnzxMVe2O3M/TQl5fbuQMlWFke1cjieqMUSaqU5s/oa7/a5H8haOR0rtZ5YloVIs397V8jTBHLhcYyuNSUEhuQKlgIspklvZWF6Bb6N8DsovWI+um5iFNLov28EkkEQGdY42nJ52mnBt+N5inKVzePco5ut29o9IbV3vstBZ4LvRnhUT5S0Efx7U3+kyBDkeiZP1YGeooVIANOtoNKys7Z3bnvQOqJ6DqAfEKI2ykqPFUwM9RKlu8gOcnOsueJKJZHRiNLWUugxnX34w9oOfRexghYNMKo2ZVBbbbeoheolLJDFEbbFtDciAZxPhEugz1FzyYxtj1DQHkbtdFeLEgViwrqUN+Tv9uyYnDZt2/eM7oawpbvRDU41/rlbc3Nr8j0yKLmb9QrtQis0mcPO+cFS5XOicmaB9643bhXrS3hGRZv9vQ+5h8eetOdDRrK+90QxY3uDC7GoZ8fWs7nq+1q1107cXvZMT0JIB6Yu19lVW5ttnoa1on0z0qvx71gTy3rZ+wOcWdboY2oNdl4X7xNMVqtlWIv27Hb+o6GUtubUAKTS+uKur2DZ5e+tj1t2XZ6F7pI8jjkygIc3U8/Y0GXcjVtt9MXHXf08vcc3vY8tXnGcHqB5tQ201WhZezy9eL3x2Czq+yCJUCJIMwhZ3QK7OZ4G+o1tVRU0WW4E/968uGymalmZsBAWVczDioj2V3BDBPeMUJ9fn5XrU4PFnITVaLcxtY3WHOiCAbRsnNC1CPG39I3XaybS0+Hn7IzOYahvtl4U9JwB943KkKQlh7+0b+Z+9vkG82yLEgd3CFoilLVAS6BizCLnDP6enLNla1RtmVu1KuRsrnvFatHt5hlJmZps2xedXJhcKFjGqTllssJ+n7gpld/89vf4tib+8W//gPe++Bu8/c5TvvGtr3Kx3/G7v/cH/Oh7N7z2BpwOE9/85tepayLlPchK1RWhWoKbWhwgpMZujFGae25LEjPUWCwmx7gltZ+KcZgZ0zaQlNylszOo+DBBLU2hFme0xUVKE2AakxdbK6Vahi2/pcUU+hqWspKa26qX64iDFHe3UeNmpR10iZTtHfM0s9/vefHyxpKdeOKbKSd2+0t/14FSC7tp18FiraR5YrebqFVZFlM05Cmsocb0kh9Ea2O2nhQJOC2FWlbm2Q6XUipa4O72jjdef8b+Ys/t6Y6XdydKThRWl4YAKmSoC1Y6RNwNxK9zt+/Yi134CGu5tANkZNE5JbOyqlh9SBWqOADWREU4vjjx4qNCFUWkILk4XWQkuZJAV0ROLa1+xH/aQvsxoWbxvdjvXDDTVuOpK5Bc4HH352U5gip1NfdUASTn1va82zHPe9ailHrk6bMnrMfV4yPg8uISHDQo4dqevHZjJK4yWjm52/CUgbry8QcfsH9k1ndVYZKZlCZEPTFWElKqiHj9SDVhQaDFgmyU9yGQhCIlLMfthrOFlC7U3rvOweIZCBXM9drAeXw3tNea1RGi+idd2GluTN6XLsDf71crlzj0oydfGcapUbNN7t1/r80zK8H4WcTbhQhX7/XJy3c0YTSAwL2X3H/x+dyf9bEOCxtxhJsm2yvP3j2u0VYqo1v07s/v+dibkD3c1+IPh8s971obDXipg0i1eN3YI+HB8dD69nW0Op8bwBuWpAa0+3q3ZW+gbzuX5+v/KnoY4/fG+TgXbkO5cg46e286/Y1tjkOOFoaOxwtIRJ3DxuXug8K+hR4cW7eERfPiANL5R8haIfsoXnrA/heuzfcUR2N//QzY7G1VkmQnFN9H1cpHdVjSf4TstPEui6Ud+NuWXqLt3PraqGLTV/PMOfcxH0GQDIqfcQz+dGc67b3S5i36HC6frVxWyLDxjK9pgDZ14LmZ77O1bBb+VqszzuWzvSNsYlxDrjKZBXfH1M383lMuqJ9faQSS27nUM+Ltqy5tnWJdmgeej7lrtuq9fd/l723/p8lkv8PBqgWklHh8fcmUJ0pZXDbMLEuhFovBtvAfG8eUp1cebZ9f/+KvTw8W50wKt0SCQZs1J9wTp9Q3Z3WhPWFCdKmRwVCgOJgbLImC1aCzA7hnMM1qabkPhyMiME2TF2/PjT+mnElpZl2LuSg6gCirkr2ovDBmXATUAY8LKwXT+LZg3yiSjKXGV0AdLTcNl2t2TFdSkJyaa5spNStaVsTBpSRL5w+ZJBOTDodfglUq+0l47/oKkcTd3S2//msfUKryt//mr7vMLaQpcZnh+aHw81//Rd588xlvf+mK082KamKXJqZ1ZZmdUUsBKrkKWWc7fCxsEotZMWAgYtYbRUmT2NwlZZ5MS3dU6+hyPLLf7ZmyZeysRZmmicNxsfMnVbQW196K5Q5RELVg9jQp05zNLbMIZFMYpJQbUzW3wkyaLCvXNClTErLMKIUkJ0iQslAkMUlGa+VUV1IS9nmCYk7RqfgBmSqTVsvomISK/Vuqcnt7YwmOVM1YlgFRNzAJmhKSJ+YpkVKxuZpnylLQnNGycL3fUyp8eHwJ1QD7UgtlXUg1M+cr1uVI1UzOM5KV05J4efeCw5I5LpmcLnhx8xF3L28oKuSpkkuBYlayktTGrdUFQ5cERSwwPvmBrxMlErzUibJWpmky4KALUCi5kCvUZSJlRXIhpwtPoZggZXOxpXD1+o433t1x+AmcVmFRQeuJWq/MaihWF1KZqOuJtJsop5XleIIZ5jlBXVFd0CpQBSknVrlASiFLIYpvr8V1DmWhVCs5kiSzlh4zlLQY3/H44900kaS6YmAl7ypSFdHMlKeujCgry2JCw/FwNwhuiXk3M+8mRBLluHK6fclf+8v/oQNMc1tNsyW6un58xby3+58+fszV1WP+q//6v+HikaBptT2EuctKETTFJnD+I7UXeFZtSrNSaIJfbvnh15B9u9A9FBO3m8PFcyskahWLvQ4wo7UlMwlBwWI5tyU+2LY+CMZOGy7NtiypHhdunlFboSc/IPhHYQhliF161aUOOKWJ7t3tMgS2wfkzeLB9rv0z76cKxp9qpaSJpF7uJZlAntQtwrFeIbkP4BlcOSWpCey5LU7ZCFzNCyC8ZXJqVp+Om5Sk2pWWI0CnP9+Bu6Ks9v5q69F8Y6R67Ta/Lympzl5cHmpd7EyYZ+TyMeXuOSwrWTMwUZyHyuz9C7+9IgiWsK2w+jgFSdnT81v+gOr9y2HJYHXF6CUpLV4rd6Jwsv4OYz5XTDRQUFaXCQT12HB1mSK5IjYUnuZA5CCq0s5pWztT3mVJLct38PmE/ylKt8R3y171MlxIgHSjMJMAxpwGHTQG6LClsDUJ0gjvKUm5jbWUQrjUh4I8FOixI7VG6IFZj+oZSG3K91LMPfAMTPaath20i5czsv4kt5ApuDKYoQ9lXdqy+QsN+EyeFdPBxOgphssuoxLC4ZmRFubNU33OtCn+vTyYPxe1kwNIRpz4GPtnNF7o3TMelGRCS3U5NKzTXq86paHkSt+0tnQRelPJSe7JvehwLrmpvdYwTvh6e8K3aCcUHLV6fgT3yjNwXJhEWYHayki5E38BpEI2fq1LLxWHmKybqEime8OtBaqy7mbbC+puojpRdSE7fUd/DUzmLpuLdICr6qEcUNbC5ZVw+MmO//Xb3+Uf/N4PeO/tKy6vdzy6nLm+uOD11/bkXWW9WXnj6QVvpj0vjvCz20q9+5Db9UxL8Pn1mbn+RAluUjI5MrJTLksh1EXqQsK28lFnKNL+M7ijERamdiI3Rtsb6AKWMYHQDMnmpzGgKCQ/aEcKnpzCUvtXD+QOBlo37Y9uHi4saJjtB/cO6R3SGEdTboVUJ5ux2HtLs2amwQSfknibdqgsp8jTD9ePrtrvPntkEfb7ykFP7K+fcvnsLS4vK3JUlrpQc6GqZWYtVTuTnidq2nE8fsRyd2Q5LKxrQTRKOgiHwxGFXm6iVpYFLzHgbiMxpgDGDHFzIWMF4xPXcIkVWxZVTscjN+7aY+35untaZbPYZAtGcqZUspDS5MknJrLuXHg14CZRImWazN0wi4WrakLsxMSclXGG2AXiJJBd4Kll9dhUuy8SAQh+SIe7kB/kZV1cm+jArKcQ9P9KszzTPjGruagw58Tt7QGkcjqeyFJ4982v8dqTN8klhFwhY2sQJStqmsiaDEgkPCEBgLkNl3pyIS9BLnbgUVCy1dVogfuZnHYm0NWFLIpZGTJQqRSgcDqtHA4rVE8r4i8UqYgsiGSSJKY0k3Ki1sJutyOliZySKXKqupInEgRZrcrQk+PCiWUY1rafTFavTPPU5rdWy7QWzOS0nKy9FM8IOWWy2PtqrU1AmCYvK9PlfG/TD3+8RM6cePb6Mwes6gdqtYQ5eYYKp7uFD48vuL0sGHst7cA1/lG9ptk5IDLbRKULcsoDt4G7uQ8JDVxS67Fow3X2vHgZof7BAASdjrMnKwk+HLJNB0q92aadbmBMzr/0X7cJIWiPDSeC3L/nIWsWdNfyaOg8fvLc7bSJIENN4OhiRB6TE2sS9ouHPkw2luxx7CboM/B1f4MI593cWD/P4rPO72sWCWTIaOqu4g5MR2tXAwn9A/9sMr1ruKj7PbV6Ddiga4UqShLzJmG3Z5kuOa6Fu49veO3qGfOU4PiCWm9AZh/3bO3XAMIVUgjzOI922mx1ZlMjD/PKMT4uyRSW1qGKVkt4EYA8+NxmWWWwjeREYOs4soOuPdeG/eVAsQG1aKcJxC6sRIITJ/L4Pn7GCNwwT4Cfh9bblCQe9rHZa8O9bv0a3VMbLWzWuwO/c5fH+KyfwSM4jL9Hd3+2/RmuzfwQTDA48SiL9YFYfwdPkN5YB8/t8w6KOr8I99Q+Pdtmhj2UDExaLduznSQ9hCXmpc9H7/tD7bYX+5iDHurYMWD0jItZeqhm7T2LePt8WIc213rvmQ1dxvs0sZowQeTLgNX4cDZZFM2uu1Mkg6hSSyTp63JaBdciJVNcSsJCpFZgYfJQsTJMS5oEvCyUsTJtc6qKK/2FkqKG+kxOE1l2HF+uHG8Lz/OBNN3x/h8m0gRShbffrnz1jS/y4sUnfP8H73NJRfOfIDLu8+v/1+tTr0xRpRSlVotrE8msTpwpDXUMhysUWqGNAD/MnGFa3JrQD189E6zbWd/rKBpitHigyLxJMOTh3VVZtZBUmSZLipEw4KC1Ut0ta63FvPxcg9O0100m054VtW43e2l/O7BqHYjPPTeUiIEQ7d+K+IYUWhIZrYrkFgVCzpk8maC7rlaVfr/fUbVyUBv/b/7WP4Zd5Ze++oyvf/VLXD7ec8qwrkKmkEXclRAkT+Q8cXxxw+2HL7j7+IZCYkmKmpmR4ymCkg2cnU4rsNraSgjZiVIr9RSV78xynKfJNHgJrHyBsaDkDMoO35XT4cB6OrlmXjpjbtpc1/R5qYO72wOnY9R2LKbFTibYLSvMKdE8QiUECWtHBS8UD6RM0WSa3ALqbSVR5t1MSrCeTBKZ84U3coRamMSzZUpiWcMaXpEs/fCMxAuiHUBqqEQU1dXiTKQ6WDTtiyosa+W0wm664l//5s/z5XffIEtBaqGQKAikTCrudpcrOrm7rVrcraDNFTLJ1A6n4nRADjfdClIQnShAZqWyACtkPGGPHUqiGWXixfOVjz5YkUnZ7wBJ7Ke3EVGymNZ/t5t5OmUmL9J9eXXZBaPaNZcVc3ee88TtaSUn4WKyWVqWwmE5kkTYzxM5zZSyEJ4MIcAIuAa8UtbC4biSZOLicu6KJBcEw+U6zt+cty4+JicodV2bMAYVSXD56JJatPGGEOwCcK6lcnO3cnN3C+ws3iPfEU64VYtp5D0z6yBGG7gdUZn0eJ7tZfcOchjwUMKZUfxSf2zgm3Rwt4GvEnlFpQPDAcRsAMoAfLf3jd146MPR3jiAoEFw7mN4qLnhTBiErgZehx+QSE12DCGsC+vJ56MgBgypkKpZ6RGTVLNA2ZbqHlZgO8SHAO6QHaNBx4F+QrGWtAOlOHtGC/EmxvMeQPYY2iwUP+sEc/UqYvtYKuQCSqFME/OTN3nxyR1/63/8O/zar32HH77/Ad/6l/80f+aXfoF/9y/8Co+uv0D56H3WcgLZOdCy2sPg5aXAlDAFSq5mvVVXmErUqjWvjSx4SSlBdAYK1MXm0hWxkeDflCf+e1PkhqA+YftJm4U1ztGmTFGfchf8bVqt9FKNPZ0dEHlN38iQ2WgpdZdPRDz5WL1Pg8M6huPisCsaTYQLpxDyUADTga6k32AgqbuDmqXzDJA0Ab4DzvBKiP3QXAzFxt0INvIE1LoBkuI8QIIPpXCRHeW6UVkc79/SZu9rAIwuB3YlAv0e/yyl7oItLk+knFqG+9i7kJqyNsYf7x2tq5E1vFa10BZ8//p9kZAlAhukWpGZDqDbzBDuqD0qZnt+xJzFhEQbfdkGhqvB7UM92nlErEcRKMknVwtCYRLLzhv6zncAACAASURBVJ6qhV+Umr0vR9dT2zyVUslz8lAXtb0x286q6wkt5tMxT+YOupwUVXE52/ay4KXJYj+5gUVSclDqcZo7k1dfvLxlOZ2YWMnVSlQtZYLDgfLSMrnf1BXZzUjN6LJyfPmS/aMnyPK5ZfGzen16GC9QC2hSsqe633zZ/g0HpIyMYbOfNsxyFIJEZLNZw8NuvLZHcHwi/v8ArrQC8JHmO3RmpNAEgpYtyLQP9V6fdPhsYLGNSXWOeD5aH0e4dfg8hWuHAtkPnqpKClWzmBtrLdVLgKzWfJ2oulLrDFX50Q//kHk38c7lz/ELX30XyZDdoiS5Et5vSYMNw7qsLGuhVEWTUGpB1Mq4N6WqeFxG03oG87dDSImYxhi3NAadBgsh9PfGGildw6rBFDELMGBgvnZrY621vavWxQTeltrU4sayaEs6oqKoJkrprig2HjhxCiVqy7KJCyCRGENlOEzjgIv18tgCVYVs5R7UA8hrrRTX9iXw7J7Js6M6/YkpLJLiWvlKWOW0ZrRm9jthnsRCO8OdrHFucRo366R6spoaGU2ruUcT5VJE0fVEWQr50VM3M1Qgk1JmVTsyzRpjoGSsjWmzZvUMJQtKaQC4IG7hs7IrUapminVbe/INdmbGK2thrZU8Cbtp5u7uhnlK7PMeEI7Lkdu7hXkSdtMEKVn1ENcml0Hg6t6LfQOXUq28ojMgUWm09iCG8bWtVTf3hbBmFkkGCu6xHiKY6z0rh8ORP/zxH/LWe9dcTH53WFB9Xs2VavvuALS9lmBDOQOg65/B4IL54BX8Y/vM+ddn0/ZHtLUV7zaCUXw/phfcyk3bPkifx3/W69X3h3D6MFYTxLMFulA5AEzUE7NEMpOWgSXWIhrpyquzA2H46Fy4jIGOVDM8u7lNnIM99Pz9Vtv5E4oDDWtFboKlhjSq4L5m9p58SZXMH3zvp/zw+z/i13711/mt3/o9/vDDF9y8/IQXz5/zxttf4Etf/FO892iPakbrYt4gSbak4AASzwLcFSGYKy+YS6p75iQZ8sN6DVpbkaES3AAywmIYgDG1OcGz38YGDaSi7YcMH0ecNiHcq7/6VRPbZl/Oapc20t6Qwb1GzojRAEGnj6bYJs4CaWLTQ7tVEPdcHNpM23cMu63JKQ2wydgS27k461On594TOetbH7gMtOjdGcbez/qBd2j/5ry13sNxXOaF1FxQ2YpYMoQ8bcB0kx8H99+zMUe/AS8XNpwP2kjcnznfh8F3+uctt8Y4gs4CbM7P+ER8tR1/eHjo0FrMgYUExcPCeKaFjBCX768oieNyAFS0VNZquy/PFnffKudsqFB7k9JpweZeEZ/7nDJ3t0eWZTG3V5nIqSs9JxJIRnRlysKUTNbIYsnmZP1jD6HPr39B15/I5hvWr6WsroWNS4d/EMQ70JrfFYeAa3Sa2UEbjerZNjKQ9pDA4ExLLfdb0zaJNG3UmNkMNe1KgL9kFUX7m1LymmubyB/rT8TeNa1gd2O1ovDSsqcGiG61eRwQNveRNLKIziRqaLbc1RHV5ncvQk+uUyo7hDQpL0m89daX+drXvsVXvv4Ou2czmk/o8xtYTxyYuHz0hDztrQoCClKRi8zuasf+YmZZBbysg6prcCX673E5kcCE7OeBaxtDmyeCVBesxfFKNXe9lHrchQn55iaZc1iFPVZinBNJiBeg795URiM57QYKsJXKyTVhTZNqFp3jcT07JFJ7KuFgxBaL490Bxd0xRLhZX7qsaPVAl3Xl5a3F1VWPKyvV4r9KUtDK3XpqB1ieMrvdzG7aOXkXd9WaUVksDrJmMovV9JSJORdqOfHt7/6QP/Xxx7zHO+53upCkgBbIk5WWIEFRkjyBVKl6A1RSnkjsqXUFsbkqx4/Quxvyoy+gnMwlLU2IKlMuSM1AIqtQmAEhZ7Ocq6/HxaOJR28lPvmpsJ6U00l5cfcBMPPo4op5lzkcb/nwk+e88+br7NLEy5uXrGthyhOXl09A4HA8cTidyFm4vrzg5csbdruJ68s9OQl3hxOn00JiR/CRnCanI7csp0SeMuqlT1Ke2CWPNVwXkHBdDYErkzPuIWeuZU2LLRHrZPSTspU6SS6kadRlEHeBD28AKU2hUeuRly8/4u//vV/l3/iLf44vX1+gWqkytYxziRhOV50EztpcntQoXExNneD7MATmc21bNDZcwVc6MxO3AOFCh2z2VRcoh6aDPw6CeaS3bzfHTfHhyPS9/Elq7qZjn6IA91bg68lB+vkxnir2wfAefza1NiKGfHRNo1kX4nwwJWQ2d3VWWGsfU1XPMJyawFhd8utut7GGSrhXjkK6bJJu4GvqIQivFNCtDYsh6jGdcYUOPhQFKZmFPhfcmmgADKq5fpLJ854V4Z9890O+/733+Z//xv/AT378M24PJ+bdzDvPLvn4Rx/zd3/wj/j2r3+XL33pbf7yf/zv8eWvfIFf/MrrzEnR5ei8S1FM+aUiyKzGM1QoznszmameiFDq5grPCnJrsaXJYw7VrFmJcJXdyg1Ai3ctansv4TkEfG3NMNzPD5FQMMdZbXJADuBVsfPGAWzE1YUlpa0frmSKzLXSRfJwV4XuBRU2MfzMUA+huG916qvdauY2sNL3gMRg3B2/h3TYe4MuTT8YdRA9yZrv0TEcR1tpqrB6yb0995CUZdY49aTzHaBI306mRKz2R1ghRXyuo6EGFsdz3n8OMaWjAlSrx3Jj5/sW6PtcazAs71tIkD5ZJrqFgq/vtyZytr2srT8QnkOjgSAIIKSHZqQ9m8ughv55TWpKX9nyMl+yrvyJYVVx+bqiTAhW5/nj2yOPro0vJV2pgp/XgMcpzhFioUpOmBxQ3G2/KFl2HJbCJy9vyZeJd197BMWSV9qZ6Mrmyc/FoB96CJhqQSqknNilC+6OheKxk8X3TC2LzVEGZUGysNsnFrEs8HWFu9OR6SGi+/z6TFx/IrAYhUuNGYYrqX3XrU92Nf9mwQWq2gCdbdytcNM28bnQI2EZ6Iz23oP+jiYY3bupH87R1yZMSDAK2xwtDtlu7IzU4/4CGEbh8K5ysXtzO3xcmIyEQDG22Hz+aMtSKaltzuRWs8gW24GWH9RpQnaFesj8yq/8Cn/lP/oLvPbkRyy3n1COR44vfsLNRz/h9njFF7/2dS4eXxCsp2rl0eMrnl5fcjXt+OjujrSzNBAtBbSqWzTD/cfnUIfYS3ELYgvoNnfAxiAj020Tuj3z6GxgJKY+JaEUP/hrxHNaoptlGRIOEQdm7pMH7lJs62nu0BFz5e6YSBChab+luxjhQmarxakhz5tiodSC5OxJAgqnk9eEdNC8LIuDxQkRZZe75SiJ9SUlS+WvpdDcYHLyjWMB/rv9jGIujWUp/N4Pf8ZPf/YxRZIJ2p5Qp6zJYoZ0oRZIec90+S7oEW4W0APpakKmS/LhRFgbyryHtbiMnUmyUOVEWv1gcICIC3KtrmFYFIBTKe5+W5vwYtlKza1X3N0rJbOSSpTpECsh0ZQlbo01xRMWd+jWS5JlHN7tE9PUrRFj2YeRFlqMEWbBNbqxjV5KaZ4FKdl7rb5m11iP6tLmROb0Jo6aUk5E4gf7sjZri3rdxt1kSW9CUZSaEIMpkWZLPhN29sa6NkWw+ndj/azqc9ji2R4U6WwEm78GC4O5XXXGGIIQIs01fkweM74i4pe6qyAdG9L3IS5sMAi97f4mFXZeGW6abT6ir68Ym9nC+3gaLx3G2H4P+owGR2CtUGSi5ozsduYZcHdL1ZPZuVzRkCXcd8+6EufMZq6GVY3vpc93u6/SXe+1xyl1cEJTQrZziTNXW4csgmXfVZmJJC0p+/lwWq2Sy/4Srt9gOS38g//jb/F3/7d/yD/5x9/l2bML3nnzKWutfHQ88eT6Ash88P7H/P7v/pCffPwx/85f+rN89b/4a1xeX1Ce/4h6fOk9mEgpc8og1eqsUhRmgZzJpwplsXDzdIl66vycCjkVtCZT+uDlehrvaYezCff+O8O6Dsk7bf95TFcJRafz76qF8GepxfjOPFmSLONDgg6J1GqtD2aiNB7oCtwz2SZFDOUA7LaAofPOSPr0ECk12YXtnh3lqdHF8jxngzqYG0hx2GbehrZOnUtXPEDM9y+n11H5Mm5pgZZ9PEBrY/7j1jtTcI2Ou41XabfC2Q4a9vcwR5EIKEWywfN9PnRj467v/qeqffQbS2zQl4zWyAFsKm5Q2Pa981rrdWrgK+zL8YR2umhtBzsNubgiUlgrFJ2ZssBS+OkPD1y+m0mXEzpNrAWWshI1vC92E/OcqOUIWtHTakkdK+Q8QVKXvyY+ur0xBfnrl1xc7pnxBD9xZkpmVZdzfRpW/25Zra/ztENkBpe7vBIxXRAHwWS4TIIivDwVTkWZ8wSrstzbFZ9fn5XrU4PFJELOaUM8eVN92DbJPZaout3Dw0HeDIvDl40Bv0omatd4g4PJ6oyz9gDo/gZtTE+bYNO2a+9MqG01tGgPOAmJ/6eplmI8nbm3+4LlnQ0ohNJ2yIiYGR9LUABe+iNPPufmQlBF0ARltTioOd0yy8fo4QjLjAD7p+8w7Z+SPvoEEtRk7nhVLVYuX+6Yrvbkiwm5MSE7AvktZkJaHIfFewwHlB/gBij6epm1sQySpLsQ1dDzh3LBDux5N7d0/rPPnZUwsPenlCm1sBbL4pmz00mlHRQiYgKTdg2zNiavTCL9xGiC5SiYOEBGKasnRwLLoirFRFQLkGzghCb2x+Es7Twuxd6r7r5bauW4nKhVmad4Z6WsK5bwZyIlmFJGknDIE6XAUo8sa0Vlh3BCC6YZTtXcRmUi7R5TdeL/+Uf/O4+uH/Puu19gt3/MD77/Q373n/xf/PKf/be5uoB5J1BXhCNVDyQmKhNKcdrW4d9KaE61yeJ2fD1/sfDxc+ViTqRSOJxWSk3kFGC2z3u4fFkdpeoCt827Vu0W/7ZfYq/4cRpWaj/VRwEptqnFs3Y340Jh1cK8s4xwpoBom7CtUeiXG9jsIcL2dzWpM9xMg0O0PjiH0xKp9yOuBvI0NeWKYm1bDrtKVFx89TW6nd6/NgleHrxpyw/vtTeQvRXs6e2eR42MvEqjHIJPfEU98+X4ug562ntdGhv8SzrAtJe0tdh2v3/Wzw3pltnhc/1j5nS0mITAWFUhrUhaufv4Iz765JbX3/2CxbJpjyUt9dxl0frl/gv33zWOdRjqMJOdFvuot9e57B4fDJ+3CiiklgdgdRd9WRf7mSe4fMrx9P+x92axtmRHet4Xa2Xm3vucc4e6dauKVSzOZDfRLakltSTDatkPBgQ/WYANQ5AAw4YF+dUvfjBsA4bhFw/wg21AhgTrRRYsQRIgtGXJaKnZLVk9kN1NNlskm6yBxSJrvDXc4Yx778y1VvghYq3Mfe6lAbENuB4qC7fuufvkznGtWPFH/PFH4btf/SY/eO1HfO03vsmDhw955qkjjjcDmiamKfkdmSN7tI5IWHP2/kN+559/h5urX+LzX3iBP/pHPsPRyTOQt+huS9FELD1Ij5Y9MnQUjeQEGgKrzS0b9zF4UCWhKZJTZX9YSyuj4du9NcqpB0oraJaDMUPL2DYgL84kEuaWIapNKVcCs4idYDXiCyBwkA1uwMTfYZ1AXiqhi/dZgdNB4JmlD7DwhBrm8Be5HAqyPN68qd/H9d8t7aZy+N1DMLb4+ZqtnQFfhbDalu0ZHrWdzR+QQxGiumJcn3/igTKlUpWXl+EBqeabmYHVJnY3gwwLOHrG8tpsmRlp7scd+FuLfWSeOtI+99Me2Jv603W66HWb6u7lE/3TJ87oZigfw6PL79dxLdAy8RKtjAhAClMaSXni1u27ZBJX4x5RuNFZ8EZiZEqZq8s9u/2OoYscbTase4xVpkIiIfRc7SbufXjF/Ysz3n73jM1qAyFYVtB7EIeuI2fzF0uZ2XFFCyk566wz1d733r3gwX4PfVeVKax8RC1pooAW4f0PLvn6995gSO5nZGH/cZ/Fj+z2E4PFbqE8WLdlLV+NotStNTud9/bPD8zTbDgXRqxFV+0f7Tvt2EUpUmYg50ql6vVFZugPDe0ySqTVqXHZ4+W1xa6j5NyyZF0XKbkCGZeWxkAUWhaz/3CrGcVlXUK93+tbA89e/1UNpLd+QyRYpk8tk7FPSgxKykIqW6Z8jsQ9RTsIPZtbz8BNowAUlClbNsRkTbPdUx+ZOnOKokTrYeiZHXUaU5QIMZAnyyiuogFCiYEQ7TsG3Dzf4NkU9YVa1ehDEiqloVgNX7YWKGAZIGueri3jBF4zGCIlQTd0rFc9MQbG3ch+Z6eTIKQSvAwvk8WMk4jQhcBmYxnVnHMD/irKfjdSVJtA09B1rFa9U5Nr7aE5GttdsvYMfUe/GpxiUc9PFby0ulAx57r2hUopk/OIqtIdD4TYsd1OjGlPiIG+F/oYmfJkzLE6N5L3gCwZQiFrJmuhG24QwxHb/cjXf/NrfOub3+Wf/spvcuvWMV/64pd59rnnuP/oIf/4l36V/+5/+MN85vMv8NzJDXS/pXBGiGsye3TcIyWQRdAY6LQDtZqDqgQb8gwIVOHiKnF6njl+aiBrouStjRMBdG+BAlUTxClKytnpREou2SPAjkAdBAax3plmWiy6P02T0VBlsDlQCi2O267HAlbV57DARc1e2iJlVFLLWpRSSJPdUO2NaN9XdziW2ZvqmKqPTxu/abJ6sBhsXKQpEaO1v3F2qlGkw2D1pM0OCNYmI5jA1vWsktukun+Nvy2j4U4sPAQpS/B4aE2u/X1wGvd9D41WWNjax77igZL5eq7tc3AbzVUAnSmEh/tXb81qpJHrUPXJkPnwUG5k5NpTrF+VFhJoX5aDUx8xnZ/z0ld+nVe/+xp//r/4z4hlD+UKgFydenGhnHZNNm4P/ddZ+Ci3a1w82/aR9xhb3M/hMjdfa93mej0b/2HRtsruN1j5RPTSif4G0DEl+K1f/Trff/l1fuvXvsGjh2cknehi4PbtARHhYj+x3Y8USeDiWOsBVl0PWfngtXv8nR/+PU5un/An/8wf4Us/82m+9OXP8aXPfZKOCdmfoVOPyojIml/8e1/jh2++zc//wk/x8z//x8lj5iu//M958513+bf+3L/Oi8/fZdAtEWNmoEpEyVrHtsxUa3tc3i5lCegcXCQXKHIDECvnVS0wqmqsv+DKnab2m0gK2YN/MR+Of/G5aXFPq1dPJZOKOkNkBpM1sAm04OocTGzDYn5XUt2bGfTWdz/j28qUsCBo8Pu9Xl8oddHx46l/Xls/XM+G1e8d+D9L0KsLHynbODNwfXgv4BURRZvf1eYUlZo/U+Xnd+g7tVu3jPAyYTADWLuwsGC0ELRlfa21krh9nwO49edFiu7Avi5bAtXAZK17lVpq4c+l1bnak2olC+2oonNQQqrfa2vSvDkTQ+aH9zjNHmqRgSLez7F+25goebL+vRKEe/f3vHTvQ/aD8sKzaz777G2iBh4+vORid8X51cQP3/2QB6cXjCkz9B2ffeE51qsBEWVKmf2USWPh7GzHS6+9y4PLHWMuRCq7x5yZoste59ramaxWAyknyt7sr3Z2z2EsaAwMffBAOJb1lwxi/RVLyjx6cMX3pi3Pngx84mSgC8GFhT7ePorbT05DXUT4H4vK2b/mn+SQcmmfLQ41IwpAWWYMWlSWeUGtBnF5qqVxPnQtmsfga8t8EL1mNErLcjx2CwdGupRCydnAmlQhlzDf58IgLJ/NARf9CbE4rec5uBdpi4mqUrJJ8VeIbSIsEDV77chA6E7Ylwe88e77rPoNzz9/F6IQh6FFnUSD9Z1DvCanzPQbxbKaoWtupLKQY68G36+31kMVxRVc5xe3eBr+/haLjv8YRLz+IXtLluA1n3jbBGmOQJVwqIvI/Fz9esSimKVkp0TXdx+Ife82Wbz+oQMt7JlMJc3PKb3Qr1b0rnaaciLnzrJ9u3NyKcQQWa9XBl6S91ELHmwoYr00PTo+JXsKVTClCscogVyyi+DUhTK0lg0pTYBwa3PCeugJjKAZa/oIOXdcXU689/6H/Mav/Rpf++df4+WXz1mv4Ucvvckzzz5NON7w+g/f47Xvv0LsImjkSKCjY5AjrLXDiCnlFa8T8ttQy9iFFjmV9qeK5ZQcoLgMdzBq7TJb2MZGzQweTiun54Y2302NttYHaVvMTUGXA4bA0rFejjMwUFadcXO6LGNeQaEFNarAVe3RugAB7SQVh/kcUaMyF81+fdEdXjtXyTZHc1PisifQMoGqrWaGxTyazzn3e73e1H0OvtnnS3fkeouix78zf+9JR30ClKTOuEY1rd+QpSmfaZTzOaQ58suPqJHARSagHuPw7ydvj2HN5bEWjti1nea//DpmKq0CHTGuKNMlZ+/e54O3PiDEI+tFplh9kTfAbk+q2vE2XHQ+/5Me5BNoxOCg5uB2nvTl69sMIg++7cE3IZhwK8LlFq52E+ePzvmdf/ZVXn35B7z79n3WR0fcvXNEHpPVMIXoDJXoQhXBx18mpcLx0YrjVeT07JJ337zHr//qlntvv8nVZeJoc5NbNzpurQZKBokD5IFv/vYrfOelV7j7iZt8+oX7nD645Jf/8e/w2us/4k/9qZ/n+eeeb862qHrg04DqEj1Vm9HmwiIo0sZ/1QWob7aYY6/qyUqJoNmZLfNYWHgjVGGe+V08Pm7MENVXPRMK58FAA07q7+TgeDXIvTx0XUfnj69lBK9dz7XfV7vcahblx3+nXfcCqMz7XGN8yZwJrMP3+ux67Cplng7Xr6UFZg6AmyyHs7/yOc0wX/PynEu/SdrvrWyoHBjF6psIj1/7Ey7dLsdvoNlnkcV7EqSWrVz7ou9KHTBLpetmM3Qxxw/spx6MyeonzEczJlnaJfpYiEPPbgq88+CS++fn/BzP8vxTN2CE194949HZJQ9OL/nR2x9ydnWFYsGNi62yGnoQmHJhGgt5TIy7PRdXV2iGkoWck6N6mcvKyvxMFa/hXkcQJavvr9HfT23NVWtBFfd6WrIkAJ1UxpiwnQqly9aN4OPtI7n9xGBxnBJKdewqSNDm1F3HjdL+ozVdrhE4cY659bALbUBJmSNUgM85+zk653WmKlRQ4AuJWhRRVGaRGcXT+8Uns5+3Oug5t8iU9SOL5lRiPdqaWa9993yRsPrL2cCUYkBSWNDVKqgplok7dLj8gVVAiEdzQtdqO5vBC8wZHwBRa/C9PaHPwvvvnvH73/4Rr770q/ytv/MbfOq5T/Mf/eW/yCc++zTlfEcchJWOhAB9xFpExBVDWLHu1vRDsgyALChODmDrtLdInwnsVPn3GASN0Tr3xQ5VZRI11VW1Qvcgsak82ru2BaXvOwMMmOonKoxjIuWJLkb6fmj+mCCkMbGXgpbIbjdRCsSuAkeLVAuJKJFGeS0wjUbLQl14SAQRUwENqBtyu/6u7+g6b3xLoesGa/B+GkCNY7/urSG19h6x7uu7t8+DQE6F0/Or5mBK6OicqliKErpIH1btekqB9fqY3W7LdnfBar3hz/6rf4TPffpFJFjGMafbpBz5za/9Gr/1m1/jO7/3Eq9/721WG+HLn92g2nNxseetN94iZeETN3v+yv/4X3PrzjM8ffdZ/sQf+zKfeeE2z39h5PZTz3H37l36QTlagyRIcUJCcUVZJeSMDtX4D6Q8MBXIRch5a+1oorIJGzRPiK4IGokxsd4MaDA7sR4Gah1vyYVcCl0fCTHSDz3TNHL71i0bX2q1DTdv3nSaZHXx579r3m1pamKstZIeGQ4ChCaGU52Y6Flxo4zZd2vwQR0gNGeqjn/vYTVD07nH2Wa9ZpoSqGVO05hZDZadTiUZAAzeDCAYPcfEKRfOg9D6LIY6R5Zu2hKkXNuu5+NqzuoQRAvKojl1BXV66JCHZYrjseO0S2mfgzrtUp/wDZmZ/G405eAg9c/CseKabVycr145Ou97nbL3xP1q1KHaaK9h1uDtZlbK3Rfv8NPj5ywYIAU0YfLMRr0P6AHwq1ujtPq/7R3SskFFXZZImANu/szqC51d2lovVx1hAzJairf/qPexfNTWiJ0IWrYQe3Ra8ZW/9cv8i69/m3feeZez80ccH635qc88iyiknAirnlwExChmWYSuG+h8nuRhoKwta7YvO27dWfOUbri42PG7v/4yX//1l/lHf/cf84f+6M/w7/3lv8CY9pAFGc/ojyY+9/m7PB1v8dW/+xW+/coP+fbvvczR7RNObt0k9Bsk3bbb762nbdLJqraVVk5Qx01AG+XPaiQwKr5EUgdjSUxqc36fJ4yZAHlrQcgbxwNHq55YJkI2VfDen3+BBjhLLo0FVNfe7OBHu745/FIKpYogBaGLHVRVbPd0NMxDpQaOg+pcU7cA/KGO5cV41sXvl4GjJ9FMRZa1brR53aiUba2uo3QOxLVA+xyJtzFqi7/T6u2+rHxkBrjVhIjI/HvU1soDHL0MOC7AYhSaYrfPyVg9xUWAfWYs4XoRPqVcOyIXV9mNVZW9nXgOWlYnYjF/DzK2fu/1viWISRr48wmLZy41QChCjqO/j+DnUiQWtzNUl8Msn6FB6sMRqf6wrVEB80dzVQNS6KUjhshYrphKJvRCToXd6Y57j7ZMV8p3v/mQs9M9907PwQXUVn2gi4P5TKq88vJ7nnB3MCodJ2vh1qbnmVsnPLyYGC8mVv2AxNEflJdRFLNDNkeUXJQu2vXmIZFVCZgwYugCGQsA9UNv5VJuU1IJFCn0Eoko24vE9jzzrkZy3NE/idP/8faR2H5isKjuTOWF4QhFKK4NEhq4MOdaFiBSHTzUrRb5Z6d11rkuYQZVllEwR36+htlw1b48VACqTkt1Y1iLn1vtQ6j0Tl942z6VQmnnyiQEoV/1bZI3wZlqDKU6XnZdU8rkXOiiSxFrMedXFXX58tLU/6QZ2hp5TJ6Z6Dt/gm4kc86mEacBLAAAIABJREFU0IqfFHOXO4WkkEri1/7vX+HV73+d00cPeO/tS8aze/xf/+hXeOa5YyRdEoeBO88+xaoXbt085vbtp7jzwpeIYYWErvkhq7735yWgARHLdnVB6KoaLC784PTdvgtEr9GaUiYEf+8SUU0mYtAitsEV1Qp9sLrMEISu69Ci9F20Xpf+WQyR3X5PlJGuWxElUrI5PUYPXls9J5kpZesh6W1ALKhhz2yaJmIMrJz22lY7tNEb1R0TipJLsixyKHSh98yUNDqGUFpbjY7OFpHsara9Gdhi4W26KFbYrbioCmhOoCb+U+vFShakOMgudqxCIUchdmuuHiXuvftD/sZf+xu89eZ9tpd77txeE7rAdrsHRobB1FdraPBie8k7P7zPmz94xA9feYOhC3zmCy/ys3/4Z/mFf+1P89TTd/j8F79Itwqu1vYAnbbolCEpZeVZAI9YFClOh8yEGFkPG7puIOseSXYfoevY3OjpxBzg1WqoE7fNHVMb9awfiSjqgRlvEaPd7JzUqH7No4k5GhJCE5Kqx8fbk1iwZpnhN4Ebyy5bRrHO5xAs8GOxEPHF36xb9pYlPlSsN6NnJFWVVJU+QyCGnl56D7YUc2odGRQJ9s69fss8UXPY7YlkzK2wDMtBZ79rCGpJ6S8cUlXF99dmFd35ui63qtcpsPX5zY7UDAkXx67/qm2IFhkK+/9yb20UQqDd01x3uADMfvSyyLIFFhBToPX5amAX972KVxALBwBSTQ4HkaawaQeumeKB3Q4+fO8BDx6cUkIkSzGVQc+rltaiYH4elQFTx0BztqlCWuZfNdGsep8CIt5gXMr8Xv0Ys/BRgwrtVpaiKQe2FJBSyCVA7Ehb4bVX3+K177/BsBFefOEZU0E0YgpZrPeqhsoAMHAsQPGxKCGb8xoEdbXQgrI5WXN885jddseH737IK/FVvvZPf53t5Tnb3Y4bJ0fcOFZONjfYpj2PMtx+9mk+++mnyQGONmtbG7qNBRh6oBP61Ym9p2L+gUR7dYVE0AlJSjq9ZBp39Mdr4uoYiStOHzzg3Xfucf7eIyhwdrllzImrMbG9mFDJ/OzPfZnP/dRnuHPrBL28JJ2fEWPvbAttQKJ2t4kYFT8HRXNEOuH07IKHD8/45AtPs+oGxNcMRZ3FMJfISD0Yh3TS7M0PLTg6z5aD1mPu1yxnVLV3hxTSBXiRRWBJKjOhDke/IpG5BdWiODs00Z7SfBHBxsh8yAVVtE0tae7TDBSvha3KfEx7xLNNLU71LTohRGf619Agjfpop3UtBReGExdAm/kP1T2z9SOXjIhJYKmvAXYdpWXNQpBGU6ao2cbObErOI4P3g86AiumQJgIJAz+ae2LsENn5/QklW0Au+jgSbFyVbHO+lES1ZoqSiq2BFVQPUjheb+wcU0ZV6MLAKkTKWHjj/R3n98955Y17aN5x5+aGq9Mtp/tTAO6cdARXiDf2i0uRCcTOlFKD26uJgVtHgVubjjwlrqY9XQ9ojyUDvNVaKeTUoTGjktEiZCLnF5dE1RYcq350WbTOSSW5WJq0sRhU0GAzx8qpsCSKwu7aUvTx9tHZfmKwaDV6uoim2VZ/LNhCmZsYSj74vX3GY98z582P4WMwLByWUtKBczMfwo7f9z0xChID435kNn31PErsOxM/qY6kzBnAWgeSk4GQKSdigNg58HTqWasjoPoqipa4uIeqfmmR6Xb92Yxh31mWpaqzqdfyxRhZrY8JQdhtd1YH6CqL/TC0e5im2pi8R3VgWBfWXeFHr77J6y+/ybDquHG8Yr8/4xd/8VfIVu5m8srSU3yp63vhi198lrOH99lvr5AcGXNHKlVdzCiCRtFMUOL8DnV+btM4UpvQz3UErkJTMn2M5q95K4A2TtLIHiUlXwAq2JfZ4Rv3IyFGF/bJJO/hEwQDlGIZ21Qy05gazdDq1WotSU8/mIBMSok0jouaSIM9AaPaxhDoYiR2gT5a/WTSBFXkRJSu7xg2G3IaQVydtBQ0G6UVyWaViyLBVAJzNlpXCJHMCqEgg2BK/SNFAn0/MOnIJJk4WJbrt7/5Es/83L/JF/JneeXVb/Lf/lf/Pd/4zZc5vrni1tExN55aM40X1sewGJCxcZIp2cRzTjYbbt1QuiiI9OzGie996xW+8Vvf5a//1b/N+qjnZ3/2i7z4qU/zH/ylf4dnnnuG23eeo/9kT9YVab9D8kjXXxLDkS3XIvSrgbKfODu7YK8FicrxakPf94xj4tHFFbc3K4bVmouzc1JOdLHj7rNPk3Ph7OqS/X7PyY0Tbp4c8/6H91kPa+4+c5sQ4Oz+OZdXF2yGgTu3bjGsBijGAEgpAdBJBMGi+0ssVGC3G60qqu/mYNHC9uSU2e/3CNLa0dQgT7U61XEzbGpjvOts7FgdarYaRlXQjmlKpDFRS21axtuDBUqgkLBWEj0hBiZ2dBidlyBevZIIZdmTTh8DhYD3sDv8LB+Ajvn7c0qwuqA101Va1ssO2r70GJhckrseo042D/cwRLysNVv+5no5Qf1trOf29aNUb6rK54tTit3JN7xvT7e486+eOUQgEAkqxLSzc8UODT0h9wgJLk/pFD7xpS8SdSLkAKHzMqLsgkTSLrQFHe0mEIqr0/qNLNe5Cq/dmVWp12T3Fer6IHY/JbhIku9fnfHm9bWDzVvBgzkxepDTxlovgSEGLneXdg/ZgnsjE0Ej/WowgFjqOPW1BeYyNbFg1mq9Nufe+9UOwxEnTwUudxf81f/pb5MzDIOBimHVIyj78evETtgMKzabY7Qo//Ff+k85OT4iSIeICeWFGDi6dZPNjSNOTtbcON6gCpfbC04f7Xjq6af5uT/2Of7cv/1n2Dz7HFcPTvnh7/+AV777Gr/0f36VDz48Y5sTZ2cj9x9sAeHZuxtu3VpzMXaE//2r3Ly54d//D/8N/sQv/CFe/NRnyJeX5MszDyB1SB7IujUnXkZiHMjpgvVmw268yd/+m9/gH/zDr/BX//p/widfWHEUJkuGScc4+iBfmd2Io43dZS1dHe/BlZ1bRnrRSqqNq4PAj7bxVGmAQcLBfloBqFo5RcGE4HLOHqzEn/VgoIbsw6knTQqMSIge+C2ELhAxfQZNps+AZ/Eq86BmQ7UChhpwr5k8jMdQWqA/WH/faHOmUvYtE+mBN1fVN2VphSCNPxKiuhJ5QHNdtycD62FNKRW8jya0ph0FIXY9EWE/7uhCIK+DZcem1FSl+x5DwUkgKFPoKF4/HzWgpadEkFwo+8T2Ysdbb7zDrVu3+dyXniJNE2fnE/t9YXu147kXT4hdj8UGFM07a1GkpgYsIRODspLIVEwrIcbAfoRX335EHLLrVWQiEIdjfv/VN/mHv/oS9x+NHB+vWa0jwjmESLdaowhJ9/6+tNn7mu3WUrkd7ovGc84uA5KOWMUb9FMk3twhkxKmSNHAqKbvEDoT0yvZGHdRJ/owoFEIrgiePYPoIXFqfbsFxz2wITWjbWNltbIkRfT3P02Ji/GSj7eP3vYHyCw+HgJobPP2K3HZ+AXTXOqiNB9j6cNc9x1kuQBf24TKi5853i2y5xm7ZtzQFlWbr3eu12niPItrrQ6OGS83ykUp2SNqC0MNXidXz1lphXlRqyU4wFqAXb82y1gpIsWbx0fLrBarJauRsRhjq2krDnijBDqJ5A7ioNZsKyhTSkBh1UXv5W4RdlUvihcQKZy+95CUMlKGa89sdiYtcyNUOWf1VyLRdR1lJpkJ1m/STmPGIkSZX+HixdZMR83u1PNZJvo6Dc/ApmomJ1eC9Qa4rbeU15HOVex1jBT2u53RH3MmiRBjduentAVNUcZp5HJ75U6MkWIK2cAvCp5lLVnJSdtCHRfUVsTaNpRgNOawiPJqtpqGGHvKuGUak7V6iKF6lJZZjT37XDjf77j/4E1+6R++zfe+9xLvvPU+N06ETW/ZuJQW8/Fa5FkcIJkMtkX3qmz9yRr6EMmlI8bAB++8z+WjLX/zb/x9nnn2Ls8+8zR//E99hude+Dw3btyFOILcRPKKPlsLkezUyxgj69ghnb3BnDLTNFFKJkZbDIrPQY3qjoI5NSb4Fu3+a3Ta3yvBFtHgQjIWkCgHwYRcMnm0ewoePKhgry6N6r3yELdJdeaLUcjMybLxkFSdylqdrHmuz3Zqpj3X6PYsXmXvsIvWesQy1QZ0MtYfLJQaRLKAQh9WNnv82goKS6BYT/5Eu3u4Xa91fHx7ki190uZ2a57xi31lsQdtv+VnTYSnZv8W89ge/RPOupjuNneroI79uxzu0I5VRAjLeiDxdyW0+VfE5hmYtH+VfI5pC+mM4xtPs37hS8CISLYMqPpbWi5S1+5fliAOqFmq9iBqYKztZ47cXOlaP59tZCW91Kxqkbk5/fKZ6+K44iwexZgm292OKWeeOrnJbveIVRyYdGLKmRc+9SIPP3jE5uiInApnVxNTysQoDKuBnK1mOsaOvh/oB4fv3i93Xi8tmHrr1g1yKQyDU709UHi0sSBhlEDfByLCEAeuzndI7P1RGFi6/+Ac+p5+6NkMxnIZx8R2l+jXH3DvvQ/YjRMvfPJZ7r3yBm/+8B0+eO8+F5d7+r7nYjex6k20B2AYoJSRuzfXXtYx8dXf/DZvvPkev/Cn/xDPP3OHZ28GNE8QAypWp49nZFIplBRhs+by/Ir333+fD88u+dH7H3DnEy+yFkzgBCEGWz9C9vcYMmERMHms5lCkzef6LJflNvbX4k1fe+l1Si1HZB0rVrfvQm4ehGgjrAI59UCMtxUJ0tks9+Nqmee8VEXaxUitY9gorsFEXmTep9K8LXBeg+W1P6MHcLUG53ob8GprZyXPUkFynYMoISjWj9PU3zPZ6frizeHnhyVigFYr30Dch0lWNkQPIXkm0689SCZQCDmSkzDVns8S2ee9XWMIDOuBG7dO6PpgLa6K9SE2hV8BrNRiGicLJKvRN7UqvLty/JQSKWdWfeRqX3j1zSt++3s/pBNtytyqIHHgw/un7MfCqo+IZsoEO2dkNQG2olTBt+pm5VIzmXZpaTKhnhefvUGHZUPfP78kCZTtRCgwuY+q1DRMHSdONQXI2QG6nWvWMJkDfDIbSPeN7LjB19caJKijOYTliP54+yhtfyCBm/bjwQ/ayiqa/1o55tSl8pCGUX2s68Zv6ZjNETVTaquAs55YtV6AGnVPjUY6N002+tncp8/bI4hRKefMlR+nGmOpUtwLvvw1h7zxMlypMTQnclZJM7G+GXDWG21RxlJIydREd9udR1pmOoqqkqbJ6yicrqkemSuJccyErqPrxTKrwbKwBtwCEqxWLCJGHZVAERMxGK+uCNGamBd/E6VGtqsITl2AGq0EpyWaY55zMifeDXfQxcJEXZi0NQOea7rVgaYZc6MszMsF1CYDDpbFagjraIrBMn/z4mTXY738dPF9ZdyNbVAZgMrgC0kIQh9swaRkLi6vfGTZ+AlFQQK5JCQUttutAdaSCcGKDVQr5ay3mr/eoqfTVOhjYFit6PpgKnO2mtMTrHdjCHQI5EKHURSDKEX33Ht0yt//e3+X9z+8z25nWbCbx0domNCcTdDB01hx0S+szg3LeAohu7x+bzW3Q9cz1LZmQM4jV2f3+a1f/g2IgdAH/o9/8Dw//TM/xV/4i3+eo5MVV7s3ODs95fXXX2fcXbDtoI+R1bonBxtnwRdKATbDyrOA5kSGGC2KX7NtEhFXSFNR+sEyuorTtYJaPWowz7kFm5DWwiVnrzfuu9Y/MIiPRTdGKRu9swuWMbJzz/M5FyXnvTvABcmKegQ8+CKnWHQcaP0Z7VmHNhck1CrXwrBeW+abyqpYAI5g2f2QJ3PYikWcS1WUJWABo9JsRm05MvdCdLPDYbbucL2d6T/139eh4Qz+ngQy7fnNOOzHAdHmWvo16eLaFsit/v9aRnK+uoUX/IQsi5dCtfKHxwAWy8CTG/yiiAazHb4WBQk280/vw+pprrYbzjnm7vNfAt2Sw0QVXbPloxw82HZJUgGbzE7tYu4t14lw7acqwtLKEBw8FgeJhWqd6jOF2Na5Q6DYriVZTXwpyn67Y9zvic0O09ZRC+B07Hd7XLi4OZfjOLYG6LVUQKR3G6/Ezlpd5FIoyeb7jZMVqYz0naBZ2U/GthiGlfWHK5lxGglBWA9rdiU51duF2TRQdGLaJ6b9FbtsdZpSOojK7vKU0w8+4K3vv83JyQbZ7tmOe7LXRUWBnsRmiGy6NVlAozAlNQc4RoIWfvj9N/i9b36bk5tP0f/J2zx354g8GXU/lS2qkS4GuiFShhUhnHD6SPnt33mZb33vJVarwld+5as8/fSf5eanbiKYcE4cnN6Y/S0fqJTOAAkRD4oJaJnHvVwbU8qBjzX7R9JohIs3b6fxgHYIlmUu2ZlBbku1iFN8xer7XDE1SnCfpJj91oBqMOESnyuwZInV8RaoteVtui6v153AyuDWUuj7AQtYKyn7MVWBYnXkKCVlLOo4Ig2Q9kjpCGIBcOuTG8ED3KnMzyOGvvkPYhxHexdRQCIhWb9BbYMeejUrPQVBNLLqAuuhcz0DA3eTA9TOA+Q3jzbs9ntuHPUU7VjHnmks7Mc9Tx2vgcLUg4Ro9PDqE4klBlIq3P/gnN0uc/LUmil1fP/77/MrX/8Bt/pIF5SkimL1fRKg6wN9J0hS8giiHTlADjsLXJRZPT/ESJSISD4IhlIiUgI31iuCdJxfJS6uTlkP2li6UqPbKmgU0mTiU50Iq1VgHOFoiEgfudwXpjE3X7Vo9ezd1qnXM2JrWLUpNQted5dwMOQ/3j5i208OFp+wNZzVgJZNihBluTziySb/hPZThQfLuozlNmcS5WBBBq59bueItQChRfKWBjkA2YNX5liYwTaTXByQllKs55apCyCtXqreq7bzqobmQM4XvbjO+sX6AKrfGEKrd8pa2gSqqfoKTnM2yluMkdi5wx2sZUVKk7UzFqwezh0dA0XulLgQD8Gij5MoWqDrglf7KFnDTBlYZKtazZVqqzWqyZ/SRBkMQFIK2bMzorjB98Wyfdk/l9IiSyKlyWgvo01SY6burBXPGs8KkLaPenStgvza48kWMml7SrDzW8+fOQPd3mkFt8xS7l32fooRd5QS260BXRFz66ZcqXwJkULXObAoVhsRY2QVBxc4GCkl0cdahI4bVdBsDbZzMrrt1TjyzvuvkyZhc9RzdNST90rpC52GBuTbPSy3xWuk2PEDYmKPallrZUJEWA8bYgfS9+ymPVf7Hb/3ey/z3nsf8IXPfo6bTx1zvn2XR4/u8d7776AlgdrCH0IwUKqF0JnibteZTmgpBdJEy7izHFr2/ItaptECBrHtozo7uNVo1EzXQTRefA67FTlcsJiPR2UY1Hk7H7Fms9vkdIfNsvJ2vFrjVPIiWjsrlbSHbkI+cXF97X8INVpbY94Kmvw3LmyAOerq7+1fbpsHQXv1/vcSeNA+u/4tWsCm/abexo+9lFlM48ddy+He9bdLO74Aslqv4UnHWAAwuf545OBnI7H53ZYOwt6PKgTNyH6E4QYTN7iacouiG2ALNDLyIhPEtTMswbjR2pbBRFk8tGv38YRH08obBIKWx4SLlmcvB7fq614buyZyIbmw3+4ti5Gdth0ijx4+pKTMfhpNhAxvLyOBfdoZuA42t6cxOcuinkbn7LVrF6S0Z8qJPBXSVMjZ5nsKVkepKNJNSFBStvVLxUCMCX51ECMrzP6SioNFQSIUMUrk/mrL/bMtd24fsVoPFApX2xHNhVUolCLkEq3OTA1AnW33xGABrRvPdCCFEnuyRErXIy68pYKxPmKghMA2TxA7Tj/Y8eaP7nN6dsatTcd7b3/I5dloQS9VcpqcraDU1sIiulCEhmq+RPBAeQ3+VE/5cDCIf3agYgrN5j2J2dXGh6i/z1gtittPaeuBMSeKrwHGnGk1iz5krW5eHzuBtXXwkV+phizHH+2+D2mIdS23fYOYSkBKyW0l1BAJUnxdbYuAP5d6l+Z81KRD4yAFIYoF0g00uTBU0bnmPYqN+SyUISJYbR4qjCIo0SmwxXqUqqCaUIzKmf05ZbU2VtVEhCj0XWCcPJsnvsY6icFuwQOcpZDGzNVu4nI7stkMpAly2tEFZb2K9KKN5dIpaFASxe8rImLB/+zhSRsr7vP6swnBnnnx/sZCFYGLXG1HpCuM2cqRoijdumccC5totu7Cgz5djEzJQOTJpuNhDmw2PavNisJkyu25Bj9qgsHto85vSKnmyQKqufqNSgsqfLx9NLefGCwWN3qUuRC7yLzARypGc8ecQ4cPmQ1LdQjNkFagOC9IS8GCluqmGuX5mupiVx1Jo2GKKXXW70pNgS+yc6VQpDou7p5UxFsNbPYIVwgG4qg0HLsaVaPi1XqZunhLrVnIRm0J0Rares+U0u7TmngrWVzU3RVfK7ipM8mU3mqkO9DF3mkOc99HdYqV2X4jPBUmlOABNjP2ZkQieTSTI51iEU/1NhrSsnXVgbN61NnJyyk1wGAAtRaA1/oFz/KJZVBtHxogQ4To776BxMUKa4/J6sRiH0lOfxCUkvF+kDXjU5y62Jux9Exlybm1ZHB5u6aYCdlrKW1pbaNALAsb8Ho2AioGiLx4zJ61KBAY+t6fdx0bjqYFpjyx2wmyqrTV7OtG8YXSrx/l/qNzpjSy2yVyiYSonGwGwo1IVmUc93RAmNak6L2L6mIagrXzKC7ksgRQQZnEKby5oAm6Tlw0StjtR3RbIGS6qJwcd3yqE9LpJf/zf/NX2LtDNUjgZB24e+MGXZcaJdqiQ9ZzELH6YZVgokxa6LtIcnrbg/v3CdEULGIXLDOYhZKjiWSpZ9w1EOms91MxwZ/sUfoDMEYFleKKzJZ5rMIPBlTtvUzTyDhZraPRxWnPruXC1VRNq3MWradHRZzklNt3qr1Y1iahmBNMPX8zLWiBqHsAsgwULFMWUaJ4qlcTMJEJ9h7LITBaZg+v1ys+aav7eDLg4Du1uXales0ouD1YHv/Qn1m7v0qCk8PzNfduJpDq4ttc/7naf2xOHYJKbe8ytqCgLrJ5TmN2u2JZ2kzoCiQbVwRBQweXF3D1CLnxHGGzoj/qeOrOmhsnPeTJ7f8szCNx4e3Xx7J8vgcPZQ45mOP9Yzygg3udQwnia4hqzVQGAubsFbn2FOuPqkStKuHKOJpCYwiQph2DC7TFrkMQ9tstnXQcbQYosE1bqwVGOVqdAJDVewmj5JTpV+v5zUgkCxTJqBRCtD68Dx+OxE7og9nFRGqq5BQ75mQjnrTde4YhopiCYu1RKRKJIZLK3mlvka6D9ZH1AR51pOzN6YwE4mCZeqvVEyJqvU+7TAk9JU1cbUcu3+rZ5Y7v/Na3ufrwEWf/yk9x686GO7fvAJEgI5cf7nh0/4zf+Ke/w3odER147eV7nMTIeqNI7rm63BK7CQmJkhKkHjTMVPpS6/cCGgqddG3MFFe6DA7Mis/HyhpqpTQibY1qtqUakoMowsLfAErp7BkG8y9EI0GNwYG42rokAw/SG3CMwfsM26DTUuiq72LNBW1t8etv9FY1hsWs5l3aHKVeUQDUGDVXV3vOHp6SUuLuc3dZb1YWYAziavTQd531LlYBBjuv7H1uC5TefifJ7ZnShx7Fr1dGmzth5QAvE1XpCYyq9CEgfQfaUWIkl5F9SCiFTQebbs3bb57x3dff5K1Hl3b+XDh/tPVSCDHhpWyZ8dtPHyECacxMY2G3ndjctLDHfqxZzIwQuLrcec/hQogQY8fVlBAC/aQ8fPSIpzeRVRRCWRHFQCu5g6xE2ZsP0tt6W7JCUdZhRRAhVeZX9S1LQbFz916TX4JSmHj3w63PXehXG1I65lN3Ag/uX3Hr+BgFdg9PoWSeu32LB+c7+i7wyedvsX1HuXl7ze2jI7a7Cx6VnQchZhbDwtB57WnwBM6sbhsXhrOtaePH7TM+itv/JzWLywj/0p5BpQSZs++BKAd50qJt10UnqrGsmywcD63/+cI902scgEkFGJapCDEQuh4thWlKZox6o3JV5zF7wf6yGH15TzktI2aZ2HkbjbqfO6a44zkrm0m7anHPIUglpVVbO58rBBPBQA3Y9qveFo/aczCEBkar0Ibg9yidO32+ijQKVXVwszvDkS4MqCjTtKOkwqpbmfKYi8RolPasK1gUEVSwiNxSuQ0BVzkVrxcrIoRSI0pex9eygtCgvgRqBLWLXeOxm62bwVYF9AaoPDfmPfLEi+DVOQwW0fPsaP2v9i30hU/w6G60qCYKsXdlPBbjs43NYlQpW2YBiNJ5H8UWMjSQXIHBws0FmMZk0bRk589SDNQIwNTAd3RQXootwKveeiIlLXTOpvG1lxwTUipQdLDpC66d2iKIwcdTdopkHif6GClpj+hAkK6BaoioFKakTFnRHDled9x+6oRRjE4WSqHTaNHRlOdzicdWnI5cx10du+vNBi2FnBMXl5eQk4+Z0EDbauhtUfPnuFpZvVQnlVo9O1TzOJIWETexhVpFofN+bivaMuahTPVnVt/1ch9jMnigpGYOitN6F/bheqS/Uo1iCAubZo5+G/6u6qyiZFF0dMDmapXa2zNtDZ99yoXrqab5VT+26RP2fdJndWstH4IYMMfBYw2ILZzAg3N7QGQJYuYMZN3/eidIOfi7jr4n0Vzl2r4HcPMAKKvfh/gRBaG44GikyEQXjkxd2DPd8eRZYEUsgUF7NsMAMkHOgCswokCCxTsO1959DS79y4TGq7UO9V9CO9Y8h218zxqR0mpvDx6lA0bElHuTwuQUwdXKxLRwBoi1GrLa91prZjdgb6Bkz25XpVSBrpuz/TF2Jom/Hz2oYWyAIAY6ori4GUZ5FC2trgwCXW+BuV5WpJTY72twqzOmBIVURlIZCWFDIRswcPAVJCHdyhQVVZFiomRh3VFKavWtvVMw93kv9i0nAAAgAElEQVSymvligbS+RL761Zf5xjdf43d+9/d54ZNP8/N/8suUrKxk5MH7F7z31im/+E9+l+N1T+4zYcrc6oVVF/nwvQ956403GXmBVQwWNKxB0RoAhQbuatCgflbXJpbvmGo3Dm1Jiz0tsmtu8hYv306Z1ajTQu/zYjTwmqOtWlqQWAha8MiJMTRF0SxEjRQmQ68IKlavX9kgudbCtSC0B1S8xYi6SrEBVTm4vFwKOUOaJh49PGXcTzz9zNOmgt0yUnY+zXbc7AHGrhsompAQ2e9zy1QFEYpkui407QAzPAnE6Lq+2vszyogKq67j7GzHO/fu0616nr61pjuKaFFOhg3P3rnL7379jG986z7f+sGbLcBW0iIbpkLt3xl72pgrxfxFGew5ley+WrE5kT3QXev6OsxfEoSjoAx9YbNaEanrWSEzEnu7v+y+saoSMeHAEufSiljLmby9Wg2Y1/rVLnZ0IZiAYd+RiwkE5X1hP05I3Fjgp3MfWYRAIKVMjIHe29wAdENHLoVp3HubM6c9l0qid/uhQM1aL8Z0XVvnQEi19B+DxY/i9gcAizA7Y/WzQweZg4Vf5o+Xa6pb0pZRmo9mu8ryE/8MgUqFaPLU1aDOAMzWwTCDLD/GYb2hG/Bg9VQl50WhrmfLPLpcJ2nwjF9x5x0BkqlrhRBanx6tEbd6Iy3zUFd3Obi/9qTM1zEw0epv3Ej7tcfojrGaoQ7B1a4a7bLWhaoVqxesXkdMVawUq8uKatExRZuMurHqHqf8Wt/igxdy7Z0tr18WY6S+/4UjeT0j1AIG4o/Ks6SLNh31ydWIrK1YZY7QVvyghxFZqEGLYIB+Ac67CGMyifgKxGPsGAZXWa39utTVvrJdy9BH1quBUhK51qkEaUGR5ZaSgdgyZjRZBr0Ea9VQb6zW8PWxKt0ZwQR3EsviXubxof6cHdz7Wm3+5SJQUJ9rNmqOlEC/srYwNnh00Yx44fCIU6WCMgzGIrgaMyUX4qpDgTHNCnjzG6pkOH8WYpkCS/4EonSsV2trcu8y5mihaKYbIn3sMOZBIUZxMmAF6+3ILWBDHV4Vpqs5QPVZ1XGIyrLv9mIuLkDHE3x9uwU7Y3Lp+8NjtH8cfK++h+bdeeay5OygTwzMO6XIAG9GcQVDUxFo1xiwd2X8wydc6AII6hPA43Wc+Pgu/gwO+I31CSx/vi52UX/Sg08e3w7XhUPgKYuRc/1TrgGxJwOyx8xRA1FA8NxmzrC/REuirE7oto/IXFHSKX1n4kfZ6XK1Psp6O/Rcf4JWQ9g8n/+X+37yJteeR/15BhfLe1/se/As/CZ9/+xZmhroqOOqUm/F1QitfM1q8W09rXX5C5vhtrua2mqnp2QK3eM4klMiRLHaQgJS2RT1sfi8qZliqTWkgq+j4qDVarjUs1YiXvsrJuNtcz+iEinedgkXNAnR71UrELL7D0RKEFYqaNAGuPqu4/Jyy3bc86PXRh5++JDTsy0lKz2Zq4sdl2dXkPfkXSaNhXUMxPVAlsh2p2hYWT9LlEKm83sttVmeSFubW4DUbXg0FGPPxNf0ZdDjxwo/LYbak2ZAHTLNXmo7OSgkzQQtFr8rkdrCrIqtgLFBCJDSZIBCPUB8cCE0X6YG47Saeg9uzEE62ngKATbrFbdu3mS/Hz0InGyM1nG9sC2r9YacYEwTEuHybEsM0A+RIQQ0QSoG5IIL35Xazxn1MpM6kgWRTC+wXg08TDvu399yme5z8uUXeebkhHE/cnq6Z7d/xKtvv8/7D84Yd6OVWCRj8CBCyjP4ExEPmC4TJrPQTlN31WBjecFcUrX56iqQjKqIU1lroFVRCNHXtNDWdInVWurMHdLMlC0YKwLr1YoQAuM4IkFYDSu62JFysnvwWalF0KSUMnG17xhLYe9K47b2CeM0gQsBXWwntCi73ciY9ozTRBegjxb4tdIWW11ModczidD8YW3+ZTWd0pIDH28fze0PULMoT7Ra1Wg0p9EnzaH9kwPFvlkBSRd7yAG+XDqGNSu5BJgOvRrQExFv1E4DgCJGDVtOagOPQgyRruuYFt+vf0AZVgNFlXG/Y+htkagFwxKEnTfiHgZz/Eqx+kJysahnCJ51MQNq/ZaqcbX7zCUt5ooyjXsTuqnPwo1wzcCpIyQJQgxqWEi75kxUgRxT9bMIU4346VSstiZERhLFwXdAvB6rgnG/nBmJze9/8T6WgG8J2uu9tIyyfaG95wrQcilt3+D9EbsuOIVYqH09BbU+jz5uMjRAnYHsDlKUmhmt57OWCAgWyVSjf2qI7NPODHcBLcrQC8N6TSdG3s2lkBC2uz2peFS871lvBoTOFNAk0HVmiFWTv+dIUeHy4sreoy8KxQU3ctKWsYouDuPFE7YYAylboCJI18awCaqA5IYMZ+exZR3MSDfVXMN+Rl/tIn1nLWZQ9YR4DW4UxCO9Jn6rJOBit0Uxuq/EwHacbMypK8lVsNnmYh3bCqWKCZnDGETYHK1Y1tPmnBnHPfv9HvqBfugpZLZ7q8ftQuToaG2l/n6vRd3x9fsLtXeB1gyetsczZ78MdGkbs+VAga0Ol0ZjxSjdxkafwUJVvkU9W+0Luag5L2jNZNZRX7PiAlLIYWX/fvQu6cM3uEg9oUycfPLzdDdumeODEGoz6qYoCO1GcP++Abj6XGwYtbkORlcClkWHlq2as2TWxmH+Gbx/nBUcHYC5iidDxSl13P1YxLb85Mc7BNfd0kbh1ZndsbT5IP5WLKjgDVIbyFGESQJd6BFuwHROyELZvIiu76D3voMSCeM5q+MVfefsgBChOFgUgA7EBT8WDnN19u0SZme3jqEn15b5mFAHmy3QYn8sw+sVk/67ZfuooO5yzgbVTIYDIonRugDkxH7ac7XbErpIEKUTgQCTmtLouLUxPmYo0pswVGXn+K2f3Dgh5cTVxVULmplglDAMHR2RhELIoBOqgUycx4sHLSMB0YiWHSJKVqP2r9Y9aGG73ds9ICC9tUzwusaSAyXDdmsBrS4mirMWogfWyi6B194ZMO6sXVMOFhALClj/31u3jMqYUuL0g3PuvfsSohmhI0Yldson7g4oHVF7IJEkMU2FnAcfp9XGdojTceONtc23fbKWPkHQZMwVAXRK5P1oGRtfFBt1U+oaJ768zkBvtjPiSRsHeGpwtfoVIqB5T07K2ek5F5eXvPCp51kfrQmyseMVIYSaDbSa3hIuCHLCS9/5kEePTvnUF+5w97mnES3szy4oU2rlQA2UtjXNsllWaiKtZVoQWy9M9XoiBOFovUGfvsM4jnTRAMZOjJUUaq2hjkjoyHngO996nTffep9nP3mT9999yBe++CmeunPEzXUk7zOPHk6cn5/z0z/7aRBhmjJBNvYO2KFYz8w+RlIXee/dB6y7nqHvuXV8zOuvvsXrxwPjOHJ2vue3f+8d3rp/yVv37jNebbm1tn7TRSNJd+57db5m0Rgxxk6ZwY64ardlzAUrF/YWLWEOyihQvNQpayIVJapBLXM1rHa4CkRUzo0pu2Zv7abEkslZ2Sdvs9b1dH1t01HVxCNTUfb7Qp6EKk5ko8x6Q753/5KcMru9M6AmC76clR1IZDdlLrcTXS48+OCcKVsfypNN55ZRbd4tggkiVu5ittGsZyrpsSRBE4H6ePtIbj95n0VfqKqfugSIMP9VxSuWTuR8DGn76gJQzp8vwaOdtCogLmkcLXPVJuqcDbMapWmuIah9+Q4W8XlgV5Wmg1omsbousaZKiHcLrpmD4DArBKs77LpISpU+OUedlpRZ6wOoTkV0B80Xg9hZaj5NE5WuIFJrLEujMVhj+EIssTYmQ0WsbQieMWwvR+k9epzAolUxkxVyo2nY9ap7OzXiQ40SLp/Z4kVKCK2Cuzrp6o7SEnDPPqM+dpjQxpLXJSzHAYZ0quNPfd9aQUkFrDT3vJ5bqeDV3h14k/ucCUP0rKvnq/wW6+26l2rvLxjQnJVu7Tgx2L+DBGvU7vdQkhJicLozdL0BzEoJERXvZekQKkCISgxYDYwWTEa8tuuIBmysz4TNqaK26AOqloWuEuDLd6UKKWW6oTOQ4xQVKa5eqlZ3YouW1SumbJQaoQOJ7Eere7p1ewMC7719xhClAdf2uMSjnP5OUAedxQB1COb01/raruvaWCm5cHm1pQzKet2hIuz2BiDX/cBmsyGEjpQnao3nct4ezFkHgqq4qmOYHTCpYwQapX1hC2rOrWZn7NOFM9cG4vVvLEZ0G4uLMVyPg6CyQlXYPXiTs5f+GffOAl3e8vn1v8vq5DZZRrI3lUZBKnOsVLAihhoes6qPb8Ki3vGaNLnPNIuUH0T0rgE+EaBQ+3QfHkGf8J3FcYRFjfP8uV35tc+l/c+FsZZHXYIyzF5VsOUgCMQVKY1KWRCyRIJ2JF2TplMCRyh30f2KMQ1IgTxFxhJIaccQC7kIUXvDnhGU1OokHxc0un778zuXH7OrRznm11f/LF5nEbx3ozTkJiarbc7k4tEFzH7VurIM7KaJ7bTnar9nyB1DHyheQpEVdmPi4urKAh1xbe0jbHQ2GyIiHB+fcHp+yna3NXCEBRS7vkckUqQKqWSKOE28BjGK2O9lAk2IdlQkmotC6Oj8nEMXKdIZFiq1rs9pimIAdRwnO2d0gISQg83z7H1eNU5u8xOShJzVabJu+wNE6enEMmmrVeB2DBTZ0+maynI42+0oAusgwGjBghKQDGcPPqBM1v6jlpZoF2HVm8p62rNer5iKMwMGA5hBYb/d0kdrV9TWqYVCumHFuiCWts4deCzVGa8ZLKUFnnMeGfcT52eX3L//gGdfeIZh3dGHwTJxQBz2BFFyWlEUYndFJ8e88up3ee21t9jcOeL5zx0TKYxXOyuFqbZTQCR4T+toGTxV8zeKje3ggm6NDQGUkpnyyH7cstuO9OsbrNZrzi6uTHymighKIUa4f3/ku999h2/9i+/zhS8/w+npOeujm1xeZZ46FkKZePRh4cMPPuCnf+ZTFvQMDoBFLOOMWrsWDNS/c++UIVqw9JlnNtx4Z8Ubb93nlR99wOU28Y1/8S5v3Dvl5Chw1AmEzg4aIY+FEgv9EAkhMo6FlAt9byUZ1hvSMraIMZKKBqQIKVkNcRXymcV7AjVOXrAAaEliLV5rhhFTDi7ZyqiCBMhWPqMpm8ZF9JpsoIs9IoGdZ/67zhTjL6+uSMkEp4JY+w3RYIyyWIgSmfYG2Haj+a0BiNpZkGhKpJIQ6bjVCVNRcozEzjQYrCXYXN6lxZg4EgPDYMA1ZbyFyKypMY/n6vM9yWB+vP3/vf3EYHEIkew1WrUflRCqaCAaitcEmVpUjZ6Y06VLu7iQPgjeTNp+n4u6WucMHCqtKzgIbdkWP5KEHrDPUzJFyuQyycEdngoY67Hqscf9SM7m+lXndRgGFGU/7W3iBpphyN5XsVSHslSwGVvUD4/wV0czu2xz9YJqtDzUPoZ2d6Zu2vfgmnx9F1kfWZPdq+3Efrs3kZ5+4Go3Enp1o7NzemWHJqXvA/0gpDTxKCVqKwxVpYoTdRVxISAzJ90uceH4+1OuPeMUjHqZrQePRK9ZzCaM0LlypKrVudRehrX2ZOkmdt3CqSeAhgaMwbNBapSf6lHZwpSQGh2tjnyl8WELGH57o9cnqggEEwVCa28+XxN8oaufZa1tOYSj1RGX6YqSE0KkkzVG40kUsnH+1QILKgZ6tRTL5sWO9dARIyTPNhcV8qhuwOt7t+ijJUstk5pVQb2dClA0eRBTKKXOh7kOwt6vpwVqvyYigUAXI9JZ+5lCQjVY4EAKmgtBAwlvBREKUROaC8frI8Zp4o0f3COVzFNPPYXVK2WX5vdst7cYafNBbQGVCKUkSrJxU2umqrMUu0AvPc/dudUWmqLQhx56C8IANu50xgs1s1hSQj0LX4M9FVDaWJaDeMdcd0N7bi1CX+cj8/y0HWavvqRZ0EkAYgWSSizWyFmOjNIljIChPSUR9Rbp7JR777zF//a//n1+/+sv8ca9B9x5+hn+yy/8RX7+s3dR7RmymvpuqcGJPans6aQjRKCYcALRHOxW14gtyHF2+43+i/Uli6UjB8+alUIO1nJEsLlRQiBSyNk0jOdsY6gPDFQ8wOP2341GnAkCOIeNylnQJK4sO1pGUjvDQZJBvW2NJpBAUiVKh+jk97Dy+r5sCoRY0/DYJfIIhEwuW6NglYG+W9ng6EzE6K3v/ICv/IN/wg9/9BbDOtJ3HTkpZVhxNAw8uv+I9998g09/8oT//H/5a2S9pLBrGb06TrN40KhkghQTIHKnvrhjKlqfR332C4OKP7+S0WA5sBCsJpGixNARytzkXLGsS9HU5nOYB36zjTbuI2R7WyUPXJUNZ2VgnYQbArEPRAYTydqPIIHh6NjBowKF0HdIUcqUzMle9cQIXR9ZHx25jY100YJX4g5ewGzktgSrQ84zoA9iTm4OYq1gSg9gLQDEWAVGx7P3HwGpvyv2/DTburTpoet7VgOMafS1xeueiP8Pe2/2e0t23fd91t67qs45v+GO3bdnkt2kOEiRbFlj5ER2YgRBEgNJgAB58kOC5MV/Qh6DAAkQ5MEJbAMx8hDFgYUkdgIlkSzLGihSphhGFMkm2c2ex9t3/k1nqKo95GGtXXV+t2khoB7Mhz5E83bf3/mdU7Vr7zV813d9F44GYTGBQCWD90LOEWwEiOKaltRK1sDeYpghb3Xfp0yJmcYnsiWji25J2zZs1zve+MFDtg9heU1o2kh0C0rv+Ef/3f/NgwcP+dKXPsXTT17n1dff5uknn+TtkzWhEb74/A2e/6WfIPeZvNOqaZSMNEIrqiVw9mDNts8MBw0H/TnLgwNktWLcbWm7zuycxUgGGjsX1IaT8RxzIJ7lM4Un1xsuHpxz+4O3ufvBHQRH7xwPzkfOznd8dP8h4wgvPHeDT33qOnfv3eX05IIv/9Pv8erLb9EtAy996UWefe5ptttzxmGkz4WYRs4+OGV3seWocbz03HVWC8/FkKEsye6M4WTH7TvnrI6XPPPsdcZhpD04oikdH5485P/9o7f4lV8VjpaqlzDmUWn5uWV90jNcdBxducbVW0/wx998hzEH/ujbf8Bq5Xnu2WOefOIq//6//Ut85gs3J9sVli1xN+CbwPBIY75tHrlzOvDGm6e8/No7/OH/8wOGOHK2XtPKDd6//YAHj04RKbSd59mbHU3TEFMiRhW+KRmy6TqM2x5xorFBzozZWaWYSXwt2/xGcVotrX2keYpHFPQpORKC9kY7EzdLOTNGjSsEjS9TUkaTpEStMirI2hgIqvWC0LRkKXYdDh9aXBGViGsDo0vkElXZeA+FcNLiXcNxuySXTMwjpSQkZwVzSiA2qtLeZ9ikxNI3OEZ8ahBp8SGyalvOxpHOFRpgM7YMWfuog4OuUTuw7e1+KtkGyFksPf2kwvjj+PpzVBaV9qNyy+YFp0hsH+3eT/L2gdMZFa9BnSCXgG8xNY9LHP4aKIoNOt8LGuu3aXVoRuqs1nH5OspMs6wBp9JrlJYzNWhbwFtipaxqxa+OsRBhagDPplI6jiPJaBkVMZm+075wn1o6/2MUFFsaH1R4RAyt8U6TA++E0CgFJHhP8hlx+4Guono56wyqENQh56QCB949Nux7v1pSk2hLZB+nUe0/x2q0Hq8QI6asON2r7Rej0tUet+mpFu3LqzSXiVKDrolewvQETfFPn2nK0faEzY8M9flqIq/oot5jSlWprYroQElKvxB71kz3rEmPoMPik23cbPth3r/F0EQd4hy8J6aRAgQfEGp/a56FABKkrOCI1IGTk15wrTZYgM2s7it5PktigG2xa5ZQZTLqmVJgpI6Zyrb/KLpPSp3hKfV+dTZlre6KbmzGcZg+FdFEWFVM979n7/nbfqko976SsdIp6xrr+7MBLiAUD23XmQhAtjmWe31i0zlhCjJlb3/t/zmdBevBrElTTV20t2V/zS7v9UuU6v19b/99iYZasiXtlgcUKOIpLoEMTATOkkhkHC1jWXP3/kO+/rVXWT/q1VmLMGxPWJ9+BPQ0ocV3QZWanUP8EcFdx7tEKgOZM72ftEOk1dlj+tRV2CJXYS0skATndB4qSdGR5BqSFLJkgrPUN88JGZSpWlUp9QVhYhV7rAFuP6ne/0OmEliWqIlsLuRiynioUnOp61Ra7WX1Gqj4yUpEVZLG4SyWSBJxNt8VHDIU/K4nMZCuHFLGHWXcIqsj7p30fOe77/PO2+/gGpRuXaBIwIn24a03Pe/dWzOWJ0HuUc4f6P0fH1KBNL0pBbG8a0F20/6fKr7F2fvqtU8IBfseUEqdm2hWojAngMIkSoIBnZVVU6GNamerLcSrz/EkRDTZa6Rl0a2AHdsxsjPK9nro9TpcHbSq1PycEsE5AzgdoWls1pvYc8PmoDKDMQJVLGqfKlzbPFTOf16KyjSqNG/nnPVF9bPdLpWRZLL/ySybEwoR8QskJWOJFBOyzow54UPAiwKmBMcwbtU2eN1zzsEwpmpm9aLMHoj3QKKKr7kw00VrxeT6jUP+wi//BVY3jnFtgpR4/60PcGPm+6+8z5gLP/vzV9huHN/5zge8dvyIt+5sePL6iptNYfXqEsmem088yerggNRf4EJAwoLiA9/59ht8/+W3uTNs+blffIkXP93QhC13797l1tNP0S1a833CvTsnPHp0pvbB4pDGO3zwOiorO+68e58P3rnNO6/d5uDwCh+dXHD3wQUpJaJEMoXv/en7vPipK3zxc89x6+Y1vvyHr9CtGkoovPDtOzz99BOMacN6s2HcZVLxPLh7Qb/Z8qu/+iWefeomq0XLcHHKeAJ/9PIrXD3oOD/d8tRz1/nUp57E9SNdGbk4GXn7nXP+9K27fO5ffoFPf+Yp4npHXCecC5xfrLn/0QMOn3qGX/jFT/Ppz1znb/3t2wybTAkRH4QSM9v1jtsf3OXgmaskCSxXC1bdCrmmKtO/89W32Q09JThOzgdeffs+67MdH95/yG4YiTEizQDRcXB4YCdLK8FjTORirUQI+Nr7W0sZSgNVUNtm34qeCalmr6A9tGJzh52YjZ59jfrTvSFgTlWFq2/MZGIuU2//zMCClCIpZhbLjkXXTDFMxTQLGh+I/dk5HQkSoyqjKmCbzK/rzMWYozKenI7e8aItWrkkypBU4E48i4XDSyJF1WpzPjOMmV0snJ33HC0E31rbTSmsLyJdIxwsW0QcadwqCyoqq8o/5kI+ef34vX7kZDFlax7eS4YU3SlTkarKxuvPZM9NYqqR+x09wjyitsbLmlRcpjPO30UdXVAUKdzPKZRbrocqBG8JoL4hmUoUThPe2hfo3ez8asL4eC+B9145+ClP1bWY6pwgpRjmMZJTmtQ1J8rIVBLZ77+Zf67fbTCL6FriRX0XNgcrgAuC2Mwb51WUoVL8EFSRzml1yDmnylaDTJVEWzBDzYWSrX+yhs3V4PyQVzFHr9eo/Wu1Sjk96/2kc+/3sHuoCUkVANJnZ0miBd812ajJZd0VJvdCRdZ0aG6YkntvQ9grgucwyyqZ0Np2tyC1lAyNp3E6ZyvlDMWRxoFHD3vbRx7vVcWs9ho4Ebb9Tp0N2jNaAYDooiZYAqHRKklGJe2db6hjRUKnn7u92DKmSBH97zioTL1zXkV2nNXdXQVGdI3EgnhxxRyHqi1NlfZJzMgCtYhdpyWXdlTGMZHSqOtZlI7qQp15BWOMLBYdwehr3bJDRGe2Ne7j3WcadjIBIcBEEfU+XErIMnlKOqt3y9n6wry3vLLQD4Om0TES94CGmgjuj25x4kiTkwffelx2U6Ctz9TZ895DeffO496m3bsv3be52BLWfsBi4xVymYMJB2OOBA4Qjq2XUvDuiJAdZw9e55033+bV77zOOOw4utqqmAPCf/Wf/9e2JJmShbBYklLEOfjiT32Oz33hJX7+l36WJ249w62nXsT5lly2ONmQh3NKTuQcKT5QnMM1QROKrNXoTADf4ohkEYoL+LzEuYLktSIZqPe2sLnmR3tnEN1rgLdh0xMN3t4rBkDkvNeTLhHhCGRBKTtS2uKKYywF77XPMpdEaTI+1nmDDbkUSuxNqTHhkgfxxDAQRAjL5/WMBGA14LlA3LOk848owwm0V0juJuve0TpoPdq/VlRCPoijbYVxzGzPtvzJV/4hn/nMS1yTraoDyjUid9juLlTEwXmQVu2FeKuWesPER0sAf9irZtEGRIhMfcyVlumDKeha75L2SSbEBTwz9Tqb9H7Oc/JVCmxjJoyROK4Z0poYt8SdMLYLijijuEcOjg4YU2TYqcCIc5rklsoYEccuRvrzkbsnD7UNo9Lb+pE4DgiFELRXu1t0kxCYAFKEZDS00JhfjJbmWh9XyYXQBNq2mUAuLzN4XIqOmkopQtH5wk3wFDLjMCAoaOOdJzQNbRtsNEGc7E8R2O162jboSAY9zMaC2UsWjc4ZbC6dSCYCzmUFEx20LuMznAwgYYMLI7lZEcNVrj54i3/2J6/irx7z2S+8xF/6K79C3u54+Po7nIvwb/0Hf5FA4s73X+cPfuMrvPgTn+fGtWvkktnlwr1vPeS/+dv/I7FtuH+35+LRmkEyX//jV3He0ceeYYgsFiu7bq0kbzcjfd8jUhidskmaoLMBc4qWiBfIGScR7z4kZE/XFkIDXThQzadxw7sf9Jxve8YMJ/0FXQk4cbz8p2/yp9983Z6fzoIOPrDd7siS+KvXf4HwwpMcPdmR/AGP/JK/9z/9Op95xvG5zz9HWhzA11/n9mt3+d6Dezy6yNx9sGaMO/7pb3nuvv45GLYsBSQIdx+dcOeDM1595Q+59vSKxVHH0TXHRdzQlAU5CY/Otjw63fCP/vE3+NLnX+TR/TVHxy3Xb6x48tYV7j0852tffYVHZzs2g8YmQ98TmKnhHiGPeU7oKAxDNJ+glq4mgeYiet4AACAASURBVE4b+LGltFZ8E/GroxZFjJ3kCMGTc7Gk04Tkso7wEufousbAWT0HY0rEOMxxZM40IehM7az+XQsU0WJDQbwnx0QcI7lpCE7ok7JYZs6LiuvFnGjbhcZ4Y2QcIkc3jg1IhTEm+n6HbzoaB2MeydFaVVrHNunMyeA9Pme89wxJSCUhodA10CdlJW2GzNFqRbfsGNY9JSX6WAg+I9IjRTg8CnQmwOOcrlc/RDabHWefjM74sXz9uUdn1AJerbPMb8CqF0xgqiZL5bHP0TfXGY0z6lqmZKHMb/xYpetj17WXXMz9h/5SQDhV7zRDmqsgj/Up1uRFexbnapw40blvdUiMBbzeEgedO2RVEDf3JNaVqtMWLhVMS/khf6d9a1bom8Y2TAvMnFDVeFuY76f2AFR65pRwF6Y/NYHTRv0ZabU1quhUfYiPVwxq0rdHJ5S991xWt6rPbQ6ywYxuziyadgparL1tvkf7J+dkBroaE+sTLGjCSwHC5aTcEqtaUZqvRSl8zmhkynaqHHoNFnRNhGTrpUGyEYCK0pCxERe1gpqrYmeBYgpqqaqnjVFpoC6QYybFZM9cqwZxrMIR87pqgWgWPYCq6GnUtf2VFU2eNZ8sUwI7Vc7YgyX3tlE9g1KfvGj1Z6rciTqrOeGyHps9pV6tVooBONMG/liFrv7747Ty+uNsSK44BTzariHmRAhhru7lrM7S9tvHQQlsFAhT0rj/s/3E0e4YrQvtvZfLr9r7O+2gKn5j/50NMKliSyE4fKPJ/G6z5eJ8zXtvf5+TRxvu332ZD9++ywdvv0fwCmqlmEhSOD3ZMPSjcUmxkTgW/JbEg7v3uXfnPk8+9RQvvvRFjo6OefGzL7A88LTNAeIK3hWKjEb5G3U/Gl1Zex4z4hvy0NOvz3j01mtse+FgBQdHxxw89yLBC+ljq+AuJY6PD8O4tF72M4eeZ70Ez+bkISUFwqqhOwiKuF96GIKI0cop1u4kiD/EyYoyPoDtGnEdcuUamcTd994lF2UZlLQhp57usMDZfRgewm7k4v57uLxWYK2yVQSk5KmffREaZFn4/X/yZXa/uOULn7qC7wa6w2t0h4f4VpkATiCSgYgjaDVscoTukqXhh61R3a+lZiuV0l9IJrvtUF+pvZ5V1Clbcqn2RtvhiqoiG3gUmoCUa0C2/uVsgIYoNa7ovFqH4Iv1OqIKklVfoPoCM3MMQ6TtvKo0mq0ahtHO2J4kfr3fwtS7XUrtUar+RWjahmRJpHN1Bq/oDMiq5k3eA4UEGp27WJXNy94ZFoEQHE3b4PueMdY5xp6U1ZaV6kP2GBF62XWYuT2SUiZ7ac50alHNWZ/62BfeevVN3nv1BuKXnGwDxydrdrvIjesHHB963nz9NT788CPOxsi1J49piezO11ycbghNx+r6EaF1EG3UR+zZ3j9jJx7nAjeuLThaLnjt/UeMOdK0CoT1FxvKpJuA9mPaA3PV7+WoHi7lCVAGyIlp3rDzAR88zntNUkQYc+bOgzPGlK1apJXYkjWZHr35yKRzkSvD57VXP+Qrhyuee/qQK4sFaYwM/cjJReLhoxPGOPLaGHl494Tbjy7oc6EfIoXC917+kPsfDJQUCV7AwcV2x/Zs4O7tc25fbGmXjrHX5+ZyocTCdqPCc9sIsXzIyemWbuE5OGw4PjzgYr3hwcmO3S6xG0cc2mYxekdwqgNQhfwiWftti47rUK9Z2xAEKXrWVG2Vyb8+3u+e9Qgri8dan6b41075FNbWmC4bxTXrWUvVV+dMlGxAX93DUOd2KwBrMYe1RVV1YxUzKzRe46lt6u13A54CMiooJ0KM2VTJFRgXiwWC80hwKg4ljmgzmRWQ1Zssxaqr5j+pwn5O1yAnbbkpOZlolJiQkVN1YRsfpfGGfY77Ifbyk9ePxetHThZrrGj2FKgJQj0bpcbk9ndVqKVMsu7V3tckUsckTL89fZj2D81Vk/o7l4txc5WxIm8g1h9ow8uoYyZmhLY6hSouU181OahVSO+9oUpqbMVEaMAqqDnbcHbtxwqihnRKpO0+UoxTUrbvoC5Rbc2xxZhoQkOtTqWkh7rO7nFijs2+t/aLlVyQoFTVQiHlOvNNVbJijJNRACbK6r4S2/wQ9HouJemXct8yUfqmxDgXyqRAW6mllhSZIYRq+CDFTLfoaJqgjdwxYpnonMwXWO92lKhzwwBitGdrnwWZcVBaqmsava7pci/TZc0NkCmKkkutLNckugZAgg6Fropz9chYkEOBaujsc50JEamktpCiUpN340jbNHhmOknXtXQS6GNkGEwEYkpfPI04+qyCLmLCNmOs4UztUYRGBI9MiGZOGqFnS1anIK6o86soqLiow3L93Ksq3jMOAylpVb4U2PY7rcY9nvTt7drZC1pSlufKYdkHiS6hIjPgog5YE38BxHkWywVV/l/EBlfb+1PSftPQNFNlODid91a8JrQpaUWifp/OeUyTDQAu7U99glMGPSfdZou80zNVbBZZVZmDYsivItfLVcPX/tnvIN+Ajz64z+nDR7z1xps8fHCKxA39oN+5WgljAQah+MiisaTFN4QCJRWlCjrYPDjnjTun/ODbrxMax8HhktXhki988Uusjo75yX/pixxfO+b4yhEvvPg8q4MVTbtCM0RT60sDKW0Qf4XtduTB7Tv8nf/iv6QvBzxx85jP/uRP8tf/ky/RtJkSd5NEO7ZnJ8G6muRXI2G9ZRnLQLJMyaQmKzDulrz38reQMXP9+U+x/InnybJW1D55MkLxmTRmWnTm2yCBYdtzcvsuY75COn2HbjylWV7jLFzw5puv87Xf/G3CoiPJwNBvKalhdXQTJyMlXlCy4/zhKeuH7xNJjIMD6zPKTlkiUoTgGlZdx+/+9ld44/tv8tQzNxHneOqF53npi5/jxRdeIu16xMOTL1yn6wJp19OsFjQtkBJOApA+fjbYW7cp+chahazDU3FaAS7oeIhSg7C6wNXGM40L0nOmpY3NLvLg3hlDytx9/y5x6PGNJ4eWMmSCzxwfdyzCgiGNus7OW9BaJiL8aDbI2dkvOzG/YyJSOVuFRAXCUs7EsQJDtfqiGaMUA5bq3F/nWSxXbDcbFgulnEdr5xAD4nLJRlFVhchcCiFon2OsoGCp51RpbLnsJYVio47iSCkGoOHIlZUy8fRmNowzHzjmZPaz2GcyQUJDSQiZhoZvfPl7yKNHrA47StPymetXuPfwjNsXPe+9/QG/9b+e8vbduzz/xPM8c2tJ+ubLHCwXfPrpm/zsL/wcNz9zA+chDeBa7d29HgJbv6S5ccyNK0s+deMGb9z+Y7w4nrh2TGg84zhCqePAdMyOVqcAwuSDdIxTHdrq6PuBzbpofyewGTI+gZOeHCNOhNhD3/ekkvGNZxwT2RXaEAys08OfNolSEiEIJQl/+Fvf5Ou/+zIHxyuee+Eah8uA70Z2Q+S9dz+kHwoPLxJiIjHea3tISvD2a/d4S26TiiMVb7GSgi/d0iGPBuQh+AYET3aFIRVyr08uDQOnD98jyqRRrOq1YeSg0Up4cHqduQgZR0piVXBBT6v1BJds62WaGHsK7ZS0p9huR3myb7oXpc6nFW05UIBRDHQxhWZTE4+pQEymh2D+rBTtSazfGhMlJksGCyFoZU+DJrUFNXlc73pNxIKCQEUE77Xavo06I1a814TXCcV51kNit9sZC66lCQ05KrXbez/Fa2RlH5XiSBaHBO9pg40TKS1xFEiJEEYOV56SRy7WiV0foXVcOWgJXpPOlAsZR96NpGhJes5Gtf9hRvOT14/D60cfnWEIulCs94O9AGuK85k8I0yI4yUnWvYTz5mKKoZqai43ByU1zqzfUV/yuGcu2vgeo1VrRI2+q/Q8EzCp31GrKLUCNfXt5UxxKhhS1U0LKg1cjcmktFjFWFylF9m95npPjyWEfwaIoois9saEWrXKej01saM65b0Fqf1oeh3sKYzp/cx0XnO4tcL3Q65n/pn+f6F+ni745GjF4byfqq9ZFMGu86O0Z2WvX3P6CEuARJOcEBoVJNrrJat0YAq4QSkWy5X2F2w3W8YxIjgWi4Ami/10xZSZRuwsGSxlTs61QzAzDDulWTWNqWgWNY6A+II4T05Rg7ugHP5aSZyTaE3UJ49VZErmVIipkJKKxXgnLLuGtm1ZdCrIJB6Ct+pp1D5g7+ZExkQQybkQU1TxJ6PrKlKsFQ3vLZmpl0Q9MzUQU/TTSX0mWp1V5wC1elHBEVW70+S/gPXl2pDuwg/dM9Oa7K9NufwesT20vw8LRlncP3tl5hvkrEhwHRCe7cz6EKbkEa99vtWxKxLqtU6191nztYKTfQL8x/d/BQ/mynyt1oNWodUW6jbV8+c9fOX3/gmPTk54/QePyCmxWGjy/MTREauDFhzEcaCkTMg6eNqZmqQvWjHIXntmnRO6IHgJZIS+3/Hgzn1uvz/yynffQpzwy3/5Z3jm6Sd56pkncO6vcv3J57h28xm8F0IYEAayj6RU8AUGhK3z/MF37tMdJK49iOxWp/x131LQQLuuVSbvVRKtv9jZ7Hrb76ZAoy+rjOrKaW9sig2np1tCEQ4HfZa4SEkOQUeJpNSTYo+EA/CZ6DwXmx1vvPwNLmILF3dZNZ7u6Dp37+/42le/xld+/084OD7GmZCXKysobyKhUPLIuIs0DBwcBZoDA9Oy9kqKEzDV4pwzThLHxx3vvPs+33r5TeKYeeGZm3zpzdfZ/uzPsrvY0jQtP+3/IleOr7M5O+XaU7fori/17BRtB61L8DETP6GZhWxjPrxteY+BA+zZ2ZIpLs+VpFzBTxVuSUnPI85zdr7jjVfe5HT7Oid3Txn7C9rWUxoPm0R3LBwdLQjSES+s9aCOHUpR+4edUvh1/FCwhExppN4ZiIP28U3K4lmmHn1Qd+drMmtnY5LRLbPi+LwktRJou6X+fD/IFju/lhiL+MmOlAJxTJQy6EB0UduVUvWD1uNuySyiS1hbZkRqv6XRerPu6yr6M11bUfXy1WLB6XnPN7//kGefu8JnXlxy++4JH915xHd+cJv1LpHxZJdgfMBrb67JoefzP/Ecn/2JZ7l16xZNKPTbnY6IkMA6Ojb9jnywYBF0rmcSVXrNxRGzJ42V7ZFNvTpRcoSiIKKjRZF49VnRkoxigCVWUcwCsewY+4QvC0ITcMGRx0Lr9fuGJGQDPZz1nCbvrKocFWj0StNlC+fbnocP17z71j2CH+muHuosvlG94PGyweN5yFYrVji6UuiWnrCAkjy56MiXMap2RCKybDtaCaxzr0Cp6FkNOD03biB0QnHNdNha15FdIcWBnB2JloInyg6XRwoeEU+wcWYueYubqAaLOtKpzJv0435hD8gu9h41gXnCTBX0mOOOUPsRU54+q56HYq0802g00cNUirZtZfOtHsE1yripjJ7dqPFJ2zq6JuzFN5ZQpqTzUceo2hXOsR0G4lisF1n9gtSYMNrIGrLNQ42kKKTicKEjNEIIEfF6tFPS/szOwcEywJgYx4FcHK0PLFyg5MKmH4hjJBVHTEmVg20xFav/M4LiT17/Ql9/jsqi6hbVnp1LB8sOkaAIx5xsGBlkQvDnKsTjiZ8gM5VFoBhlZ8KPRObA9NL+qtVENY4hCG23JKdM3++mdyk9zU+HtVJN60iLcRy1YtM09l36e7W/UeezmfqnlUhTVFVQ7TPUgao1YXIWjGv1LU/G41IULfO/CEIIzdQz50UNc1Ww9r5RGokTFkuttI1j0soa1tNWhOAcLsCQsYBAxydEUywVdNbUFAjXtS+Xz22pozFqNRijxU7Jp/19NXpV0IW5/63InNCCUq1ElE6yvxa1p2wOVUVB96SbzDuZgjsRR8mZRbekaR3bTWa7G+ga23M5q/8Ub6NH6jgTVQ5DhN2gXdaVgNJ1gYPDqzhxmpiliHMLUirce3iKIFy7ckTXNuScWC6XeO8YRw2m1tuN3Yen5MJ6s6UET9sEDg+W+ODw6P4qaUsuhTa0LNqWtmnodwPDkOjjyKgumyCocp+D1aKl8R5coRigMEYoqEOo58M5SyRdUUpITKq2SqFtTfSoJn4Cld6bxlGr0OZ0yJnQtKScuFhvSCnSNIeWdNevq4AKc+XYwIEqlFI3j8y/BOkyTZrar+ocKWXW6zXDoEjn4cGKpmlUcr8UQqs9D5OtAIZxmM5nydAtFuSSOT9fk1OibRudmZqLUoLFEHoLJGXap9hYG793Ipmwr0pTH4adJvDmrEvR6uNq2fHqq+8QGs8Lt64SHPgmIi7Q73bk0pOj4GmBCM1O5f1Li4olJbKAk5FxDJTiIYwgESkt4oVbt47xXhj7QsTxrW++zte/9n36fuTo7/1DFgvHT/30F3j2+ef5mZ/7ZZ544ime+tRLhNBy7doVfMkEv6DxR1xbrOhajwsOJwOpXCDBhGMw+l9SAE7JUqMGWlIFUjD6kdR/RepojyHhMvRxyzun58ABvnc8jSfFrNLuWRe2pYXQkCRQxsC4vcK733+XX/tbf5+P7j/UWbiiFaala2iaFT/9M09yfHyd5aojpYE0eJpGKAm2u8gQI+vtWm279RfJQi87Zq3gI4XiIkKDy8LVWw2ff0Yt6emm8P1vvMrXfv+bBNHzcvX//MdcuX6N9dl9/vV/86/x7/2Nf5ej4wVxOENE+0Rl3yGqcZwFdVF2igOK+RqcaPKAMkj0DDfav2jqi3rGFIhKESgB5xeMKfC1r77C3//vf4P333qXwwPhiSePeOqpKxTx7BYDm77w1vsPJnZN01g/IEz0zILgvI55SDFZzxXqS0zcqgmF1LVTNS6nzHarM+3EALPKAPBW8dHeea189bsd3jvWmzXOBwRHSolxjMoM8E6TmpKZxnxWkoJpv6axTGDVLg3kvJvWtVYnNN8uBiSNl4BRQe1iMNXVmGs1M5Cy9nnlrJ81PUYL4Pv1higj129c4fOff4Zf+pUv8Tf/5t+l6zqevLHi2RsNy5UnMeDGwsn5gpW/ipw5vvp738NHuLbseOKZqxzeuo5PLW/94D12Qbh1teHGcsdue8rLr94ljomuBZGekgXJWh1VgCUgLCkUPJmYI+PYs9729YLrldtSOeI4qtqtqWWr0ufAZjuQcXQrTVTiGiQXxAvnux3DOLBaLclJxa8WvqWUREwjR7eOOYhALgSXacOSzXaLc55db1Wk0lNiT1s6rdra3MpShGHT4HwipAGSIztPlkRbOsYIfbFERUZ88XhfFKjK0MdM1zlSbNC6YSTHjQI2rrYuZMgQXIdLiVgGE9SCFCrAa32e5rsqzbPGlxUcKUbZrH5MCxkWG9lap4LFZgoQ55Q0/iswDiOgP/dOk+YaPNdrFazymawX33t826ivL4ngG0II5CEjTaMJoddrLykTI2QnKrS3GTTWy4XtZk1dDmJi7Eelnjrd80MclbHmZ9acCBAjWIVeEDb9wKbvOTwILLoG7wsj0WiswuHqgDhEohs567fk3UAclOo7ECkIbQu+CYzDiBMtFIxJwSpi3b+fvH6cXj96spj5WE/ifmwP7KGoFY8EmOkq+l7r8TNU/nK5wv5+qkTUH1tk6Ob/3heM0SB13vBzNQ4zrMbPZnYEE5pTK4aW1FTqWzKqoXNCdtaDZnFwzpVGqvdaR0TUylg12R/r29pfq8v/asixm5LuisrW6Lz2A0pVLLSvr9RIpZ9qQ74rl9Ur61WUuv45T8n3VMWtIKx9X12bem0VbRX7jHpN8yOX+dqnZ2t9To+BR5VmeOn5YVz8+nxwEzhQrBys/aHWm1aiIoTTxmBKOmbFUczY5/qomKRQrfJWVHpWwwkp1lNmCbYrpqApOt7CK5AQjFaDVWKaYHOeXCDnRBsCiybQNDo6Q9Um9WsbZ5rCpnyGQLsI+CCMsZARdtue5DMhtHMCX5scxJDwOirDqm3OqJGgKP84IZZQx4WMo1UDqnrjFGTpWkxJfrHdYnswpWJwxPz65/USy94mv0TMU2WOCSiowAuW/NdeRHHBpMXFAtwWH4R+11MVZu0KyCkZ9ddMRQHM4Sdb3yHOSV0TFEAITbhkw+ZLnq835qjJRs4678qUji+ptdY9J1qJXbaq2pLylhQFkgpUiankiCXW4gtjHvDSMBZdW+9aYim4DLiMkC3fDtNZHcaCSwr8iEtcPRQ47IAFuQjjGHn1u+/w2g8+4lvffJMrV4/5mV/8Ka4dHfCv/LV/gzZAmyF4EL/DlWN8UrGnmEdCHf8gASke7wWyU8oCmmDUtm1tUmE/PtWnYrTJ4j0pwsXJmtO7d3ni2hMUPm39PQUfGnKB2F9A7inLJ4lnF7z+7S/z2svfhzZw49oVfONIRsVq3ICIY9wlhnbUYNiGYxej4293PcMYp+RCJE4sllKgQ9WkEZAclPqZe7Jk1mMmF0G848r1BUcHpjoonvsXI++efsjpozM+++Y91ueFg6MlwrrK0MxK3dMOnW2foGNGlEQhkw1KTih4nG8VrCgt0AA7HCPqsh3gyakBadjtej744EP+6A9/j5PTBzzxxBUgst5EykYDxVhmgLJIoWs94j1d1xBjoh+2xFhMhEpBm2pLndPzMYwjLkbGaOdguhMTC6vnbQ/QRXSPlyzWF8ncz29VevEO7xoDbFGA09exQNW2zH5OREjRTXZ80qUTFdXpd70GpF2r50b2YwBlUzhLFn2dAVj/z4lVRe3uCnsJowXQTvC5cOfOQ958fcGV1YKj446mtJyc7diNIzc5ZOwLF7stoe3YlZGLk57dHXjq1j1uXut46vOf5sZzz/Po/pp+PdD5jiaEqUgvOHSska1h0XFLCvwNOOdp2oAAMffan2k+MheTVNDBg3aOk/k53XhFrC+5qMhdEWEY0TXLQnFCLJgas6gQW0k0LmgmXpzarF7nFlMyJUB2AUpgLA6Kq1sbQsMQMyWpr3QSEBKuRBxZgSaL93LyFBdIZSSWSDYhO4AiEKd6VKdZY4kUVMHUea+xqThL4iLFQY4GQrvaFw30TlXUDQioyv66dR3UijYK0tQdX8Oxuj81FqoOpDAO45RM1uNdzO7UuEjjxvp7Fi9KVQG+5F1tzmVGJCgjxWIj772yfrJt1KjKzt47fBsmjLYqf+s+UGfv6s4vek3eycS8q0UJjXWs/aBSakWFBofeE8eRxUKDqTEWSuoRF9RviU5QTVmTXESV+MX6lPOkaaEx8xhV0+GT14/n60enofKYAYXLSVspc5BpBr7+pwZmc3Q2BVg/PN6kksdqIqNiAu7ShI7p06aqJbNs8F41Syzhozoq+47LTfoojdQSr2wBsia6jtDo76aSp56M2q9XKw41Mf2Y+MZ+wjhf/j93gbU6pE3FwXt8EIYhWeBaTOugJpFVlAejWBSb/1Pv2b5wT5BmtuTzWtckcKqmmAGsxqKqzBWjwNb70hx773NtT1TjWj/q8l1XlDDpPLk4XcWUfCJuj5ZlFc2aINt3aC/a3IOCJXu1MXzah06MalEvY6bHTkn0XkDnpPYHXR7rohXVOck132moPZPxjTaPLnhH8A4vRh+y/VCluEtRBw0KZKgiqdJahkYrYIIa/liyKlnW+YClgBOCV73EyQEZzUqVDvO8Twxgmehg9f5z3UYzxVakAii1P3bvwLGPX8y7eRZPwnLMjx/ssv8Z9Xemv9PzVayC6Gy+ZEzqUFIRxnGcAIxJuCanSSDI2XWnGE3oQz+3wFSZ1Ab9+rVGed7bm7V3sVB03lbW8QLJktlLlYqJ3m57P0PTBIrV2qp7zqVY30ndg9UguqmyWtcwF03kRfTZlWziJva9yQYcK8uj0DZuQsDjWCA5Li42jPGcR/cf0HaBze4RV1aHOLdktWwo/Zqm1b0m4nC+wbkFwiGCshQURFKV1JgSUkxlt/JMp9NSLiWMBQsInACe3WbHvTsPufPOHZ5/8SXbo3X8UqDfDnz07m1K2bI4LLz7ytt886tf5aOPPqQILFcLJKCzDoHG7PEwFsYxgnhSTDrzEu3bjePAOEasgKYK2Q6resp83XbZUoRSVEEy4qa17RpPcXCwXOJcw8P1CeM4sD7bcPrwjGE36jONkP0e8oISTairVSoYY4dHy7NWcTQGi3jiZmBzvubevTNSCVy/vuLo6gHe6diHOGZibOj7zJtvvcObP3iTN77/OuM4cGXZMcZCP/SWqEF0M1Cn+zkTx0TbNOSsPiWOCe/1ASrFHUSCVTL1d8BaMQpT20W9FUqBnBFUVKWYeJiyi4rZoCoiN/vGakNCCDS+Knw7KmhZ+x8BnQ8nYjoBei+VviaiAigxKKDXBG9gYlE8rAjaw6hjt3SO7dxfWgHXKdqQCjDYkRZ9ik5lQrjY9Hz44UOCqbG6JBAh5sJmN9JvBi6GxJGHDSN9H+m3hfc/eMBmu+AXtj0LCjFldmMiuIYUVRlTTZT6EMxm5Zwo2VmPV6GQICrFO6akvqRU+3vZ5kpdaFuzKkZGzqRiBktEabzkSf0zWw9eVS0XUfAkF/0Vh4qklKRokepojbjoyYZXStH5jwkd5F5KwmX1nwVsTA+YRCBKulSlzVzbfygGnhR9joL2UyRMtkD7N3XyqQ6RF9uTiRkMT0VFsyqNXkpt85ijzOoLppWTauvLFPvUbLHALEA3HYbZN1c/VFsgpnh1zwHWqGW2SJWDZ7FWQQF9+5yUlRWWTRmn2D9SCk3jJ4Ch9kM60bYov1f9lAJS3GTB56S1niWMrSNTO3X1887pfY0xQ1StDucdKUJMCddmQmEacVQMvHMWE+FEhW+MJj778Mt285PXj9frR04W/eWztJcU2Em6nBftBV72azVQqsHpZN9kev+lpIM5OJscLTNiUkzERvZ+XoPoqnxWA2eRGZXcP7SAVQxUEKa4bA6SKWhWWoAj4UiDUhmcOCTPtNSJh57VAVYHDTNldeobZK7gzPepQWmtYoxof0rnA+K1uXhX7d6ErQAAIABJREFUtGcrOaWXh6aZ6LQuOJq2JY1p6j/rFoK70BOfJtjITNOegmVBezTJleKr1bta46vrLVQ1NHNcJvhBTSJtDasB8AYkyGPjFkQEL47dtmfs40T9qAjgtN/83CO322xBIBo9QhAbMq0JpnfgpJjKlyZmztXkSXBWSas9oVPwgRrglKI6sFITxGLiQmXap0rhHNVYJ0hSq56WXKJJvFjQmilkP1fKnQG+k6M0ikelUAs6tNo5aK+tcC6wvtixGyLjmJiGxhely6zaJW0XFLQw2XQViinWP6vVALmU/Lop8d7nFysYbQ4oaNA2jBZto1L19fwK8/NyNeKqe7vUCrdSYFUUwWslz06qc7r207kpCh7o2cxGB9Iz1PeDCjBkpU0vFt30/ZRKJ58TeKVY6/NXSXKm3kzvvNFQlYadU0J8rXZgAFG6nNDaftVKiJ9sBVRHqoExaLUwl8ZsXJ5GS8Si/SIUHfciAXXyHOCAYAPCVfOkJovac4WrPdf2vZZ9eKdB8BirQUs4gW7RsFioMEimMKbMO997h1zg23/yPZwpaS4PFpQsrHdbbt+7zTe/9j2uXTnkhc8c4TrwPiElUeLIo9deZXXzGQ6eehrVj7Uz6pJG00U0SJBCcoXCiNCBX/Hyn3yV7738Cu+98x43nrvF+uJf4+DoGuN4isuBh/dP+N3f/QY4x4vX7vPr/+D/4u57r3N00BIayK0KVUQLZOKobQTtItCPW3a7HdoxZMGvJTY5Z2IR2nZBSV5HHVFwwTGmAkkD5DoCIxJoJBA6G7nSj4x9Ykg9oVnQdMLhFUd3dIXzRyeaSGelFKe8onhr5MHWYVLZraCG2SUBl7MKxVg47YaC7wIP333It/74T/m1//l/4zyO/Mf/0d/gqSeu8/prb3LnwQPONlsu+sT56YbXvvsqF6cXrLoVyyaw3q21MkDWwM55XE46nsTmSZVYWA874ogpMRdiUtqc2Nw3PZeZMaWJUl9796tPq4AOk1ewemQSnMt61vfAo3pWC1gvupBTohRhuWjxjdUQsyYFCGbPNUFL1ofnw/ydteMwRgWQlp0aEB80yfTeGaVUkyWng4ct6VXPpnYXctQh91MebOCiPkpNeqToEPWCcPfBBbfvneKaDpHIlaOWNrT0MXEx7hDf0veRXRlwAY6vNbz74Uec74754OVXuBnP2TY36OOA+My2HxgQhlTYrDON2YBdn0gpT8rbeCGWQt8PE4XYW3uLqr+XOWkpWGwkkwie7O3PTNI5gvqA9Bk6rM8bRLwq1WYVsotKZbGeXLH2GyjiSWMiDSMyGo2zjlIrhZSdnYRElkJBgYiIzox0yamia9DkYpd6XFEafG3dUSaCASwuAz05CUlkSviqWJMnmRvSOEecEMRANgEvnhJUlKzEGovV+BMrAszx7ZTwlWmZqLEuwhyvFp2xPJ2Puu/3TsikmzAlnXNsWyHLfeXTGniMw0gU3Zc5F/Iw4J3NTXSOg2WHbxu2fc/p2TkUaEM76Vdk2wtOBMlFRdSKxc8o+FYr/PqkdKSIhIRkQYpVYEPBl0Qunu2QgETrW5wXNmc7vEBwOsZlLBEapdznAiUVnO2fsTIRSmIZHNl9kiz+uL5+9GTRF0oyFTIACq4YpdTeU6sUZTpd9pdTM8eMp4glTY9XIeZk0JIVg63TRIKRKTYVhJxjPd0Wt6qRcKUmpJWmkZXaaMF8CA3eetpiSkTji2uPottLYrI2dosj0Nh9uUsKn1UkRQM8N91XrT6wZyym2Hp6FeZkSYeij8OoFMZGE1BFjAvFOWKMpDFTcKpq5bQ/crSG5oaGrsOqE7Pjg6r0Zt8abCpmUWdSLIEoppyZq+OmiiwUXS+KOeNKKTCxICc0XmmYuSS8a/ZoS3tbwapeih4nFRZIqoZaBXO0v7RMqpPb3TgZ3eA6ckn0vVZBqL1oJcOkqqnfWSmKdS+WmBWqThn8nBjmrMNnEW+ouiB4ch5sH6hxLcUSrgxlVKCkSCFbZqBJmbP1nHY0FOsp3aMwTwOuNVJSp8fcK1srnDoDTB24kE0YJ9CaUI5emzqwut/Vn82y/16UZuudU6TZlHwVRCnYWMD5vNi1pJyN/lKmhKaCRVOF0hzkvL+VOtWPI2nsabuOpmsvV7eRCeiwJ6AgRxAbxC62XtY/KHWfqYKat3BRigqvFN0GOpIiaRCp59tbx5OuZU5VtCoyjhEX6hlXRxxz1EDJ+73zjyHsRSsbuaLCM7pcRbxEtPdCK4QaaOr1O50P6B1dWCAuTM/Aeb3GnDNeGtI+9KtGTgPVksku2zMysKPMiHRCA6YKpngJdMFxdMshAfohMY6j9TiPxOw4P9uwe+X7/Ob//r/w4qef5erRX2Jx8yrLoyvIKOQ+sf7uNwlf8Bw++TS4SHGt2hEXAYdLNhw6JZLzZBKBDCnwg+++zG63Zr0+5c7tjzi533N85Vn6dEYJWk1J0eO7JYujmxwuFvinrrA6aDk5OUHySMmB4A30QgwgUqbFOCRsmo0m6PWciBB8w/HhIdvdlqEG3gJiVYyC0f0KlBKJ2eETVnlRuyXFVD1QimseCrl4XNvhwlWQJ/GrhU6pLkaHpZBlhyNpUklh3PXaO9wYaJdVQEIk42OCLoIETteJb71xj5N+5D9tn+Ct1z/gH/zab/Le7YfsiuNk7AkObhx4jhYti1VQtcxczLO56QzXWcN1z6vSYWa73WrS6qAxUatqM5AZ/FS7WGmOs0CNVFtqe68JYWLiqEtMKpFfy3YwJWL1tM91FSwoz/Rj1DUKjq4Rgi/ELAyjJv+LZdBRGhZOiIiK3Bi7RChITnjnWa46xn6gH0Ydr1KqD86T/UpJE2mV+tdkUaZzb2c4q72PJErWnu6cMyQhpR7vHGNUevth2xKbkV0p9CWTku7FUgL9sEWy8OBkw9vv3WW5SDTDQG4GFqHjIsPJbmB9Fkk1caitLVlhhRpX6RgvTQLSHhjvnJtVsKWGRHO8hSUpFajPWcf8OCu9p1KUxkn1O6ZkXrSK6Jyo7wTI0SpZNjw+iaqLJq1o6fQXp/3OexXQbLa8Qdcxl6h92pWtVctZBvCWUocbmU9LWr1O1c9l9HdQxfpSafRmGycTKmJq2AJ4kDTtwRrzUQGRGnc6MZthPpg5Vp2UtcuccM6g49xmAUxtC5dYaNVq7wOmltDV76jMvJTnOK5qhYgXmq5TQcfiGPrIbpuINt6xHyNt09D4gDktvTeB1gXIMFibhnidReqzKeYXpY0mGWlo8MUzpMwQR467joIQo7LsuoXQ+Y7x7jluKbjO0TQL4rnFpySSJaSLrFTzIVStg0zHHNd/8vrxe/3oyaLzunHBPCzTprdc7VJPYX3p+8VoQPayQ1UPzz51E5ircTXREMExVxDmfilVQqsXoIWtYv0KGgRWimDwWq3IhtbV7659SVUMRXsWawVhvubL9TF9pZQIzuGbZha/qUIylrBIbZqerlsMDdwzMvU7RHtTgtMKZQiBvh8pBbwPhDYQgjCINk1nq+hJyvR9zzgkE7MRhmGnqn81IUTwwWsiU+aRJLWs40KYEa0pv9dwpzr+lFGJZqfN3LXH0Dc6fD7aM/XSsB/z7t9ntmeTXAavNDpsxp72jtb3O2ovXqWq1L0xiaugPTdKZ8l4F+y+9PmNo9HqKnhRrC/RzcCCx5HGyNnJ2fQeZ/O9bBo7ghCHkW1OJjBzUWMrQJ9Nfc4pKrrZth3OV5drSn1W3Su2d7BErliVUhNJTUKG1FuSiVVLCotFQ9t1s0BN7YFw1ZnpMuVkw4CboKM1og6yV8XT+Xc06NfB3eIUTR5jJqXM+VrFKNomGKoZ9xyuaP8m9qzqgPasZ8kJHB+uEHeg/VNDVLGLlGwUQz3X4FygoFLf5q8tkNBgyTmvM6CcKKAjlR5WJsGNlCuFziOBaVxMipFWGtoQ2O12DMMAAsGuXdFlDLh3FAIpZcZxUOqP1GAAhMuJ6yzmU1PHGR+r91eBLTfZmky/U2EQVXgt9IMmEE3b0ISGYbchNI2KkcTIGCPSdXiv/ZbUJNUAsKlvpgZ2ZmfGcQCgH7NVs5Xa7ENg0WmS0DQrNn3Py9/6Np1zrNJ12vgMko7BJcJh5oV/5z8k+sAm9/iSae0OQz6y6x8YUiYsVrR48C0xedbnO/7g9/+QlAcWTce9Dz/g7Tfe4LlPv8SiuQFcIcU1zsP1q9c43dxnW3Y8Wm8YiIw4KA14jysFicmABE/O2h/UtqqoiqiiZ0pRA0jnKTkx9lua4Gn8gs1mw26zYblaGX6joKGIiqLlnBh2vZlEZSJshky6WOP8hvVmhwsty4PAm2+8wv/x6/8DT9y6wfnDO3TZIQ3cO3nIxXbLz//lf5WbN2/hI6xPT/mj3/ltbn90m//s7/xdjg8dZfsIFyKuyeTjKwwsWFxfcXTzSa6uFjRe+Mmf+gzfexmSK1y/1vLUU09z7fohQUZKKmy2I299cJtxTHTLhfqslOZ++r3AFT7eoy+I2V6g5GkIeHGZ4KxvP0WWqwVXrhzZZ2liVT+r343ce/CQJrTTCJ9+iDhJeO/UdtTKu/mQMvkjTa1T1Er5wcEB4hrOzk9VzTirP2y7JWmMjLsNvlsg3ms1VGRSIM/RVDyNun//3kMFuYpSLvNahe6apqFr3ZwgiK5LpiBZY4FsSqP1MLsiU8VH/Yn1Ylrz7vn5mvOLC7quVf8zKiVvEVpVBz3bgku8d+8u/+1v3MY38AuffYqj5gpff/eCm1eERePodz1pM9IEAQIpqd+rfi7l2qJRQbrZFpWCgfZ7DcQypR265ntJS7VTCljNduuyPI7+vasAr11LNpCggoridIQWCTD/hSU6OgBeJtVP/cy9kWVSq21uSiCqvYX9OK+icVx+mbjSDwMyHh+Nlo22Oc/itiJESpMy/v4r5z3mSi2P1KTO1YJGnpV8H7u4Ou/XTWs3j37aTxD3mWlZasI6xwf7lc/qT3LObDZrfV/Ms8CT27+Mwmaz2WvLEZpVANSPLxcLmuApseF8s6VrGtrWs40Dj852vPTCNbpSIBY+POshwq2rC0pKNJvMJgreBZZ+wXA9kkXba+L5liDCznrJvQPnCtuioHwYtcikorpO58zuxdmfvH58Xj9ysljAOO9lAqxqkljzw/L4Yd7//b0fCvOh/vj7LiMN+3TWeiSn3zNAtNJlwCh44qYkETRBVJrrXs/TlIfuH/45sdI4sCZOsvft+4EiFuTmqdHcOacUQ/vcFOMecmsGx1bhsdW9jOgaIuxkrw+z9nLI/Al17SslolaQnPeQRu0DSHOFTVB6TrbPFYc12O9jZ2rgppC3jgUpxjyrz6huBMdcXS3a31D2RHQmUyeiSW8IdO38jEKxGT+WLGL3FFMVNJnBAZgrjzPAp4IOisy5aW/WxvK6NnXtXbBeD7XGOhzY1kd7yZx1XCitojqrad/tfW5Be1DEWrX2E92J8mry34YSmIOze/X7iYVRMs366wiFYuvmadtmEkGi1IRPE8GaAM/7t3bkac9OfQq1V6oizHo78/6vPa+1d2YKKuv92j6WUiYl1jqjSdfT41ymkEij/b4Flylr5QCEfrcjeEW364D7lCsAMSdmeo6YqGXz0ZOpCiC270PwxJK1Zy2ZWptTYQftxyg6BNxZT6zZgRqHeNNeNubWPIdS6vfsAzsfN3bT6Al5/D1lspMJOxt2JCfk2c5ondVZG0prlcPtrQf2p+Q5uFKgSqnxOWnpu5QyqVsWsWafDFcOj6EUnO9wwXPj1jHPfvYG98czuL1G7i81Uc4Dq3DK6urzrK7dQnJPTDstMMgB2/NHvPuNP2Bz8oCjF17iuS/+Ik0T+PDNN3j91bfYnl1wdO2Aa1efYBgyH779Dqd375LiXQ4PR2Q4x6Ut24cfcvveHcbNGY1XJyXiKGLDxg08cG4GEct0XitgWTThd3NwhRSWphw9jD1Db3hQ0UrULOJl53QCUyyIc47duKMMKpTkc6FpOs5Odnz5d75O23XEuKNtIGfhYqO08ffe23BwuNKZgsPIh+/eJsXI7bffoH3hMxwc36SUHTmt2T56l/bKSwTbFzkLDY4/+N0v8/qb79LHDb4B0sDRIhBQoKFtPXzotTqTJ3WK6dqnNYJLdrLut7oOFG1pEJcpeaS2nAoa+HsPbaeMEVDFYrHn44ObwDkRDYx9qYrjah+cAYFlL+jdOzBMlXCnAjzqWow5IA5vEvw5PzYGqRTarmMYIzn3lFIYxpEyqiCPnhOr2otM1cMhVqYRU8BSx2plU0afEmw7a5NipgHNmcoMmcGaiSljsw5d8WYGHG2rc3hlzLS+5cbBFVbSELfC2mdKJ+TRq0iTVdlSqTHLfN4xELQ+6zq/usZBFUgqVCqqGkcptStw72Xmr+o81IR4tuOzrakPbRIYq2AEaH+q7F3TdEEy14xqDFUuf3/tZzXux/7GmPbypb+pYV+xD5g+Sz6eSDLvFV2fcimxnK9FJpBytin1Y90Ut+0D3vt7ZIoJeez2bO3m+IfpWuq/70fE1YZPRYr6HC4tgH1LqXoRde32bJb9b6pCijEksJ5DUbpoTECOdE1L1wacMxYdsOg6tpuB3sDdAUd2LQ/PBgXVLE5b73r6MjLKSC5OFa5HVaPPTr+ntTVsOg8u0LVBR4FkyFIYc+Ki33784X3y+hf+kh+WoP3/eR0e3ig514RlPpti0Za4onlM/jMyxukqYJrDJJeNwuW3ydwwzP4hhlp1qWg7e4hlCK32rdiIiJqUOGeDeMeRrmuVhlKTOZTm5n11SvpdpWQWywUYMgRmWLMisU2jdNZcCjnGKaVU2oZKV6c4UzYnR+T93FPmHDEmFssl3kNO2qx/eNySc2R9saPfRULb0LWNBsMlE4eIOKdJRHD024HD1Yqjo5bdsObBo4HgPY3f66vEhE5Q4SBxULz7/9h7t11ZkuRKbJm7R2TuferWZDfJHohz0YgCJECApP/QowB9nx70pF8QJL0KhABhXuYmachpcmbIrq6uqrN3ZkS4m+lhmbl75N5VbBahYT1UNLrOOZmRER4e7nZdtoy9/sLsmhxm7caBIPoVBGSW0cuRqap76xnTcO91Wm9JEtZlhZrhD37xOWB8F+vl4j30Ul8fJRW8vL6wLcZlQc7iyrp04hEzw75X1IbOMhjOQ86pP3f3qAHAWlc2PSBoTszRo7PiTgz7nSUJN4LNq9VBXyyIEVKcwxCkQFkKrpfi7HvOqOtNfpeoadURXHiwoAhJzBlNE+73HdthuFyuWNYMWKPiF5KOHHvF/XXD9bJiWdiE1xT4+FpxWRPWMhrURxQ1Z88ye42LCCHNqorlcoE2QvGOSvheTnxqMsU6LBYJr7c7RND3krNIeHNsz646dOmbr19gUJTEiP6+b8i54Hq5gt2d1JkND2dFjL1RkJOi5NKN4uBtFHdCo+hfIEASNCUMWuA1TyArWzjprEEkLJvMby47MmG6qsGm6iQ4SXrwozvqCGPE15ZH3MOR7RIt2se44EopQbV5neuMLKByT24siDhhTuw1/zwMSEkyYO5CaFqsr9YUdSNzaHW4azf61PBP/uEf4PXlFftWIaWgXFfszXBfrshRaqAGaYpPnxb8yX/xJ/jv/4f/Dv/0H/8jfPiDXyLZBv32hn/1p/8bfvV//i84quJf/+bAr14vsNbw9V99ia+//Ihte8Hlw4qlfECrOz779Iovfu/nuN1foGlhJuf2gk+uz/irr/4DbveGp6WgSGZ2XKrvRfT6WG2K9cK2LtqGARx9QmulbFBVXK4XPD09Q83w8nLDvu9YlhWmAXP3IEg6G3HMOjZ8PAiRZyN5f0vKd8EsVGMAIy0wa14vCEBcljlR19EqWlP8/A//CP/0H/4S//k//iU+bnd8+c1HfPPVl/j9P/oMS834y199iX/5z/8M18sF92pouiEvF6xLwdMq+Cd//AVMgUu54L43/LN/+Sts247LZe26ZTg6eVqfc6bDMxuBd45gqWFkXVzWtnbgw6cf8Ps//xlp/d1QlSTQZthuB3795VeEkKeIQwdqR/t4GOwSLKu3pfL7rEtBScBlXfD84ROYFHz15ZdYFwZ0Ui6QvEJbxXZ7wVIKW4rogdYa1qdnwsf3HbVVbNtO+VZKl3nb4e5L1Pd7TV3JC7MuIz6J+36EZOnrIDJhDDBEv12OIYLMSQRrIfphq2RS51U4X588XbEuC47KffWLLwo+XQV/+q++xnXNWBL3dlqS13r7u0TYLYF2mhSFeEAJDn5WQ0buttV4jtTfxfTjsc494JSdsTKCJgx2EhQbrYZ6a4dUeohGMRlL4dwi4Pt0uHpyQMOBCYstjqn0qD/3pLIx22PWZWGcIIhSEQ3j0M+Td7y4rvL7ujCXH4/9QAcke9ioj9/Pn5323wRD7QFNeWc8/fcR8uLgRvnL9LotbIY+3ejTEeMFOuIkZGMCAwabMAyb3cE3NVzWKwM59aAOToloq1ZhHjjn1lZIzZBkWJxBuGbWueYCLLYit4TfvN6QkuGXv/cBtRm+fb1DAPzxLz5Bzgl/9puPSFCsCfgHn1/xsjf86b/5iJ+O/38PO776HRyz8/F3aJ0xNsJjhAoA7L1d8F3HLLg86vNdDmOcNHJewyGYFTwvO2qM3nOK3a6czrU3gujReAPQlVuPQttjhMmFXPxbou5t7PY3wqYLmHgqdGclHi0cih4hwnjeYJaCMfOwYNT8RbNkGJ2V62V1A8rH1iU1HRyF4q6komddIz+nch1Rtez1QESLMII5MPYJ1ZrfY5DVDKU73mJALEwJ7VnW1SEaI7IZETIzwjyXksg6qIz4i9ARrRWAw5Kss5J5Ri0TjhbzkQRQicxLf800tqMeI+YmJZKvqWchcoJI8Rgoo6bNr5ud4EJMO2PfDJWOYEfvRelEAPGOerZ5Uk7x2ySCpaQOW+yQWpH+CkcU2F/ppGUjohqnQqbxvQcF8N8Xp9RvbUdkHCe15PveaBD34E9MnjgJC9nSrLHvGcAov8ADLiA006CQ4qyF6cwmZ5Piy94PqnlPU8LHM1IusEq24OSF/4SVA7bTcS2JBqia9PmOulALFl03lqIep5tcPYrLZxagZ4p75WIw9mHO6liPifnL6MG2MZOcyzAQu8EA38c+7Qx+tbGn0mg/4v5Af8HaGvZGCNDAMch4dW7M18Z2Gcdtx3/466/wTWNWGWa9OP1SVtzvO375hz/Dv//P/h3+y//2v8HnnyTYr/4cH//i/2GfvMsTvvr1v8X/9S/+BdrRkA7S5X/y+ZVseWmHiOHbb36Lb377JW5bxW1ncPGzpxX7045vX16gyIQIJ+6t6sGsNSeHefsj9Hl+QKF0cct33Zrhtt0JLa40fppOLRUCUukBtD436utSWc8lCcgIohc6mYVmF8wSTNn0O3sPv2bep8w7WC8FqPXAX/z5X+L2zVf4zb/7S9yPA9+8bLjfDjz9uWGxjP3e2PeyZHz8+hushVlENeB+HPj1V9+iGbAIayABN0rjnfV54DiB2P9zUMLnp9dVT44jYiH5JEtCq4qXj3dyA8TqFOrRnsEzJ4yBgLXyYxw9SOljkMhEcbJ96Iptu0GjjsX3VmuKVu+8mQ39HtdptZEjIGWkCWYJ0I9Sd/pDrkaVzLzHguWby93cUB/W+EACjbkZksH6h6E3Ax6rLQLHJBJKEh1JFV+9NhxVvOejX8djsa0jq4bTxRiZ+Z19P4dsB/rcaLC5YrbHbOiJ/vmkI7pUwyzkJhnsdYnTs45cPlw2+addEI292N/1pA/7debP4ncxbBky9NGs7HMfCq2Pd3qGGNyDjpP+73718dtp3vuVJ50823epr128OeKa/Yrvqdk3NvAYEyDTdSed2387O55n2zm5HcT7jpdh7jACvi98bdlxIJeM68rsf5GEBUBtArJPZRRUJBjydQGEHQEEgksRSFLsreE5JVyQ8VIzkBt+/mHBx3vFxz0hZ+DDyiD863Yw8H4RLAnIeGdyfjp+FMcPh6Hq2EADR+1Cc3IofrdjRLYQV7IBL+lQCATufZBJjAGhM3YiolBu1LV2dhZnJzAiPL0/oc6GGo+A+lE4JGf0lF6jRrZC633rAjJKeAszdnQuEplUW/MNPo/pYVxRJ6KANRJq1EZjPXo+qrFGyFwQBnSzHQfg9WkADRNTQCzqQZgZZS1lcqKQ7JF1g9oBsRtYkM93nMMG8fGmLHh6WmgAOdOceqRRAexbRUKDSYaJoHrD10dHvvfXM7KbHseOeizYt4PcMB7pzrm5AeLEJomOa3XoI5uJJ2bIKjNT8/IL6KbCuqFDxyah7iOjG0ru6cMFSQTN+/KtywUKxddfv6CZ4vn5imVNvg+44FQP1KN67ZRCMqPm99cDEeVUM9bGZfT6umADBdxYsXACWb9BhnP22cqFTazZTLn5uHl/C3hedqjlrAx07Mvq9UhIbl6bOB/IqOtIORqxc01biv6jvN4a7RaA004MJthoO0PHm737Xl/vzFganbiS2btPBFjWAq1kH25oDuNlNqYUAXwtAr7uEgli1Awvrxu2YwNEsK6sScw+j8Gi2zxg9PRUOrFVHAFrDMPu2A8clQ6WJCF5SmusgfJ3QTSB9Yh377doAUlD3+OCgAJi+vxMeECn3YmG3GHM7uCIt+OoxsBULnxvEQYBJOxnfupOaCkZ2YR1WpOTKL5Wwgivyt5kT0/PHmBq+Ae//wG/jydAKopV/joJjpbw+u3X+J/+x/8ZZcn4r/7rP8F/+o9+js/+6q/xKg3X5y+gTfHvv9pw3wwJC56fV6RkuOuGdGRs7Y4lF3z+/Iw1Cz58mvF63+jAqeHlfoMiY1dFqhXVibKaB7DsyGhCAiQEw193HPmcTUkqUg19v9RacdzvXEcODd93D3D1IF5oEd9/rhPGQRKlnMmUyiBY80CR+Lo7IELyM6IMos9tpQwGM1l/8LMFtRn+77/rrXteAAAgAElEQVT4NRIMSwKKLfj4FZ3znBLSorjtOz77vHibph1tAw41/JuP38DEHS+QoGZZMmBz/1GOOQJ5IQeyM7AGzJnylIbl+beDQVFSwbY33L/8utdvhV4gRD5jWdZTQJDOJ9fwY3nH7r3ociAUIH2/vb6+4GiKy+UK+PzVY8OrY4c/PD0hWniUUpBLxn3bYAiSq6hxTjiOA6bhzLjTpWw/RNIswX1j8Crq7wK18BjUHXXa3IE9uyNe2whDMxKGCJi1AYAc90XCfT/wum9YQDI2Kwt+uwNrsQ4RNQjkCGBrOENuF3UGye5JoQf8JPZ46nVw8H6DdDxbh4DOMMceUHb5NNBGgHnfmajl61lK71Xdomnw5GQZ3EbU4Vzr5CQGBwJEXFdNjmx4S93bnKV1OM2TcyXwPcr7DX6ECISfHbV4p/GM46rzlgldGAkHXnd2FLtjGmtpMjjmDKQ8/Hs+IhA+/wY4ZzBFnLfDIpgwO4/o/7aH9Rp/z2m0PeF7ALOHBrYwgaCJwpKiCVCy4Ze/+IBLKXi9HXjddvzR52wZVC3jy48boII//L0nmFb81TcHtgp88ZTxtF7wz3/1FZ4+A67PCz45CloyfFIUdzHIknB5FkgxbHfFy01RsmBZEkwKzCp+On6cxw92FtM7n5nLr5MB+V3hlvlzEb9iGFwIaXJyFOcMydx0nPce8Mgowq5uINYWUdARzWTGYro9bMrcWXc0VJWGs9evAHQeKfcG7CRk0mMGM2ANpgbJZHeUFPc6nTlNzYMJ7kotpUzj0Z02bYqjHsiSsD5doFoRmbSm7KNFQRFkROI6x4CoXWwJ234QWpkSykKWKtaPZeS0eGaGYxzsXsDTlcyAaCF86eQ0A7QduF4XLJcnpLziq6++wq67zy37RgJe15IzCYcWxbKsg1wI6ORDIl57mYCqDXaoM566o8+AMY7KTJPA6MhOMxnrozvkvlK5ZlKU4KK1iqNlvtdGCNGyLI66fenGVEkFKpxv0oTzz+wkPOtCB+fYmhvnVAxqhpISUErPns1jCvpo84hdSpMBF5TZTb22ciiY2FKtsU2HOcmOJ3778wKsB8xBtuQQRrSA7cSaS9Dj8OxLZIO9FtizqrPyi9rMiHSmzFrZY1fcbhvUSDq0eNK2FHdI3bBb1hUpCba64ah3308svidhBp+tLAUpAQXsEyVpG8oV4m0xgIACAuj9tGjg5kl5OqTL4Ve9MDEJ2l6BlGCHf9SLvZhNtpldEPPe726bnzuy8n2cYThH/ZPS+Ew9KuMMxLzwMGkimDBeO9dJvN9urM0BsbHmW2O+Bm7IRS/TXDIWDy7dbxu0Vex1h0jjuwBgJaOkFctTwmfXC477gf/1f/9n+D+ervjjX3yBKoDaryHWcLQbloWO2ggyCHJxAidp/t4TYA3SGpIqjtpQ9YDIimQHtB7uiBlEuI9uO+HfH54vJKNpDpOd1iGbPjOLAwBVGchigCUyk3TmRgaY01Ib4fzBdNyURBQF6u8FzlRMCHygJgyGQ3dmtCXYwRmIscaAC1KCinXCpGVZ8cWV61glwzThs7RAraC2yrlPgtxuUCkoqUAsA1ixu/GPwnUhXh/XguAljb3Ze9b6eq2tIcp0zbSvoWgtE/s9svcht3stlBv3ixOvZSoIbNuNPQcldV0xfovuiJlZr52PPdFaY69YSVjXFUUNaSk9IJaQsVqBoWFZV9Rj871hUBPs+4HWKsfjUGVVdYcl+Xv3+80OrTniwQy9WTsioDM7JnRuRARa20lFzwR9CgDeCsmsOXuo72uXoJAElYy9GbZvvoUq8On1E5gCDZXSowJp4XszHeOWWK39/U0op2mMQxhE7o12R3KSto5cCDkjAAsfDacevn0N+flpPj8cw9l2c/sIBjR1vy9kp/TT+jyfNPQwyvo6+46kwymRIOjM1PPvwxGOa0W9fKzB7lTJGEMPituwB+fkBS8/auRn5MujjXHOKJ4dyjkZcn6mKeftv5EwrCdHMsYKoJf3UIaNOmSxeabdrhCDivYWJAKBGDkERBLaoUgqyCa43Sv++rc3/CdffIo1A3tVfPWRtYq/9zPqwa9fN3x8bfhs/RRpyfjmY8Unl4QPlwSzAtGE7VDshwFmECgqBE0ykoljMgCkBSn/5Cz+WI8f7Cx2chi8zegJwtfj4jMAOEFb4qRpk2COiqALIcAeNl5k694aaBE/hQHiUX8qOwHcGYioq4gil4zIOLVmMGt902UhdE29/mnEmw1RhxHGW5tw7gBYWD/BYEKgqSqL5h8gvGf3cjyXmbnBzRqP6KlH5yR3QgHVweK6LIyyNs8IxXSzSJ71dcwkusOpDftxQNXrypaCpyc6O7fXO5IcWErCUiiE8kIGVajh9ps7YILttmNZV3zy4YqSM5pWaDM8XZ+RSoaJoXqD6OS1LJHJZT0eW4SYcfwA67O6cRIOTs541QMfnj/FUgpMD9y+vWNZCp4+PAEAfvvNRyzrArXK9gjUVJAk/j6lLz8zBhRaO9AQ9XdkVL2WCwyK+85m7NGeAUaIUjV1mC1JQvieFVBlRsi0O1mtRRN3vjcavf5cJmwYLgE9peMUijkJOrkBAK+NdSMnZzfqFCmFYZVwHDc8XS8oy+KtEcYOiT0Jad47Ukfa2L3NgNMCnIsMMqfWprDK/o3VFFkS6lFh4P6qBtSj4bbvJMXxFiz7drDX52XtWYR494Ngw9Cwo3qN8+VyZUBHgdfXm5tYrBf95LmwFjppDxLlaLER0WCXEyl5HzAZjeRr272mkfvNzBlurSJpcguMG1MsIUtGk3bKuPQOgx4lYpNnb8zhJYXpZCgEKQ28hyKzMeLkSmwx40GLqU0PRHoT5qWw7lIPrkkvbULTDQqjI4HIEBmObcex7RhoD0Kpe9wBrC2ryvrQVg/ef1kBE6y4Q3WBlSeYHajtQFoK9BCUVvCcC7744ye0PeHXX/8GxdjGBTkBGViOA5KUtSyNmaeLFIhuNGIOgdoCLYJDdlQzaHqCpme8vH5DuvyyoGVni9ZPYNKwN6IpnkyQ0sLsTQNqOzyKuaAehr1WMjL3iIGgLCvgstg0WrRw/bPm0VBAlq+cE2QRtJbQKht+I9Egz0ISmKPuKHmlXDOgNfbnZZae2rFpw5IXwqPdOQMEYgVJAcsKtQZYBS4H2ZSV8rvZQseifuqNrdnKSSzhw5OibgW3jecmb49xvVyQMwNurEum89GDI4aTTu1OjjuISQSyJIdPNq/RHYEF9RpPALhvd2ALeS1eC6ko60KZ6IomDNiU2F5DJKFpgzgUOLJWAjp0y7ISUgqBHhX1OADJeH56Rs7AfuwI6J1qZNvIyhyInZITrAhS8brrZthrkM6EMU2UTE6L990dezwhiHxGQGDUscnUT1fYasL3e4LAqqHpgbxe0LKvQ78fTZ8EtlhKuKRntqGoB6oZCJAGMihzIlMYxzzG4bRE8DucI46E5zf30bxeUeAQ1SmIdMom+7XHtuGfJYe/251+AKex8YP4cTgjrnksuACmcy3sKnTnu+cEpprBx8zcKdcYP5jyDeKOFYQ1tx091us3x28DkQUE0mm0ArPpvcb3MZ657nc8zmBN5V5Hd1IZyNXpNzw3peH0xTuMMcX+nMsx0GdsgmHjPK2RGIkxiYwe39FmWxKzzc0Ulkjs93wt0LZjWQ2lAA0Zdb3i6ekTLKZAq1hzxnJd8bNLgmjG81pQ1fD0tGBdE66r4Gld8HS94ON2YJGE3x47PlZDvSt2Nfzbjx+hLePDmpFEoUfD16/307v56fhxHT/YWYSdX6rb5H5MIafpr3gMEk2CQvo1ZHiLcdoE15SI/NjY9NL/O5zWcQvrdWZIA54y7n8WHAlDAcTvwynugitqh8JaPG1gv0I4iaFQJhiONCWBjBvo78fNnJSlFG8ozlR9ygm17ri/VjQd0MycM5q3Ocil4Pn5Ga0yk5NzwvXpiq+/fQU882MxxuRtD8S6YwUkFuAfipQyyrIgF4PUwx0Nj22mjIQMtYOR6RS02daZOa0BkY3qGTR30AXu5Jri5aN1Rspa6xxg7A7ycTD6e7/doEvu9ZMACDVyRdkdkHhXDHSyUD8FrIcLJVnClkaDewAkcEmMtuWcqfCEfckgQASJ1TVTimh8a6ybKVSgjNI7REkjsp+m9z1gQBEd5XqiI+nq4KxQEXV1QUYTa8znLgx/h5qqZw34faxnagubjK3WGnJ3rGlUOI0t132HZflohePbjqjdGsoI3pvTQCPxcrl4djqCLEPxx7x02JMUD2IEEUBCKQsI+eLa7PMC9KBMckIGa4om1TP6gtoOqLVeX4sOYwfCjFEjxFRbA4LgpilaVaTEtRjEPLEmY/vb9CxhvPYnnIxJjhx8q4YeBCrL4jJNJ5Zm9PfJOjjP5qbkZCzwzwPRUCa5yQGscMPEvdMe+TbPcOoY87btHsSK7JricBFnyev1DBBbcByx/Q/C5eoKlYan1Z/QCDvP0Ust+pQ6qU7AjHMSZM3QKmgw7IdAK2DYcPgao4PLe1dt3qtwQAX340AWQ17WkWUQASQjZRrcCZRh8MCN7zo6RDn7nFA/kKgl4Mj+dpVlBFkSVIaTZIpO+MGtsrtluCB5/TTLUZmBPBqQIpsR4EJ1+F/ZQAs+QQ9DSgfMCob1q7whKgzFdYmgvhJCzPUvPbPSVPp6MLS+nsRle0YsE3G4PoVab8Tt64b+heCyXqBKFAtXZO77bll4TvSmy5a7fo6ejIQ9h74WVJfbkUXR5ms/J1yWBTDKc+qJ0csYYLucQMlo1JmAUOGn6zMDBladzZF7uR7NmZU7LsIh3toDJ9GAnu+WYw2SPlckfb+FE1HrWHPd6Ra4/EbvQzxkgAcqXQGLt7xqlpCQoCAUNslCLeEQY/P1aRa6cLJLurPvcxo6JQwudP/J95O4DUCnLXTRIyIq3uGcTYuLnJwWAaKOPAKp5rqlZ+x8PrpP6QGznuF9dPxORuJ5fCFLgzk17DIDqM+S9fIjs+b6+sHZssh2vm95PaLL5s/f+83buYtzZifQHr4Ltd4NyTE3k96IPyNQ0wMSQUzVnW/XZqEDvmeMBb4GfK5Ci0Ugy0AuhwbApCFnw602bM1wq3y3ioqvXu+ALThqQtOEL183vL5sSGXFrgkfb4rXrSILcDsMt91QFbjtgrbvMCQnNqvYjwO//XZ7H7L40/GjOH54zWJfk4+RgNgdcEFi75wzPgrDT9xQ7YvaN8BsLM+CuWP0+cV5e0z7JYxxGqC8W9/AJzHFH4UwlkmhmSsjiwumd55nulf89gQ7mKJD74mobuuFMULN4N9Fj8GEkh2DDiO8CWfZGk+0LCvEm9PmnD15NN/Zjbsgn4nX5PfqGVlkh8A2BLFtcqWYNDvL3njG6XF9QG7siPTnCGdDXNFoa9gPwiJFkkfC3VjrClJ6DUY9mN0qmRmA1hp0U18TnBTaev6+/eUEa+p4/nAiw+AbNXjWAm7JOWjavGFsKLiAfpjLN7+GkiAonBHxOT1FNJ2RLqW+tH2uHhQH6K8NUAq6MmHk8twPKhzBnpmaDOSzQhgvKHYO2T0H+2zsIc9zng0Ef8GMlDYnfwknIgIuAadsWNfcAwVzpDhgYHQqOHaWGFpnBg72WHivNfq5Dhn1NdHXpD9HkG8R8EKD37L0dSAyweLU0Mw6iYqBGQgSmsSb5J6LeeyOGe05NyzDGHQ3X2IKpcuDBEMwCKfEZ1sXZkQTDJqjdnIypj3HEO9FW+yxYRgKaDy0NgXCRJCiX5jQcFJVJDBgYr1ht+DYyHwXG5zR5oTsqZAeFJHC32XWuMAUrRokK9bsxrwJRAmbUndoxa1DU8NulSx7JUMOEn1oVRw1HIqKFmvc35WJZ1NEHVrOBarRC7AMRwQmntX3WEesC19fzdlLexsEawxuuTGvHYqdvCbOdY95/70wwNW8fYevXe8Nyr6sdAd5jkAkShgaRrCSe6OqIclBaGkTd4QM5kyWWZyONhkAhWrlWisAKuFk7CfoWSM11K53MeDVLg87CZBxnfb+bilOi3O1G/6xhkI0QKTDtQPSm4Tw4ozY02GYDx0fDkxkyyjYzJ3mc01Va+5sags+apgZWlWYNSxrOGLWyV1yyVCtvowZiKpVURvcWfR9IV06u3zwGveQhSEz0si29cSbTM6a+tynsdfDXAn9NoJgIRsw2QDq80FBIlCYeO2mS/1RL3vWW+Pvk2yWwWiNeQ342cEgPD48W0DzVeOck30RMhtd/CFstPEb61ex/m58yaQ0jWm2805T26/x5tw3J87369bi+fMH52mGi/JSZ2d41k9nOOn47tFh/K7P4+Ee4avjm8kWDbENeTOmx+v15xWZxT3HSOsK4y3E/I2xCRyi7d+x+5oHmlyH3A5FbYTLJxi+uR3QSihpMwNqw28+7hAjl4Yg49vbju1oyHnBXoFvb4r7Vrs5UdVgJtCW2E8YinJZoSZoh+HblwOSH+fwp+PHcvzd+izGWg3BMa9QgCs4cORhtPQLmFO4hwKyKQRGJdQvZmGMTYaixaYIQeln+6aONgdAw2Vl5Pk4GMlLyckyvANtRHjmbKLqqNEwDUOSm7ss3q5BQ9FmN0JnIp2I9gW1sTm1ej0pxumJ+wR2wZKA2+0GrRvrufKC233D/WC2JCFhvV5g2ti/UdWhsIbb6yuOo+K6XmGXhLrvNC+N9XzhBBO+43WABqyJdOC13rFtO5ayIGdAkjd4VqAQIYTD+GwtjGNtUGnIAogaLLPWURKwlAsOVUimNu0F7gAkZSzlMqnqSufScmenA5yVThIMbPnQDoNY8fdvrpAKLkW69F0yl7ip4uuvv4aZN2sWQhyRHCodbS1A9shvvv6G7zaztcW2f8ta1QYA3opAM9QUu7G5O31uxe1+Q06C46DRU5s7hqDRzr1AYyxawajXVqU8MlimgRdxjrCUybBqkeEjhIvOB120JSekpyvhl03dDSHMSN3gVXNWxcYsUhKgZJKkJDFAo8um7y/QiI1+Xw00hk0rkNRhle64mTOQimDNCSlfvE5oRPEBdIcuzCJIJhlStDFwY6spM+jimT2DwlqGrSsaEtQtNTGgCGviSmGbEtWGlBbCXTE1HncDgs6EQ7lD5kiCoRKq5ylkEUNSGiMqXay5XPJc8eSMJnPjFjTCg6BKnJDHshvnKZo4c25TyShOPAJjbZHZDnHooRn7kjKnol5Xl3s7m1IibCFoaFjyTHKx+DVZJ1i9JZAIsO8VAYM2CGAJGexlh9jPiZT3QUZhrQBQZNkgmgmXhC9t8ax2cuf4cHfBDEmAnAr0AF72G3qNTs9C5D5OimjWDnb6flPkFO1OBKVc6NRBoJbRGvCybbjfyT6JHAZ1dXke+8uDVZ0IK7GGxxpSAUxTh61lDxjpsNz9vQjq3oBU/TMAdnA9ZieWMnNkSIZ55hjumPKqANriQzISPbhTR1bOBil0Xns22AQ4nJk5kR016I4MwL41wGHScR+LNQvr9eJ8HOq95sGylMKH80CdCG63vTs8DJTFdwOyHQEM6ruE4gRra1ldZ3PdqQHbtkGS4HLJgBp2sGcjRHBodYIaZhhfb3c8XS8wY+340QTtEJiwVj95wBNWcb9/JGGPFMAEr3dFVQYc4n2XRBTOth2INMZYA9wD2SfBXE9Z9AScVDZ1gzt0iGCVOepIek1sh9d6QJZiI4I8fGbWjjdUu/jbUjSLGsqwSfhnC9bzB3slnM5ul5lR17hcY9wn+dXFHcvQrcOJEQxLRC3WD+WR+efmhlwES83E28S6mytg9km1s7mm6E306AQNs8e/cgfWcHr+87PiDedDZFenD4Yzq8FUPgzQOPUxCBqw0BlaeiJmwkAujfueS6UGrFVO5z0+Nh6eKYKEnWxxdoSHJz2NIfajr0Lfg/GrDv3G2LMAIciGePeeqfXv9m2HCPBnv/qGPZbd1v5//+LbXgceeuOrr3bkdMenH674sCTcX3fcDShSsb1W3BU4jGi3iIxzbZC13ACyGwNIa8ZLIxLtp+PHefxwgpsO6Zx9xGnhOXTPBIRdPDpHs5PoC5JwKZCMomsgO92hy2yRLlRm+CidNO09EgMOODto4sI0MOesm1v6Jg/h29pUtO7CcgYRxtGb3OOMX/dvu6APBRx/PxGbROQxjDEzWM8M+D0TEHUGIbiWkqGacOzbSbhs+4ZjZyPy7QDqsXfj4fFFsFFy7g4x55S1U6lMZBAyBI8qFUBelpMCMDV3XKlgWmtA89yEahdgkspUa8F5zg79ajrVhLrBTYNeO9QqnAmSyQAwGrxuw3vjeIkUoytniZiFO1+xvtgnKBQMs2wulpU1eeSTXFyxC0wbjj0gmOOdUpUG2MkDJUYyG0uuBN0oCfIkjnnU7s1QTTyuifj/OzUm4yWgv5PYK2xIL6dr98hpKEyL9RA1SgboAMPF3svuLbXaTkqLgRWH2klk2FmbNB5n7EFgQKLNjY5ey9Fri5k5TgCyFORU6Dj6/h+EBYTbrcuK4oyQpdABzf58p0jxbGz4lFgh1PNIDZJ2tPsGwGs8/J0FdIr1lO4oCzO96u/FMBAK430HFEtiusAtwbmJWpl4fa1FRsmzF+JZL1hHNiRrVPzK5wkSLjMF21dO68aNCTr5hC8y05EYzQ35g0Qylt5+Jta3TWtRu2ESjk/KDKAFW7Vvd2b/tELEcL1cEJDElBKWstAodVi/AajbgeOoEAEuqwd64MygJUOiXQjg9b8UDgzCsP7wSTJUbziajqx3osGcU9/9fiRPqEc0h3BMTcOZzvTscbR5/fI6lkcwJgKYYuq1Wtw7yZ0ac4NJUjjG/ht3hPv2dQeaxF3iwUYBckKREgoMez2GUSxDsmcd+78vcPE177DUnkWz6NnHoFHsv6Gn/D/9elzNZuQHYHsgnVa4AaiUISXD47VcHwD0UOz7zoDO0wqIIXmWNkAQIoK8ZAgyatv7OFLOWCCdMMmADvtUVdTmgZDKjDcDvhmtsb9wyZkqNGSbEE2RTizlQ070Psr+biILOQ63D7jj+5aIdxjXCqbX2BcBFezvfH5Xj84U5q8evzt7sD1r5kiWR0xfL8/w7GVcYb72vA4HlNl8v7lma+OXQ09PzpSLDBEPzr33nH53C6fQdTO60/cW8nly6sxGsP7Rtnw4N3Teo2P4+KwjqzjgvvP1QnbNjmEkFqLucFzn/d+/d3xXRvI7n6df27qT148JpjwCAC5cZ/b98eYxXEZ02UAdPhzP5LXdqsD9zn7KS8lcE6ZIBlyuVzRV3F5vZMrNCQsWAEeYFv6ek+v7QUopvZ/599g1Px1/r8cPdxYzGyXTdhmRkZHm9xhZKKY3gmIIBxh6+xsJ5xLTgo5FNgnciCR1xefKUoRGRM5UGqpUThEVSW44mzUUb1jN2gVCH6M4PHvT79xp24fiSIkZndjAbGxOCMy6rqxVUFK119o68Y2ZjlYPU7Tq8Qjhtiwra64m5q52VI9+UeDv+0FB5fWCkjKJXEwGZXnOyMQteXbTiXIMaLXh2N0hmpQDjXDBcexsgl6ErTg0sgAB90xQPaC2oOQFS064q6EsAikFhtHHrLZGunug1yWWpSDngk8/+wTZsxfq4aVwd8II2fYNr68HlmXphD9iXowuXMr70VCrIS+EKWn1CHtkCGK9mbrwNLQGzw4PpTIblUJPFQJFKUt3VGnAkEkS4UQDQDNmyKIuJJHSnsHVhiCBBAS10iiPzEnzf8cNVBVIw+DXbrcFdA7diVE1NqKuiufLFakk1MboemsN2gofUQm1i16CJgZTf/fKusc4cq9DVTRRqM9TO9gEPTKikgbjHxsvezbzRA5hfX67epoUmmp1EiajsZ0Tck5Y5Upin7q7kXhBq4QvEzLt/URbQ9OKrGTN1UYCopQztlpRa0Xz/acKrOsFS1mQJlKhIgtyYqY1eWShGWDNnB49e4bVyWCcwKvWoz9HSiSB2g8S+yRhVqoe3uMxmHWJ/3EIqWHb72iODrher90xC4eq+n5e88VlECHAOQuqbB3W54sOzbRDBNVItHJJFzRpWMowsqqZr0samOHYmPC5u4wS89rGyBZkpMSeqMd+Z3DJA3Tqsi6njKenC+WZsxybNIhYkCZ64M4JZ0Rwfbpiu29oarhI7rW/1rzBuRPTHJXsl8/Xi7cYSchZkExYJ9sa9lYBI7GWVbYNGEE6kl80dZkoDEyVRCIsFScxAjOll7xQnpl5jZt1fVR8/5JBmYQm2QMZkeVZHNFiYk6+5S1skB0mqWgHDc+yLK4fGuphWNeFATPbCQVWvvcecJmCaI+BGX5AeUddmYDGnoSpCERBsrTkVZ7V59h7SKZ06Vexk+OIgTaPPaKKZV3x/Pzs6IbqTiWDQWaUzSmlEdzYK2sWu6PgOitl4BY62d9bSige6Glew1yWBbCGbXuBpILjCJ1Dx1csoaSC4m001MmiFObkdBTIox9zrA+2XOm2Ns7tP0Yd+hQ470Y65z5abXSiHx0BaNovo7xCZA7avedEjM/nwPP7p1FW8a9RohHL4Zyxe3TC4l333sgup4YTZN34R7/k2Y4xwlT64ujO1nc4wxEsDB307vNPAQzxAEh35B+yfWfn7+wofte8vhnT9Jvze52cNRFEXfV4H+n0TuZrPN7/cU7mIMPj73/XMXNkfv3p7+fzJscQw/4GRnY6Ajhqiq1Wb3WVsF4KEYM2gjzNDMd+Z3AQXivedb2eiIPUag+gwh1HrdHO7Hd7Nz8d//GPv0PNog0tgcc4CjDHLqa9hRENs4e/YfwrHMAIH8YF+kfWrxUY71mBMVJKivScM5aFULTjOKaoUGyssYG5mN/mwQM2Es8dguEcqRqKpLpDxxqs4q02qCzYKsDw3v4fUU06rITUCJZC5tPoI5iqAWg9cpckGBMN8IzMyMy6IPb5i+qC7qjraQAuiMGeZx5tjv1LGvLWqbebNti+9fdTlQBFOkwOl8kAACAASURBVDUGdTbEMHaDElp6xGr6rztw2nNY473PWR0zQyml9xMjlb369JpHommgzAo5p4zLJU/XiYyQ4eXGYEK0QkgCJx5Bj2KnZYEZUJsbCs6cJwn8MKL3bpwAbIKOU1QTPfrcmV5dGJvqmRTAj+6AYV7iI/o6vTxYY19BUusPBRo1n+dtGLsPQ1NIAmywJvaA8PT/UIRUctkjx+iMk8l7knYH0QhdflTg/bo+KT3gI4zRW19z1tdqYoEYYANSnjMN8BFQga9BfyT/Ld9Zw7YzKGRq3hPOW+GEY27jXfUxesYsi2c01SHZToBhho5CSE6W1JpOJB3JDcVJXhg6ZN6E7X32o/ZMWG1R3+b1cao49tbhzMx8OPS8ZCdbGcy3EeyCr3GA2+Uunj1J6J8rwLopBPvwlF0KGndB30uY2tn0/oI5d+NNRJBLweprFY5YYGAE7kB7hNkiAx334tpM0z3mbLRAprYWlLFHVdRWYaiQw2uYlS18eiAGgHpmqa9r3wMp+XhcH4RoDLp6gTj5Cx0Lwahh1V4/O3RCSg5Xd3RD3G/UCU0QMYfWwzQSjdNvjLXjD1kOERC/2t+NZ7W6Qa6OIogg5aizDd1jPbAjCAIk4sT9feSQAWP8sT84slHnGQ7OkAuGuh+Ivr0hr+Yeq/PenjM/4ezW/cBhR+xAAAxsVq2oVZEzs80iCfu+Y9s2eOtYOp3CfSsQbx81HLXk0Hz1tWtg8JKIDcBSIATG/gfeGvbBeIlY50CfL399Xf4/NoOP42/nxLz3fcjuYQM9OhcjpNjv+jfcw8b4If2nw2byz23I8BFQwjDx/EfvOTunzFe3yew0tDcO6IMT9Zjh+12dwDf3/I57vTdm/zXmgX6fE/x9x6OTGDJ36Of+Lcb6mr86TfRkXo8XZjLGOl1lvNN3xhK3l/nvErrcsCxMAuz1QBaBOLqrOkt7zudEiKT53zGCsw34t3l3Px1/P8cPdhYZ1X8bCHh86TL/ReClFeaKajrR/HOMa0b0KLKVtFkmYYEO7oJhjiwBAcGg4ZqcLCGiyl7z5Ys5eVuFEP5zJm9cQ8au8Qi6TQq/KwkZTmPAEwB43ZIwy4A6OYaPm2RsqFoV65oBZ/7szcBDOeeEZWEPxZQTpLJOLTXFuhYI8oBpupci4j0XI6wP4HJZh8GL6NGV8cUXnyOXC3IGluKvrwFVD2ir7hCwQf26lD56IKFZxXVdIM5kCbt7fR6N9RSQLBe0c5YBMguvUEKeZbEBPQT4SpKNTEH2SK7FQnBHJi8LrtcLr+HQSWZpEm539nJrbkzkwj5fAjolZnQeVRVVG7Qp9hoOAzNiZnBLpSFJQcnChsuAO5as5Ujgn7WR8e/p6UKYZ8ACZxpumda7L7+o/uFz29g2tPWRPRtnnkl3Kc916P+3FnUWCSbR6yz2o/Q6H4Gvp8Sef82NBenr37ozm7yNR+77YJYFQ9ENiTDVWhi81QZha5LIkBksrYR6Jix5if7cqPVMBMN2MBPky9O3IookhASrGZ8NctrfccwQ2DMcxidY8mT8eEYnNpczwI7nAqoqsiqspO4p0FiXc81uGNiqMCRkJNRasd/v2A72/OvGTTVsUXvtjihbggRskjKl1qlmxqzvnd12nu8Mp6NhOvdP768HdIc/LEc1OAx7vMtgjpyzin1fRp/TZL0lSEoJJQ3YZdOpB52JZxhbZ8496sHWE6Xw+mBwSEHW3wRgV0KFa90BCDNI9eB4lxVA6qyhPWgBzokqnGPVWQLh9U3CkIM6KUNOGUsa8xnBqKNWlFwcKsyv1aQzUyMJVu9fqUYz/KjscVtKQsmEVZs1KBRliVYeUeIhzHK25m1nXKa5s0120AiM8rPsm011BCiWxfVe7D0bAcbIiALUMcULzSOLWLLXayqcCdGdxtm29713HBtUrdc+Xa5XrMtKBEtzts9MErOXjy+jbENCkxMRc7/fUGvD5coMKzMXG/aNtafrJ089Y36/b9j2A1UVcjSv/S1QMEOaWYTaS+YUAY9ubock12e+dgOW63XgYXvMqKbYGLP65nly+veQcvNv+Y2GrgNlUp6M/PePKbtlwy84Bbbec3JOVlOMnGPR6XwJLzdYXwX9ncCs6xRL0q84346B0rDZzvd5lLfvOYrd3kLs0bfO3PtZt4f34OfOpUfj2udz3sv6zc7f/K7HZz7KsF0e7Li/jfN6InaanUcM3f/ge/tn3e3r59g0Xr5GGWf5sovK9Hk2prTOyfWMMRhYIqRGG6mY2xbg3mkNrmN4LtdJXHEkX8YzzaOO+74dy0/Hj+v44c6iOf04cJJYZ+gLHjYxutM4fz7DS+OTx89PwtYmATRtG8G84cPpG3VU8/EmQtU933kxD+dltntFQAhPGg5pZFoljSi7HfqugHxPF5yNa3+GbpB63x7MTqx0Ya9T5PNxGs+ZVH6sqqhRb2GA5NKblltzpzElJ9UpEDGU7L2CEtC8r5cIvAF6HkZMqEcD1nVBXujIT7bx40N3hRTQ2BBVk4odayL0qf/H4mbTo3Maem56TMbp3ujO6vhI3vyt39ONCqfz4aoT0KhyqJKAxkvUBj5G8/u13Cg+jh2Xy+L1Do8KEMOQ8H3T7ZDTZE7ws2n9h6mZxqcU9n39OIxEacwHkYMIcLRoXE6IddLkQQqOTU0g6jVGXVHKKQBjFs4G//92/6Ffj9kfEhcRaiY9w59EvIY0MtM8OuMrznMb0NHTYoh36qyRkYmZc610gtP4d38ZMd2p77cx39LX2SkjLK5inQDEprHEmgiSiL5OKFSmcfK+rTXk2QnubTDYA3NZMpbLMmppXG4cB/uM4mGOAoIetWoxlyJejwPKGxX2ZBxy1tHLYQL7OoqgD7MyCpWARY4eYTlRPkQLoLMBNxnKvoYbC90BM1RHg4gA2RJawOh9rVl/ZwIDe+nmXFBpwXQZEu0/0pL73oAbQEfUQZbi9YmDLKY1tl6yknApCbDEOtHW0FS9vjIcdi69hiBEA6CE38aaMtBBr9UDOO4AB2u1pOiz6eM3MhAmOCR07CCuRwVaVTrkqYR17gGVyucGUMrFlyDldvNxqNEJXcuQFHBnurUGWKAVJhljcNbY1JEnQRrGAKl2IpYlgjPwGsKovzQicFKKFkPj2vMhHvwL5zS2NHuJEhIsiYRdxx5oGK63ZGlI68kgj3ZCNmf7Bh0spmjIGM7D5+8eYcgb0BsqvvOzqZodYwegt+kIMyl+N+vuh1sNpyT04LvjemtdfddjyDQHDxJ7/Bmv4r3bycNfH73Zh/vapO/7CP4mJ8vG3D3eYnZM3832fYdT9zZrPP9bTveI799e+u21H7//rnH1d97v+LscZwvnvW95X5zX+ju/O9kn/XvX32LTq4t2Z5RfBkfAAQikQwseAzvb7bNV9bi6fnISf/zHD4ehCpilABy+c7Jk3xEK739++s07Rxde06khHLvANevLm9Fiw+hBF/cP2MnoTdc3t0ivWTGLPmzikXA23I1RillXnEFYEGNYSkFKJNOJfmhdEQpoPGp9d4/zMiMbEAZoa9Wj/2z8zJqraMMBvL68TCQf6plEw7bdUY8GeXrGsi5o+9EL+1VDELrZVLWPQYQ1fMe2Yz8IrxQBLkvuzvFRay/+RwFUKyFyYtCccRwHaiWkriBsQFeLiWbY3JsJwloS86a/kSWejewe5xJhW4sszpCbHL7p5p1MBrjBCRgAaxWtpeGYGLPjR2QZRSBOJ5+M66hDR52NUcUhtQhoJ/H2odUj67IsxVn3+E6awN/RWYmkkl3witcfObGJP0MwpVo8E2jQRzPrMAoZhQ1CBnFmUw+jF/5xaEPbFGk3ZK+9ivrZtbDCAKYwq7gfJEaKNSVQ74vpa1UVuzNwhkNrSkdPm55qqSDs+6ZuvEf2zy/ktS0kZGhm3l4CrFtRZVZDKQe6EQDWiR770Rkiz02LxeHegCAy+pOy6oanN79OBS0cVRHU2noU/URnMcm52GuA15pZkFyMuueoVyIcjs/wIMpO6yGcOjNDWVZcRZEvDuacDM4klDG5JCxLwdPzyj1/vyNQB09PhNg2d7aWyxNyStj3w+vKFgAkzen7zAwMSAGw0jNPrdcQDTRFOI2RLVRt2Lcd99vR5WYwYy6FRDQkOpJpTv26mGp8UkJaE46Dhn9QuQe0EgBrlj1wEzISANaF9cSV7FtIFllkoFXCYEsp8LgWkggONRyHOctqQi4r31Nm6xGD99vM6LXUzHwEaoCZP+nOljGjKsnnlk69WXPGTIGhojbDYgmC7PpLUCJzyImAJPaW3bY71pKxSnKyG5cfqcDQcByEJy+LdMe7muG+HR74YR0oAwJEtdQGvNzuOOqBp8sF66dXrt5MEqR92/HtxxuSAF98nuH0LgCAY2+43TdISrisC5YcunBAsYdqJZNyrKHQeyklR50ENNeDDTBkyXh+foYIvMaJey1fn3C98vdNj76K1nVBWRjkAoBslOF5TVDL2O61j529iR2C7iUhAJ1S9thElxfBZBpycDhx79gr3kuwB2X84whgN5ucx776hb/z4FEEb2bDPoXRbWGVhMkeMky6DvouK+rNeMUhhZgG/HC++TMZnFkeoVfRWa/DFpszggibq9cfjlpOM3PiOcRMYkZwPAbTv8vhmh7jzTHKFtB1wvzbR6cwjoEGe2Q6nZzKFAH8GPfILMZ4v8/Rfe++/XzXLY+/HmtlmuPJcX9MlsxQT15/QKW/a4WcbexYY+PcLBlVK9mHBaje2qz3KoXrPhk1x+Lyy/zZ5ucMVMbZMf3uWMdPx4/j+MHOYocoIbJ7Y8MAMqBxbpQ9HqdN7NGL3yVt//jbM1xgimKEod2L1q0bNnFeh5uqegbNnQz1onYX8F1QImq2ZbCiecgpsg4JLqSUsJ9eJ5Gdoj8Y8Pr4OT89OujXh48llQIRr8mjZdz/L+4kirBGSJ1gIHkJXfSvgupJgF4uKyn/E+9Vj3a6N/dw6iQaEfWG0OiP+TAQhpV8rLWTBXG+7/cXNA0H8MGJE3f0DDBteHl5RSle39UDDp5l8Zqeo7Jlxn5sUGOtpnpvuCDOCeM3g3Cp5AIuxCx7VbL/Wwt2BkldqUu/NTMMfBTFNWV3Jjn2wOVHtJxGhSApeh1fyjItf2eJU4MpiX3WdSGxiNGYiqDCcDLOARE1wgt3j8jTwQvrS5y21HBo82CFou3m5EzRxBd9jseuHY4nM6aJxsvwqTrkNjkfbmc87nvQuhrLSB544b9rU2htSMl6C4y+f91IbA4LW8WDKxqwp4hiemZPvQ9ga25You/lEacaRkvAz2O/dcbFXiA2AgxnZT9gOALPKPoDnYwY4x5jRgXeosMAHZnPvrd8fbHmbvR7i4M2ln/mJCjbtvc6u+y9DJs7UakBQMMzrjBT3F53BmlU8dlnDAi8frxBBPjESJry5W8/IiXBF19coI0QvtoUl8sFT8+EXrfjIDvyVp1QBk4ekx3eaigl92BGSgnLssAUUN17qwugOVEU308Y3+FkDyfZYb825ETOGSpCBSXA9XLBsq4wbd1swhFvikDSlEg01VoFIkMYxos7aj2AaGH0+TrW1PcwW2iEjqEMyikhiXo4LwKN1mUw96z2Nf8Iq5sW/Lh3kKcYnVL4ugEofwQZKVkPAHJNnB2x+DOl5ChztlzxpdKfIUlGFuv1jwmZOq96ScK0BnOQz7gxmnLupCM0UBvIXyJ+rmfqhY58a0SeZN+zTWtHCnCsmSWdESCb+llSzqgHA4CqJEI7oTWAacwjw/38/AytlXBkU4gUQEk+5TsdUZ7SCYrsHdul27Vjj39fVijsjfGKh1luEeRheX13Ns6ujn+m495x/6g5G+OPewgel9V74/oux9ZcjoVjcM7284LBNDvk5HBSMGmP7kU/OHt4mK+zfff+PH5nRpAnnJ1Tt72GQzUG8ybJ8Ob+eFhPb23P4Sj6XNmAxoazE9fQySn+Xe3YcZ937GNgjD9SzogPpf/V4jv/++PKsmntft/x6KaGPdtvCRepIkBz1IV/TdHQEFaUuKxC6DiL+XE7XAY4u9vWEnricaX/dPxYjh/sLEJotMGCyX0uWI5MSkBAp8hICBQNaTQMpC6k+7YfhjI8ai8pDGc9LTKe50pW+GtmbTw66f/nGKIX1OKNnQ21NohMhmkCtDYc++5QGO0CsbaGdhyorfaMRSkFx1EBh/KFYdTHbwEdi+biMR9jzIohDDle4PlyxbpeIGJYV9ZRaH3By753Nj1xx9FAOM66rvj00w+otaGkgmUxHA0AXt0ZYY2MmTkL54BOBhmK7RX7vvEZUuI8GJDXgqWsWHPGUhhxXVIms5z3EWut4na7ozj5jiTBWggNS9lZR33NEG7FGrtaSWLQdO/CirbcIIooOaEeB47DhbNGXVQII2Yx1JRZrpjb1vDN1zfM+i0J4WH3Q2mY9bVDdlwDSAGdSo/IA9GrLgx4QXb4lRmzUwnMkJW0IAE4Uh3BhsTathaRyYCwRhCgGzDoiilu1pTZwG07kLyGiuQrNCo6e13AwyjSyQqMhVcUEg8ZGpKwdcFWD9ihEBXkAogES3DF9XIhC65nAVs7WAPoUdseDElnYwkqFAyu9cydcuuRTsqLkot/R5mSCw3TnNiaou471ssF61JwaMNRD+y3gwZ8Tl635nDHPJy6JefeGzJqHgcLpkMuQ07lhOJZ3pwzmi6oMKxGJ/q+HWRrU+ttIIgYwCRXXNYlr4dTQxAwhcMYRoY2paK1XqiL4yBbq0HQkgCWkRKw5sXnasxZOJvaFPXQ7pxdr0+43W7YD/ZHpdi5dfmZUoZWyu2CBXvb8frtDbfbHZ9/9ik+/+SKlIEDrHl+efka27ZDVZi9KQnbdoOpoXzyjFwKvvrt19hur/jFH/xhZ2E2r5ElPB1cO2a4rBcwU9mQJKHksYZSWlir2lqHkKYkyHn1gJ+SYRkLjqOh1dadzLbXAccXAELSoyTA4mMQE4gCrVWI5KDwQckFRyNxSmsVVqKdkoVt0/dgygKtCtWGQxP2vaJWQvRbZgZejX3+yGJtHhCLsbkp5XoT1sgi7IEzwQh6kOE3Ue2IuBPIvqOxDpbMtc9siiIn8bYdhuzzW4OtGGMfmIg79oCI94A0yiQRdAInIHswy3uwJkFKBSk5/NO4B0v2wJIqcmF9c4YgF5YowGV4bQxk1H3Dtu24LivKsqC2hn2vWFae21rDy8ePUG24XC9YV8+cbxuO/UBKCetlJVrBgFwWysfjgDb2Vc7JcH+941YNUlaYs/iWkoEUGXJDU6ITciZyB5MTGZmppnPGdLTXmss/hq53BZNHkEQsgssDSt/lUJcLb+UodV72df43NaCL6w1De2ZofTvOEZycHQX1tanaLbYRkGuNmq/zAhiCFTPI68zb1cS9HluJdXTINKYzM/xwXt9kFTGRLLlzdLIBu2eO6XqY5vt9x2weV58HL2EKO3ac4yzBXV0z2DJQQ5GdHEGd73Mge0CxP+M7583PNJ1n0//SZDuG5veQwvfe93FcjwEus8gkeo9P5zLojv0UODOXH6v3NVdlex8R5yPoIQ8Sd4kjs4LAjY/6t3O0fzr+4x0/PLNo1kkaJATOFMwJQ1cjAnOOd8RFEMIBYITi/bUiCIghJsczhIPE95gF4yyQrUe7xsYdIx1CSgBQGUfiIeB42Sl+qXANyNKzkaa0TMLRMMjpXrNiiHuOzCy6szCC2QEvVDQ9cLsdjBqXFfAm5RHpY085n1+L6FKQKChSUo/wTP0SvR4Gxp6IL693BIxgXYv3NzPsB8kOUmGdz7EfuGaPWqth2xS1NdyRvQ9avD8+99EqtOUYKkSSO8msdxEha6ABnuakH5nEs7yK/n75ihy6mvqkORvmpABiAaUIL/QpdiX3qCwES441zDkBCOHyJwGsQauvIWuTcLSu7AKRbF7grwZs29EdP54vp7u7n+B/mbJ8PgbWqUaT7pEZzTkzixKPamDGQMRZFq3X7agJJPVJBsJP9+tyvhhoiCBlAh3gVAL+63squUkr4sXzAz4bT5Q8paFiGMBV35cRPJgc9g5pymnIk5RgqKTthq8bMOOx5IVEFpI6WRBizgEn0VDUWnzsDQWKQ5tnxdXHSaP9qBsMDWwJY6hSsbeGdgzUAcku0NkX/YG4Zz2AxP1FAyGeK7ujZ/7eOsur0BBRU1CFUkZEo+Lg0sppgdm9IwBoJxmY5OczJAH248C6LtzX7iSFEdH98D7n/Gy0KiL8MedgiByGcM4FJSs00YkvpUC8Cbo41DKXgrJePSDYlxN637Fu8EY7Bu+9Bm84j0lu6ljjNAzh6AhDTuhrJzkB0rbvyClhvV795nyeptoZqUkGpd4iAb0npbkMMVM6Ek2hJWCJro/gQbvG3++Hj6kvaspnswRJhc+iAUF1CH8Gxi/E5R7AIFHuH1PfCIDCgI47F2okhqFcccIyC+CmsKbSHbewYANGyFYEDsMFADRASTJjLSOCruZtNaI3XxLiMmzKvofuiOAI33f8ORn4EmtAeg3waN5u7lAWd9p86/p6CTSBCLPgrZHsJsQ7dbH2GnBxnQYxD/QwSKZmsKpoLuySJFgyB14YxGlTLfRmGrr6rErODobZBEX3wGqgNc6SHY5DjZKVqO2NfRnXHLZKCPP5XtzzI+s6f69TsLmTk8m0ps37g7o+Pts45+t1x9iXardbJjOLWnsS3GGPwYVVfC5D555RX2eH7NTo3nrlNzCdMztZJ2fuHQfnvd88ngMfrjzM9Xf9PWyZ6So+3+OZArHQ36bbtKcRdJ1rp29mxyzuEjL05CxbzPUYB9dQvBEZn7rsn4TUdJ9xj8dD5muc5mN2DM9Z+AGB9V/6P7pDH7aPOYpiHkPo7BAxoQfeG9xPx4/i+Ds4i9OWODlfAPrWCUx1mhbq2LT9E68VgyueYGYb2cj5utOfItPmE0TfvLi4EBc2CZOAEY6i+SEj3mY2eRCCRMiMeY8vEI7n2bieRfSNKrQIMeomh5DtGzdNm1pwEnDSx6+odUfdKnLJuDwz0ksWN8+A5oJWI7IXzJYkD4AptLgB0htiufErbkQ14Ha/Q4QMeAA6s+lRDakkSKaxd7SGVWk4myrud/Y5U2OD7afngpSGoGqq0ObCQLwORCuNzWTA9B76mqC16Eaku3syonUxYZHxzG75diMuGRvCxwV1rvTw6KeN78l0aEgp4Ca8Tj0Ofy8JpnD4nRv9EkYQnZagqO+Gr9AgJXHGiCZ3hS1gne9stU9v3kI5GMDMxJnJkW1Ucmgtzqe3ZwlDrmeKkdFZhpH6dSMCPv6bYMLGAIUPyulDEJUAsGEAmvQndmXgARB3umIdikNVQ3HMq1y9zQtEyL7p1wpSC9pDqf8oScJSwKbQlpzlVCYWUvZePOJFwiBi7A9pBpjCW2I6U6e3XLAGbU7ShIajMVhE9lL2ywwCpxR1iq0Nw8j3b5DrREac/focNpm8B5/LRsNQkgxKCRYPfAzi5YJgdQ1NOomrPpd1P7CuuTsYwWwKN2PC+IgrpC5ruMdyzp7xH4Yss338XNT876lfR5yMJ2UiF9RlWUTdc0ZYT77evXlzhygbIyr+aAE/YhDAZbSw0bo2gxU6xhJizED4fAGWT0jeErDH3b26w6KWNJiBzTNqI4jH+vIwbqI+XX286DXCpoat+joWgbiuMa0wLF1WsH4/4LmB1JiMy/7iIqM41oMq62chCtWda9sUzSp3pUTfxqmtStRke9CGWcNR8x3TzOdXUCBz/4AruTsw7nf1dxvOYhjz6vs8HJ1Tuxpz5ykMyiBNMnT0weyYFC9rOOl4149sAO71p7V2WcX+yUCrR98zw5lQiEcrW9291lFOQRHKRzrISSLIKH2/xZz18WD6LWJfCyKLljrp3AxBdP3kG1z8nYf8MGP94mzQP0Ihh70yaoRP9olZl5shM7tO9DXOzThl9UJQIeD2Md+jzVBfMHG6/5c1Zg+Tcrouui4Sn7zHTF2MO57zzbNGlO2d4zEb+uiEPl7/vfuN4Q7n54xKG89+/s38WTzmaXH0eek2pgc+4tfUUTj5fI/OV/9ccJ5nw+m8GI/5ng6Yf3/2+DfQ+5XPzxTyvT/Mw/MKRnKlywTE2rU38zZdvY9tLglJNDknxS/d/grZNJ5/yIOfjh/f8YOdxZwlgmf+xkM4hdQNwwRdwfSospE1L47UDaGHxR1LaN7sYkgRvpz3bBiW04eEzbnyyRP9vDtxBkIKchYIglqbY0hJkJeMoB7PMjImhOQM+vdcWBzvA8QMK+mCKVNB7kfrRkqfJ1MGmfsjmdOkMzORC5u6Rx9Ic0WfUsbz9YL7vuPYd5C8h1ZaNB8WMezZe5y5oCMroTlEyfDJp58yQt3MjR46ietlxeW6opQEqOL5wzMbshqNcjNBWVa8vm40lNKKZUneCFuwrAVpzTABjvoCNIGUs4A2N8Rv246Am2WHp1kL4c7VcbksLKpW0BAAcHjWR1yYHQ6fkbSThVEFURMU7SDCQA/iBPehKSjDrcz+r7BOPQlgTR1+TfbBM+lHYPYTfy+LGwVe0yaAuSPMRuwNuZRukHW6dncEyaiovSa0N0Q384AF16M5rFdNfW+M+WXvvuEscDhxzTSUlkjPnqsbQKZUSJoMl2B0pBZihvgUrCHED4a+1+I7RessiApAJqbM5Mo2QZBK9l6oDCisS4aVPCL4vca4uRLzTLUYihM+fXh6IgFKTjRyGp8lCIHgMGvW8hK+x31c2Fg+Z6Sq2A6FtYrALdTWIMK1GgZHwMeT1xDGM4uFqxbthSIDEYZR6kqSpD6EsVuO4BWfdTt2b6PgGZvCXG2tkSmm0bdtm8PyADg0KyU6mpTRHpALZkmH2RuUNWEeGGC20GBWYU161FeNtcD7VjvR0X7sOOoOsQMiiuO4+3N6zWZePXBCmZRT9l6Iw0Cdg3bRr5BtNdR7tsKd7AyRgv3gIBGOfAAAIABJREFUvtz35nW7GWqC7V7dvjLfN5XkR2aQQpQC20rE8w0iDJJL5V53aEYYPf+dUZbSHYRa927dlLJiKSs++ZBIFuStKKLH7fPTk+sDBhySBzOaE0E9PT8xQ9oaa8pBtMDR7p1hFACulxU/XxbAFFkazCFhSQBtB9alsD5Z2JuziODYKiSxnrc4NFuPDWkRlEx4sgJYsgDOIMssfQHAdiVs1UHHs1XuX+7V6LNqgDXP6HHNVTcSY51F5nLo/QE3MyOJE4wlIK0pUinM+PkeS3mBOfRcXN+hJCQwA9+cOIoZx4JvPn70d526oZxzQfOWHbEOqOtGn+KwOnpdZsjSifSEFkrqOlUkUBxAFunQy3DExBmHwyDvsgtuyE+BI3R5MjsE1HsaNAUue8M5zCl3g787ABb6GzA4rDguh2GHDS91GsvkIESwcrbGxnjfHjJgNSM+NNk+MwInmDTj+xlCOtiZB/Pzu/eTuJ0zcn6PkxlwyJDD8S7gzymYnFHE67I31/iuowdmwqEL9RvPdf4Dw6qNd4kxr/7FTLIDgfcFDSfR+onvOW0SetlfxBxgiEcTxJq3vl5jLffr+nO85xaenh+skeb5GQK2EuvheZn6xMpwVKuXaKiNoEf6nvv8dPz9Hz/YWSRsBKRyx1jAb6NPEdHmQomm65d1Bfuoed3dBMt8I5Ts/I++d9/s4TmaZacNcTonJHCHVD0yGI6IVIfetLMRzsi7R2d1nO/9BPp35yiU126AMLQpKBUXPkVCCQ2JPL14xpLniQwG1HhGygff4nEdhy2ap0riPPHnYH3YAm0V6N8QTqh6oB4Hci6sl6kVWIdzk3NCWQpSOtChti5zoo5qRD6pQJMk79P1/7H3dk2S3LgW2AHIzOqeGe3u3euw//8f84Md4bB9P1YrabqrMknCDwcAmdWtXYf24epBqdDMdHVWJpNJAjj4OIhXsNQEDMXATC2NmlgKE7IK3vbqRDeGNoAxurfv4Dt8nAcVbOnQrbAvmNOkMzJBUNHHwHE83CCEN242GtyuYQlCDb0NnA/WBBVXjEHd75WmBFlEVwhWwFAeZSVnsLlfeidYydcGZ6eM9wNkGnc6R1bhb36v+EUYuqKAljxnVYDi75zgPq5B4wDFHOjYcm+Z68sMFmm48X6W8Yh7h1mrLNMYcQtmGjVzyRfh+6+1XNstCGALf8/qJS61su/TQQCmRXKOZs1yAG8FhgR9SF5QBdDCKLFUhcoOMxq+Y5yIb1zFWbAmq3uKnR3X68pIcBXjnBGZMIRmBIJ/dwP6fWUanMaTeP/LM2t0JsskjVfBVpn1YADe74fX+zKa/3a/I9hEVQWtN8gDCYpG6zCYE7dQVh0nZcDjceJwFlABI7ijG45xx3F0bJWMu/4Gvbep11Gm3TNrRMMpEYnhI/eATXDrRjvlUyz0iLgZ7o+717+w4XvvrBvleotasvlMvRDsRooyHTRY9lDUi1EmCOZ6GPkexHtQcn5KKZ6csNRZRTrriAgdn1drmUa6wbMduE5LrR5JZzSe7MYDipniHgJSRbAJUHSPFejzTkdIrQqAKek1opgOdLe9QivZRLUUj9jymYoIXl52bL0486eDEaGRrgrcHMyR+ddb2hgj0y+3F0QanJk5ejW0KF0Ih5HP9SpDRu/ekmc6WCGz3thsJAFL7PnsxZlOVsu9bkSovKYu9xUCuajbjAiHqExG85SPbvAHKLJFpiHVEKajiPs6gIECQOhjB2vPoCNRzuXw6y+fr6eozOuIWYJQAefbEOVA4M+h9xc5HvZAXEl9HfLnT2oDQ5cv8x9RqU8H+fFxLnbPZ2BrsRjz53muAPIxMnm9SayV6/x9Fr2UpzGtAPA5hVjEfv3RfgU45mfiWWVyffd5jlzHKvI8dn/0Jxs612UCxVh/U0pGFP+yHoC0M1MZu2yRfOqP74kZSZL3/rUjYS+FwOIoWNitY2pE5noMW8D/HfcZsa7/wT3/OP7njt+ehioCkZFeqGlYzcLb+GkuyAAFbC9AgEbFQYNpIckwS2/HuulXz4qtV10EgniPoyJL7YsbKAQxnmLn9QDmBm7cd02hEI/cHMeSCug1Dkm1f3rhvFHhbVtlqwUzEmO4wc90UaabdfTLvdbrEcSx7mbbC2TTZE683XYcD0M/W447ss5omHp0ripTAnUaPuyjZt4PbZlAeHrOMKA4A50wBUhLwQ/bjh9++IK3n+943L+jbAVSKtDo0e9mqAGO/PWUUtBbo0ddzRlI4Ska67uCRxIZPVEY9tsG6wN3O2kMOgjftorbq6J2wXEMyAl0BWoFtp1GgRxMMaxbwb7daOx0Rh+H9aypExk4nQnwtju5xhl1VIqqYA+7fcd5NPztp59gHagv7G3HaKvhbKevfQLJ25cNBva5e39nK4PX11dMAc+UyD6Ax+OOPjbUWpMIAm7MGcBaKoDPZd2VmAPTMQkP2BBdIKZ43Bt0u6XBXbUm4FSNVOBlSybojH3EetI1LaeoYt+8R56RmbGbZV2vxW5cjKFhhubvThC99hysRmorBJFCLADqtiGgArw3Z6b2gIov9oYWwWM0jAHsWl0/u3fcDMdx4HF2N+gE+7ZjNp+nMUWnQ8Ojnyh6w8ttQ0T9Rz+9qTqwpv21dtLwFsn0LDMSS4WnOkiiRvcyWRAgJvHDUk3L9E0DMBjdUUbuVgcU54H99AxA74c7i3aIcH0/Ht/dSK9AMdzvDwCGbdugIrjfH/i5/YJadwiAn3/5GQLB6+sLeidx0s8//TxlEYKBc4K9MQYBauVa6H2gn+40s4GB4inCA00GU1FFoNF2QYyEUSOis1dDMlKgujWf6wbzuisbyOyNMIDoLDOc52xYH73+Ih0Lw9wBM9NzzVIEZTQDcAIHY8qtGfc22w2BzrSsRzL0wyMkhR6NUhk9RR84zhO9OamVk6mYW+ujD09pdwZrr0eF0btf6w2jHzCQSKe3E9YNTbnGyErtQNydrVwgwrIBYYSQ+5IMxAqmq6kElzEg6B59jNi5IlgLDQNSCCZjDdZafd8QDJalHnF0d545k2vvHff7AwZgf7lh32/o7cRxkCTs/e2Os594HCeqp5vC1wOMzoC3xwM2Om63m+uiE+fZcDT2tfzmrUAAuJOpzXpC3zBFqBu/3HY6Zgb7YnJNl5QX8Yzz7/QoXH43fB1ntonzEwjW1G+XUWJTlrlMp5kkF6fYCkA+BUcuUodnvoQR79JxMdBDNs574vJMH68fcvtzUHZNNxwXwPJ0po1/eK/1swsQCkAb3AT+fxKG2bQpn274q3P2+bNOcBnn5HsRfPIurt99wm5Pv1trC5Hv3GSmD4dOyveyACJb3yNm1ooh1uZqO1/Hlo6S5Yxwvq2fJ4hcYHE+/zq3l1ezzO8nDoBwfoifM/xvRZBz8WusifY9YzGKp3m26ZzWX53rP47/6eO391lcDMxlWQKYcPEicCnREpwltfmzIIjzRa57dv08hPmTQBBMADLPVwe1SEMuF70LIlHDWh+wKsfo27hGGSOgIi6kM+0uBKZH8xDsYbmxwhCdjGvIMT/l4sdc5SYnO6X4mOgd8sgAZpQyPLOCmSbbeye4kAnI5700nO8pmCl0uMGrG2T39wfBnxZEJC2ef/kW0nm1TLGAIKXFmglh9kHQL++PFjPg3x3+fA57kR4yMZ+JGa0Icg34PVRoaLXWvWoxjBOvR5W4pteiusNBRPj8nmYlqpliJaKQYtCh6XUHzCOtJLt4f7/ns4XCiE1DNt7dwZQ/roTxFdFi/y4MQVTxQTl6LQxB0axtWAkQMqKwKKphVyOBADRSUuZ91Oco36W/1+LpYmNMwBRXC6A5ZKb1Ynn3s15o1vd1A3TMXnrI+q+1Zie11qLo/DlVYd2wbAS0s+M8vPG3VlStiBQbeC3a6a0m+vFwYicgab99bkWjv52nLIouexQ5T1Ery3lSMg8JkDwTA2lozjchPi/mdab+/YwSLcZkBhm3lDvRx5WOI95IfY4E899MK1xaLxgBlepU4sh1Am8TMwVD3EOwppABoub1x/4Mhc8kCp+P4bH3CfQjNTGYYkNmQdiahXXhXtu4qoEwLqeNwUyNRlZS8UyAAPeRBhg6xywkxfOe9GOYN3GPLAPXG+JvKt67AcMIfGQFHFfTK28yBrK9Th6pTywdCj6IXE+BY0uJFFYndqqACUnMqEujHY1lCl8fAxgjQUVrDZACcafG6aQ+fKcCKV7/DXiJQYqVucfckM86z2UvRg/f+aomy2H0p9y2qLUQlFEz1bM7eIJ5+xJh5HNoYfmHMVoSxFtxz7FENsdYgeIcA8w8TXIk8IfrrlWSZt9BLLpx2ROx/vg5Fj2G2JT+y89BzOqUWK/9KU5bjjVjiAb1MpRcT1dgllH7lJn+3CFHrkbbxXaZY7q+y8vnuf/Mf5Rl7q7f+WRHLNdd/jAko/s1svbx+/bJ7yLSe5nOfC9TF8aef7a9nqN5693+2Tv6/JC83z874tWE7A8Zd7GgP9H9n41tmrafpw1/9j7+acT0k/Of50vyj5lGO+0NXOY7xz5mWVBYZX8cv9/jt4PFWBCQVHZOH8Lfu+oUDSN3CnJzIJJpkcPItI9nQfF8zJD6s1DSyza4eqhEi6evOJgorOdZPeasH+EppVRs+47eThowA16TFEJTUOuMApZtQ60F53GkIIqm9ZnCFKlSnna77owYAw0HzRoQgNT4RQu9xRjoZ6eX22gUjU7WTi2zXYR4TRocUPfe0Z2gRZbopU8VnxMEVcVrhM7jgHUyOo4+8Pe//R2jAz/88IUKMVLqHLBGDVQCIrPsLTnTMh0oTZM6hoioXaSxYwlgCfRYq0lWOdYpAbNtAwCIeWY/bWCWGI7JDnnbKqyfXuMg7PMFsiwWSduZkdj1d1VQO8e+VXWGT8PojXWtRRk5FEE7HyhKRtlhw9ukkJnUBqkkSgAHFdxebxitXVLwWg+2QfNaqkWJBzOgzTq22IvniBpARnC7pxnGO6ViIXgbNvI+E2z43hBFkQLzHFDVAMnTSDKbYMbtMAzz/nZPSsEM2EQxxGstheyboRDT2LOBdmKyq6bi4bhWBTUGPCpMUE/RQlSmDvK6sD3M/WgQVbx+Ua/Vs0xDNxhJkkrB++MBRP2S0ZistbDup4fRuLQ0mG/F92/kL1u8YF+fMwqQaZhBfrDkC5kBspAxBfNnbz0BXjhoqnh0tw/fh4yq9dFnn9DC1MRhE/juui1jDAeTTWAWDoIyUyhXs0+1YvQz8Xgp6qnl3DEhmyFO5e4RVv5Jp9MlHXhxXgS4nXfjXGfKapmAJECxCNPl29mccMhToDOFeJIOEf8rSL8c4DjGHe/Q3EHh90LgEsn7Brhup6eQdjLadrNMkVRRnMPnVpDZDPH/MMD6AApBddTXCcRZVAEtG0QMj/NE841einp9qUKkYtiB6H8qi1zVouhgOnJ3gFTN0IQgHAJni3XDz5gOH3t9HPwemcEZDd1cHiWITkPV5r5w3d4HibLE5X9vrGFSjyKKCKoggWdrrMW2QoetqOD15SU3ho0OUcW+F2wBCGzWP4aOrZWR0OFs30UrSmGWRxtM0U7Wz3UHG/co98BIB0v8Mr4jZtmKKfqlBpgTiRRswfwz7IVxMerTYRUnfmKTmwOoMT0N/qnLrgtrCMeQ9Xtug3wAASGTA5wtQOkS9XOSnHVoM2I55ywQjZZrRtjy6HM/h56WJWAQ4NXX0XM0cgUYKx5frxdyI58vrznXyaUkyOh0ifsWnbpsGcwn4GY9xZ5P/3CuhONinQw8zalNaUc+hWfHazjlZ31sPOZaUrRCwKj8i3c8Ow78NlAYv5fl5WcpQ7bYuV47MwIzyjmdUnwm5wuI2y7b4bO98Mfx+zh+e59FzCUaL3otNrZQzpED/iQUI/qQHhSz9Hh9tqg/eptogKXXPQ1nc4a9KWBUCA7So7Re24FKPE0YEQH4tIinTnHB10rGtVorQdh5ohTW750urHprGMI0oyBN4PxQ6GsJw2EttvYZlRkhDfIOA431WiqOSJ31Z2dvNl1225wvtah5gM9V/iv/zmhpKBA3+oLYJ6KCqnACjTB845pec2WG0DDBFBjCeXq4JhCYHqX1mh6BWNYTDWWFalxT81opgEwQCTriJC2KxfhMo9Rro4QiNVIsMnIAIBrGQsSNaI8aWihYXikiaiIgCIn5XPnGL8p4qRcV+LzFu+ZcjsU7n5Eb/3t82A+zV5gNcwKWJY3Z/4MJwWKyaq6GDPdCAj+f86DnH2N4dMhyHNZ9Hw18OGwx3mLu4HMajcjnuuNzF6/LbefwOeDczfJcdwAEuyIiHZEpZtkDbEwacaGFDlFvMQIa2EBE9jsj5KIetVGI193yNfu8QGmw2dUxkc9rNv9fDDc6PeKdAJIgojA9bQBSLIHymgYVBn/UzfVGAproHepPCAN7NTbx+uB0yvn66Wv9G51bJG3iulEti8G9ZhvwfPXa3ogycy0+G4XTM7wagSLMBCgWUXsHbBJPF45EDeQ4Z89rYsOhGIa2KquD1XstrNkhGimo4ZAzZlLEPUKmxPsNZwC/7Ht/dYD5JlXM9jRXuceviipGO7N2PGqyIeFcJBjcs4aHYx+94WwdLzG2LPR0GRjnGjDawP3o2KqhbrcE4IBgqzseB2tVtXhv4NGhnv478v0FIRsWeefstCIJNkO+0iHX0SzS1zV1F4cm+d5mrX/sQyQ5UdQMzz01U+cDfIdjLOZ4dK+v9Av21ghUCvJdkgF2EqXAZVMtheB4NERqaFHFfRzTua3CCP2T/ZDXsZijacybuF3h63Z1iufzIFTEzA6I5/4VWzyW0Sfg1eYv59VzGYqPZ+JZl+Gx3//R/VIXLiMwINmxBdOYuqx9PssSQ5xX0GvZwrzPfJYVjJqBfBeYcm/KwV8b93L59YcnFPlsO34Aqc/X9EeJfz9HXZ+Pz6J1eb0F9fwaAMuFkt+Z/5pa5EnP4Hn+Pxvkr0yc4cN7/Py065g+rsmwfSSdHSEzYmdcR+P2rY3Le419kT/Fmv24rP44fmfHbwaLVQounNO+kIIAbKZDuhfMxMEjTZPzODw90uvrgA8bZd3o/Btzd/u5YciGGcMNNYkVgDnEiFKoDifhmMpi23e0o6W3pB1skv2ys4eXakGpBS+3G+6Pu3uEAQjro8JLOsbA2Wio3IQsrHEwWqTYtlfWuvjgDEjD2gx43B9obeDL11fc9oLomVeK4iY7Hnd6ZvZtQ6lM17OTBjnPK24AFNRaUCsgDuqytcICYvZafOwAwGfYbxX/2//6g5PCGL786U80NDFwnHe0di7P1TEG0/O2omjWcfQH9n1nrSIMrX+HLamxEvJBo04mCCk85pfELVPwHkfH6G8wsI4P4myPXXB/DGAA1cGdKNk7WTc68Pb2jtYPdNsgUtBBmnqBojWCom6zdrNWoA2yMpKiejLsrRGGMQYejxOiim3bWdvnzgpxoMGIz1IzNXwNh2E9IkqGJU0vUl5diU58DjNPLbYldcPXszpRTHT7LIXROtJbuIe6AFudZBWx7sRoONYiaIORa0bCOo7D0khibULJseX+FzjIpjUQKZTN2WGLzIiVqOHldsOXL19gZvjll59RtJBRstvcvz4/sy2OYSusbfz29QYRwfv9wPFo3j6kAahsTj5O3Pbqdn4HzPtCWoAK4DgajrMnb8YlJTAMa0NGDgPsp8FiIX+mkwOiMFEa7U7mtMqAtVF1sItuRadBP2+PL6+vaeSHR5wXmqk+IpitPPz/iFBhkZ0hD3vzWmrQSCxVnGWX0eh29hyzGbJZu6qiHWRELYXAi0OiUZ6GvwSZ1JT54ZSItgP80CNUyaaNjFKO7gDBZU6kNBLAOwlU6p/F2ADA5upsjQJ1MGB0FqgyQyOxm00Qy7UcEexp3I5B9m4tcZ54fWiZ85w6rtBxGGybQ1Ku0CfDKFUfHb111G1LnVgQPShJLtTHwNEMjzbSWcL31oBC+djOhu/3O0qt+MufvrJUwVPz394fuD9IULb/ZYcqm9BzfoH724EOw+vrDXVTtHEC7YRIRR8N39/eICL493/7C9fNeTrZ0IH3+wOlVvzw7Qtr8DsZTVEk20ppiTwMHsGEqaporWPbKk1fN+QjMmfDcD8f6GNgd4drOzvMWuDkxQkKWO/O+Mz9W8z7trjD97bvlCPgfgfYkoqOyFkrTlAdO3lx4oSzw2Xd6OZAx6N7RoDsfQ+WNXQFSetnl0herr2PRzptIsPCZeoaubpc15GPxOaN42k8IccB8UShkfID/s5k3mJxzixWWoJh+/B7jKUlhx/DLHkkLCij12svQ56OxfhwmcMn0LcC0s/mk2J6OiWipjwc8/m86fR7qq1cjtWZfZ1eB3u2zi2W9TOduDFNYYddnzfs10VtpIdgRtFjrUZ2APLaK1BGzhuW3/jTY9Em/lyWYFDCUeLfKlryedIIie8ugNRgM1Ucbm+CmtOtH97H38EaSOC6WEf6x/F7Ov4lghvkfrfYbek5CS9OGTo3C5B1A6reAHkYCSuyVunXPXE0aP00rD6NBWCm3SAIMhEK27Fcx7376RGjQdq9ViO81KpePyNsnT2cvECLpnH0bAhCZNbBCdIgTxpuY60IxtLA28FC1AOKeiTA6bG1FK9PImvkbR/oMlC1ImpcAgSqN9iOMWx1wxYEOfI+J8/Hp6LY9xeM7u02WoM5e96+VQdGA7c9CHMMAyeGNdSyY7vtOB7diRWiBqoDA/j27RuYAdwB/DcAy/Q61qC4UFFj9NZfcnif6PUO4E+4k3353NMcyw4mMGGqKDQieX49mYbgLJ4H6wtF0EbzNeNAk+YpWut4e39nCq/JJExqnP8wcvrj4HPsCpWGWshKmlGn8ALLFJeAJAjNtQM426ob1a4ALAiaQoAvf0sCD1myIyUNakikQZfYqW5MFNSwbVz2F3hksRbgZGuU4eOH6pJC6CQ5ydRLeaBOxY94VEyDo3uqedWIuBtqqUGi6JHeSEWekYUECTLT+awrRmtOauKZeULVMwmfBkQGtm3jNaw7AUdhbVZEPCwiI4LROoYWrktVyFoo60q0KBl2+QbZE5Cb2DLlMUil1OtbzSJqzZ+LFDICi3k6u2LzvnMhydZWDnHUTBkONmXeQ0UwqhNwhaHqGRC8hhsAShA56obWWz5T1R1tNNTCd2ijJ+kEsESKRHHq4WCR8u84DgAEdsWbwIdwYfSn0yCXqG+dZEHTmRf1b/AM64o+DGI9HSytNzeYR1gUBAZFGX2FoBQDRHGeLb8nucZLOusILqeBqYsuW6OncJAQxaJj+GK2KbdX9mwzkvL0xSkWc5ztoSz0m2QKuIrQyHTng4Ep/Ga+Z4fvgz5Yd4rIwAjjVxH9X6Uqxmgw78UkSjKyMPKiZQ6BdIViAbswSCkYpzu03Em5rkuDd2ZM3WVMMV/2umGWhWSkxj6CpJi/mQIf5gPT5PvoTvwlef1uTL++bTUd0vE+ztaAILDzNdPbwO32mmu5j35xlnLMYbAPGCbQjP0URzoysu2KiwddfgghPCbLcayNqMFfoU5mOy3nrkcCurwGZXvKfTNfpoILPAsAEfN9AV/IZ8yU2MXRFNe19VrXQeX7fD43sj8Mect5jAEsLcx8ghNoYXkvMLuAmc8AxAewnbrM5+yCXz8797M44T8HKp8CyQWwrff/zCGQzgPAs8fCnl6uuwAwPF3jMn0yT7+MzcLKiAutY47o9/N7XdYrpp0hT2vjCoTXGePP4cQWuPNlGCBjOjhk+c4KOkXx/2f+/zj+Z47fDBb74qmRou4tDEYyZEpOj1QsT7BiUEWAISReBHIdDxuMPrhxH4ZPGFsQcZap6V0LAR4RvNYaFAbNBe2efw1PeEd4d4OVDIBH78iqBu+PVmsl01thTdIwBwEwbPsO652kKS64tRD0YCsZtRQbJAJxAaVFUaWgavQf7J5eZmgne4OpKm4vLziONxwPpBERhryIYt83dBtoR4cq8LorrCoNmt7xeLzBTPC437HvVPp1o4H30893nxvO799/+jvEU9mKKs73geN8AHinQlHBVukF3qqmQCz6cEZIQzfgOIA+gHYMnGZ49BNoZBmMWjprLi3E0IzGpBZ6mgOgPx6c/82ZBqkQFe8HPcujc+GUUrJ1wOjsxVWLJgtAtBfh/I3FgCQ7oDEYCbNZWwkjs+sw1sWdjcqUETAyzUYj7Fi4YTB+fzvx83cD8E5gUSq26hEJB12m8CgUx9Kj9sRT8kQNZepg7rXRUUXRhD3kejeUukHBaBCAgC1gaqW54W4TSEMgxeM0w9lrzQGMRHSbz3KMjm7RI614ndNaQwJ+Z8zogQAYo0DKVB9i4a0fHvkEsna5dzR58DsC9lYTdQNyRiHS0BaFmO9bQfY7pUEboFHI9qmKYYeTecD3ckHZKsqm6B2oqigyGIF0uVB3Zg+UuqEO9vAczjZZPLU2UhmLK7burS3KVhFWMZ1avubhzcIRRr3XlXaugH3bmLVQFEWZKQAzoDeoGPbtK8IDpoV1mGc7uV98D7Cet3i2AtdyLTuN7tbcIedybmc3rK28co137tlqlemyQ4CyAVvHtlf0NlAe7izRin339ew1K2TbVDQAipL9BLkgHCy7ddAGa4b7IGOwCuuQewfE1J0/bFUTmRTcW+qsmSQrO84T1gwvLzfs24b7+3dAxev8WNsmEs4o1wvjZLQygVzUEC/WltIh1Hpze5iybXQClBJ4AJYG0f3RUEtFKRsdDk6kEs8yFGDmBds4MIJGo7KPjjLYgB5w0hixXLNaABRDVwJQiGKrEdFn1D8dNCbUdSgoZcfRu+u+7vv2hfLQDVMpija6M/vy/2jD0xF9MDlXw8lymLGi0JPrbcAzQIRZGlYKztYgw1tkiRuACUZm6rOZZY9ZUYEaU0jrLjiOhre3ByCF71Lp0DvPgT4EpWyZaBCg3QDcPAop7uT75yH7AAAgAElEQVR9dLJ+d9Dw6INMtCKCInWCqKj7Fcn+iwHyz9Gx+Tz0GLsvGVWBiYPp4b1xow/mWieXC8ztmahZnirkYqjPnozGPT9stjFyW8o9q5gCGVD35gVojNTaCTrcnDebLR5s/m6NiAFC1rEnnHFJ8UwHuJcqQObY7VpnHo5NW+ZhvaZAcjhrC5RLxFWu0bk1onjhL4jzYk59L8OiPlufnpU4Nvddkoqt2Wkkagrn3AcgHO9hBXn5sUcjVUgqhSAakw/j8Bm5ADDI5N3lWCgvYUC2eMpvLpFP/z/6SxumY6W7F4d+A9exxnFlr1zh+s55ziih+DMwKXuymy9PELZCvgM/D86Y6v1vj9Ny/MxWEQAdfxy/v+NfY0OFL7dBf+OwuVcoKCzBXPg4YiWTiIALKBZUbIbFOTLv8eQHSa/GIjAAJLtkKKHZZ4be1IuQWoUqwjl03bhR2wO/rqi613D2FFMPuxdveBz3NGd7XZ8nFNEkJpD0wMRchKCMhuXhIZxz4kBZC6rZZGEsiiEesRUCmz6ExCHVAbWRlGGdr2g4LiIY7vnrPSKW9GTFz+cpCLbRLoazOWEPFO/j7s/HqN6PP/5EwUI3FaMybjiuaSaRmpDPuQjRVdkMY3uBUrx+RqZgLA4sWYejiHo0LWzEDQdrcEFvznwoYjN1dbmnxVw7ULE20/pUwtsd4JPnq3sJcx3mj+aG07qKr0kjdIY8KWtfm7EeVhnKNCi40uf54aHnT5KKT+DMig6yxXw7+HvgGjSEx284aJ7EJWHgzLeVhlr3e8WdBxAZBuueVh++5UYDgsk1HL2xt8yzDdKzGQy8sW7CUPJXEP4EM7L2qoKRM1OO24x9OZ3Uw0ZH04pgUBTfP+0kEKfTq6eTZjX0YvYv70omQI5NGoYggUOMk8DI0Ekk5ZE+G4bHMVBLQ902FBU6SmSgg06k0Z0pVoF937jWwlAvxZepog8+a28HIGzKLipkijVgSERsB4YopPBeNpgNIACkKkznHpJ91hoOj/aUqugDGN6I2RwMidEkIFGUG1sOjksVOoe0xgqFoaFIgRidhB0jQQUJrdzQWfrSFhWMqp5iP8FrqRWigjIkNm8ay9orhngaq3gtKkZGGmVuuTQQMwuh0pllNmVf1H32Po3c1VDr7HXgWbCUibKsoee9moZZ7C8uHgLIYaljLumIsHScUZ/m1kskoqpONLN6/WPdMtNlWAFQEc61aIk1bLBGdEy5vB4X2ey6LjKHJlAwzM3AczOFNOS9O97SyA/5ZpbbKvZ3oyBDLylZEaRk8O/11jybgDKieUuUAJWxNNSvSSAzbfyL3sGsp1uGw/VhPt/8w3WzXKcpDKInFDGBQi4Dpirn+nq6xMX4wZQxgPM9hAy2y/c+Hrb8LZ+ecbnPP/t9Ljb9+PtYMxfRaPkCVhtglaAfQCKWtYapQ9N+W37/yeDyn5ZjejpTnqOQ8xqxhm2ZtjV18uMcz0/W9WSfrYkntfHhOhbKWnKeY/i/dt9URU+LyJ7m4vkKn8VYsczJtBPiq3J5l8/X+7Bcl0/Tf7HIIi7pX52MP47fwfHbwSJmsW14Xy8NxMF1FFTeY1yXI3sdGszYYoJ50rJs3AjpLwtV6AGKovxMQwk2VaHBEMJdwyL2g8owH2D5fC7naLrN8bN/1nBvYfFUsQAq8d1oQFzr5tFBNzgsUuQWi9bv33rzNgtOyKJMn4Snx8LgbJgljfXh0bGIPpbK2qHWD35PWJum4HxAR6bgyZAEvq2Z06Mbhka6Iw2g8KzONDqCrG4NMKYTFjdA4ameEXE5nEhg2yrqtuGn//rRo4UCerwJsnqf7UiweBsN9KCVUjyNky1AwqMYRt2+bW44eeqtENSPMXCeB3QrKMrI3n7bUnKZU8+31nEeJ1n2inr/MmW0MZZHAoECA/D9fkAL6/novaanf3VWyBAUnfU0/eAzmGkaASlEL0rpWXDzz0jrldAZELDucKY/R0Px9uQ0CcNUddZjDU8BZPpyViCmsUqdNKNQkRYivjbCnb+mxIrNlBOU4kQSNB5UmVrWzRBptTE2eBQ7DRzxTARXxvFuzQx7sBz2SLVEdLeYz+bv9vv37w4yBJCdntxhaMIaMYDkFyInrAkjWiDouj/uUGU2RNLsD5C5TaYRE9GhMNpFZHr+bVk7gw6b6SChEd5NUW8bVJkyCXR8v7975G7H7bZh+/oFEMXZG97udxyPB+fiVvHy8u9Q7ewLCG/zIwDAqFhrJx7HA1UVtz9/pSfcvcun97V8tAdEC8q2Y9s29JNgsahAt41Ro07m6H2jPOrWgeNkD9O9onegmcI7D3EIY0BtMI0XYE21FoxzoOwVx3GgmHrYjJ52rg1GtgDDVqpHgR5c8VLcs+4RNSGjbimatZHqAFZFMYLsx9NWVQXQgjEiBU7d6Is6RLavMKOjYNu8trObR30rzvPEcQ7UpU1S3cgaHBkhqa+GYYzmZFkl9eTq4Mz1Mlc0Iw+rHDDKLPYVpR49R6fDTAsMzGwBLFmEDdQTI3QEPDNAGTGI2tYwupmpQHmi3hYm3Do2Oqs7HSVd9r3QEWAI0DT3q8F1tOtKs3h34Yyb+pWtNQw2AK3BCo3pwMp7+7O1gVMMbfO+jzKdq2MM9GFo7cza81I1sxQm4LMJGnK8874zNZQOjeG627BGmuCMuYuRHjJT5s8W4490w8v75TpQXxuUeYoEnGljAGbLRXHVGAGCPhjbzwB/KjcXxZ/UGn52g+W4ALkECvGFDygsidAmfgmAfNVXtoDsiPyt94xo1vxwgkaIQJ+e/xlwr/cKOZ3/Bt8b/zk+TMU0IJ5B1Sc38fvo6n0AljX1dOWPN1tsj/mMsnx3xhh9UJ9d88NY53Msvhp+X5C1lh/Glen67sRze188SrqO8zK/ecdpJ2emkOebR+ubOD4jkfvj+P0cvxksBqBaX21FKJHVaxT/+bIRFoi3dmIY6xIACnmYs7lFEZJfg60jyPamOlMdCEAdOIritA45W0Y4tzqVEeMrPvbCa5YajXmZ7iF94PbyshQRM/00vKzWLY1JWc7JwwluVCtKKWgNMOvebFsx3MIVZRocxFtsDJLkaCmsVRkD7Thc0QXxTChgTaOsHSce93fUTfH6ZfPaFiBS0frRUbTg5bZDdWC8s96PgJPGO/vBhWKfRAvNjfUg4Ailv3laJwGrJBg0ozGvVbA5QCt1zygfh0wDe9igcVkU1YUDyRccrDog3sJwrgXHeUIgNAhp6yVwA4BaN5+rgVIqSQ96BwbT88zrq0opGI3phez7ZajFIxi6CNIomDASrtTK9RcOgwGD9SDj4fdqKShKpTnGQAsmMN8lw2sPIWD60nLEOhrhADEwpdibfIzecZz036vXH7Khe14g75s/qqCiuJMlotzugNCKoMY3VxihCG47wThBWThsZo/Cgan8gsIiHEPDjJEMAKPRmN3dCCRjJKO4t+2Wjg9OCZ8x5Adp+rmmojY40pRbO9N4hivmbSPhyA/fvqJ63V2A1NVmCtDH+jkBjJ0AqeA5me/v7zhHgziTrKjSueL/dRsoXs+pIP16a22mntlMQ+tunKszs44xcL8/uO687up+f4eBbKdvP3+H/sIX+MMPX/G//x//p0fQmI7248/fAeOevt12zlszvD9O/L//+Z++l8LobviP//wPfP3yFX/9619hJvi//u//hnjLHQOdNbfbjh++fcEP315xPxv+6//5D69hfkBLwe224fZyw9fXF5gajnaA6frAbdtRteLeThQR1EJgb0rHlQUoeylo5zvKyw4tO5jebABeMVrD6MgoWG8DpRYcx07njxbUuiXRUe9MQWbmgOLrt68QURLJGAkialEUBaz3LIVQB6jTEQYcx8PZbukY/PbyNZlMo5XC435ArOPr6wuin2ofQK03tPbGVF8Ao4dzUrAX30PKaKxFz13E+q7JVHo/Hilw2DqpwFpDHwNVFa8eSW5ny1Rma4LzZC0p+kA7Gvpto+PMnZ+jN7ST7Z/6vqOoklAHguP7LxjtwLZtjGL7Wm3o7lRjj9kAfer7NuRq72zToi+b1xU6gBKS/pyNdYfcP+KpvA2Aob0LWjvR+8BWK1QUDR3H0aCd7aWKh+Nba+jGSK2ANfvDgF/us5RiK1u+q+61jjACfTNhVNg3v3hNOOgy8jS74XWv09A1Yz05maSXmroAWDJr8+AOsxldhLvYZgZP/v1kSCtmenrIuciqMSDJvsLegl3++vyYXrRcj/mCnr58zY74CF4C7KahJwtQsevfa6YWk3kmyBKVi7z/7AHmHA0Ee/aMNq/OBrmMU8KwXK5BB96VUyIzdD655zx3JKidUMdgS1baYs1e1svluqtuBjJLbAhTMZNDYwW9y3fTASCJ19IOA2QS3i0j5FfG5XN+FmN9nvRnsA8Maz6e2a4pghPZNUDVZeTzPF2PdLTkXTywNBYHSJbPrLjhjzTU3+PxLxDceIoi6JFMz9iyqfNcrHLFoAXYbjtrbU42Fy5B/46lgDc3vueYD6bNCKKv07h6aD0lbxqGTvDQe9bj2dLfpbc2hQsIgMNwjDS4SJGNHHNRhXj9BrDUJmCm/QEh1PzBJSJ3Mze/KHuIDRsJZuL3pShGKct1phIabgBLqaz/sQG0ATManhFtGBg4j4aiwNhiYw+EVzE8w5FCGXOoXreT5DEROemWRBLiAu5SlyEG9KXZuxv3kBkZHGZeYwMydo4JfC5eKZle+PTK+jwCNEJE3JAXuLEXtTkUxJv3+GKaE9OP0tsNTKHlc7wSIKUXzJHRyJYgkgKzD2NqH1x4+3htMM2Oe0NyzfAci9Pjj9wV8VdGPkKJ+BoKQ1Qkqn+R953jjt5+0+vn07n0evM/HUFdPZRM/QtmR8gEn+kpFWEPy8U9yWl3EhqZfcwY+S6A1xmH4QOzzFblG4BH/ZCGAe8XBuMJmF+7RKZC9xRCGgW9DzfgQv54Kq3XCzOCN2WV2awdWvds1DxFDS8E0KYQNI+icl4i3f0SNRL3gFs4jSQB7lSElpFAMxKu7PsOgOzStXpqjv/+2w/fXI4Be91xHAdm3ayzdAp7LG777jJIse0bAMPb9+9s4H7Q6bBtgr1WjOGEOlrxfr/jPB943Flzdp4NtVZs2w1Qwf1xJnDZ95pOOxHF/TD88v4L7ueB15cX/PD1FdFb9+df3lnrt214ve1og1kD339+Rx+dUdSX6mu2oPWBx/sdGILXr+yzx7kBWmPUsZSQC54zq+rM1SceZyMxitd71lrx9csXbFtFbw2P+x2nE+UwOlmw33YUJ4DpfeCX7wfO40wdU2tBLRvrc8WXJGgs3e8PbNvGzAsnR4uj1smyGfs6GCf3raJWr/WVmbqZ61OV5qgIbi837C8kdApZTUdHg0Dx+vriBFpc56MP9lgcxjpPLbNOywzn4b2DIdjqhlII6Fs7E1+EXg/yrd4a9330jVU67UQUow+cbqpSjxQni2su+yqqCLQAKjXlaPRspPMFvvdmZoI4SBtmsDZcVwN0z0hMKwBDtw6DTGcc/EXJ/Oya2jgzluIonhWRpHf+7kL3ZpLn4nhKWYops3G5x2L5yJQRFwAZoj9kvwDp1hawENE+ApLPjPNMdfzsl3HO4rxcPg2ePH+MBQivh/yDC+etl3o//8RkPu8asb2OYHm2nKdrOupnUdB83+vzBfBdPl8B5tqzMMHn8oxTtS3RNom6Plw+m8/9ZMP82vws1yDo/Ji+G7NDZ3Cs56lvaT5cnQfxzvL8BQhOcLuuoznjE4QuM7mu8WXdpl0bY5e40sdnHjbbpoUtBFu4SS5jg2eU/QEUf6/Hb2+dUbf0pm9bzaLyiO7QUy/0CIp4hMIA9XQXZZQjaooi2gSAJAuI5W5poMON0VoLto33P44TUSPHI1I4PP0N5m0NNk+3arkgLfYiaExmuqd7TvkbelPhRlhEDzMdzY1ptmtgOlNrHRC2BthvXzBax/3+cI9t1Buqp9UJ6kaDKa611YriRf3Da5BgBqii7pUe4ltFRlcHMBk0fS6kQHVD6x3n2bxNRDjPeJ8gyzlbm89hiiZzvsXZ4aJp7GRujb5es05QlQZX7x3bvjlA9GeskW5b3KCXBNPxnpn1OoVnCjMHGADfwW3f3BtbMAbHToPLxw1G+Uh4wmfsFsY0OSxpIDVoEWxOXw+R7KklboCZGAL+rIpLhb3k6HXm0VtH7ydeXpi6ap6K3BrJLFRCDVjK+nzWVDTzMxFLD20me/p6ndHAEOZIozSMjvydClRG3n/0Aest07mnSTNBb4B2rRV73dK4O9rJveHj/eGHr4iWDz/99AtrI93oen25oVbS+7cxMHqYICQMkRIZBJMZsW6bX5/j0aKwky1tUODvbiDYi7XUTNXNTR0OgW5s5g7D9+9vaM0QLKixwLat4N//8g0GpieHMqbxHdFwEKCHQWe+hsI4NKBqnfpcNSO5RWevzpAbwcKbKcaiqFuBlpJjUE/zuRVJ2bNvBYqKePUWLB9FYTLIUKwVdfNeikVJ0mWCn79/hw3Dt29bptwZFAOKdpwoynsfZ4cJiaOO0QmQSgFU0RFgBZBSARP814//jfNxoJSK3t6xbzfstw0//fwL/vZfP2JA8PJyw/ttw1//+hW//PzA3/72E/rouG07bq87vn59gVnH29sdP/74dxQt+Ev/E15fb+ij4/3tHT/+/TtKEXz9+gXfvn1N2aVacNzf8f39Hfejcay+j97vd4wh+PbtG97f3vH9l3d3KHi0zwZebjv+8m//BhHDf/7Hf+M42yxDGANb3fBvf/kzbi87rD9QywaI4sfvP+OXX77jT3/5axpMBnHA01GXcohI2wrDUJzkKA1MmezZ+7Y7Uy3lz1YLBIqW8kKXaypupfo7Gcn0SdkgeL3tCZoEYFTQaylfXnbcXm/Ubb7Xot+qYGBXgumZJmoORoQtkTYSSdXYz66HpDwlvxlLF9T1XLQAAYDH/eC3R3PdwHcj5qRX4UwyeIRi1vEP1FTgo5PwKhrHh2PLIRxLU/y/KL2OeQrnz4Cn7o4ZxSKxjyYYn6DC3+uShqqL0T8x4DWtbv13pOOShyxkS1nO9cnzUoDVmble6xLB+kdYzp87aiyng33aBCHbDGvN5vN1ZiAgh/gM3uLZcljhLI57IO/xa6mZtBmXetH1OQOcPAH++PclgviEY0IGT2DOX6rrfi6vSD3GrNU3eJ0wJir/4IS4PkPcbx3bDBhIrr1QwPL03XByA4aPQcF1Pdhlrq+fG224y9qI97cEOnyagtE7nkslCH2WNWGevRLPfQG/KyD9CKC53439hhe5on5u6Ns/jt/f8S+1zoioE6IoWzzVAGR/pGE+DU8zI+OegB5OePqPJtE2gCnszdm4JIWbG9rAk5dj9dhFykgI8mn8Rj3B9BxGTyVv1J2GhmTaKzCfa0bfNNNR48hoGgCCVxq6+7ajyQl53OPMOYe4XjMELVxxkrfQ4HSBCAMiUqF4nuYlp+cM3qi6JJlNCuAELMthRuHj70ngjGQOEELRRHpbPG8IXEY7413B31EIwY9iTHw+ETEyQ943cY5/TVU9GjS/G6mgq9cqBA9f60x3oTDy2jG/zxx7rEuk4ObMWtZhppMDPYVo1AL1tcGQr1ezcvFUmk9B78NJQ9w7HWslraL5fv1yqSgARTc+bQC84T+nNzme19a9skxkrBHfP/PdL9fwn6WUXCck8ph1OvlAvgZKKWTK1VC+c4Wrt3oBgPForijjmT8xfkQy0hyESmYRHfR5Uc10cKaGrs8ce1dYayVL+bzNtCQan+4wsQAGK/HGk1KXxdC3+VbW5R01vPk8KZPmuQnk/QPJz5F78Nmnn+fAMuqy1r/yVdPRwkgmEC0ycuuaoY2B3jr2pgj+l+EEWDF/YYjzHY5cHCHvUlaJAlD0Mch+KQIpvE4f7A/4OI/AIGh9QM8TA8DjPB3U8Nzz7KwzOxuOx5EOhePsuL0Ax3Hi8fC2GWbeby+iwS7TETXdbP8SBl98zn0h0zmowprjwWg3I7XA2U46w1wPDPN+iNYhsk+D3bxFxrimuc21HCuE/8d+T5POqeRZo94T+IkzsYZe4bozCKYMn2lmfLnD183VYJ0yF8t6nRpx/j9SX2IayEadG3McxnrU6YaRHQ4q5Pd8jdrkKpAw6jHLEKZPLy7Aa/URsmlGWmMWs6XJqhz81hJCzOZ7jwvEfph7GinHQ++AryHunHN/vdOc2zSE13F8ODONgWWsa50qB5J2j616y+YjrAL1evXny1+F0odfxi0ln0EcIK3X/xCFleWE9aX82hhwOWV9VJ8SzvfH2Yp1hQsI4RBiDT3dWFar4vl2Uy/n9ZebXqK8l1MjU8aeBjmjop89rT1de/F2zLOWdZP64XncCchmFs56ztWyiH/9yiKJ0crzuOe/0zmckxBrdGb4icjirLjqpzmGz+//4X5AMqxSP8wsgd4Hnhum/HH8fo7fTnDjlkr0GJo1UST5yN5eEoyU/F7vTHN6e3tDNJB/eblRWXgqWvSfil5gwwv8VRgVCUNRBB458h6FwhqH6dFFLnyA9ZBhzLC+LDx5ljWIpRRYpJ+p9+aqmukpo3W8vLxgc89x33am/3j0bgzxfm6MVh7lQO8tyX8W7JXbKyJj0bTaPNWnN0ZqLXidFZl6GYZp3QqGt6MwYxP5PoAdzjwa6bMuWGPOw2AjEA+9SeO+gGlJETEKJVu8JUj3vmui1VsxuEBxhRz57VHLGMbA8wISQTL9TQ8tgbqJM8MK03JZM8B0r/u7eRqrU99L1Ey6ZxkcR++sG3zWbtG3jGamk4zITEcxGEphn7rRB5p73yNSpcIWJps/WzbMHgW91ayRbVidCZOcyWBoo08wAkwCmyxQEDcCh3uf4WmKBbfbDpjh+/2xpP3aNMYiOitTAdCQc7JSN5ZSly23HMOwldknrh1M3zybk3iEsSoCGSSRyt6ZtHgceLCmdK/spdhPNgsPGZHpUDbXMoaRgt+4Vw0O4FTZCseVFh0mNK6710WKROqop5HqQHXjWgWoO734YxD4nR2eDk9mzt7ZbD483qVWdGNUk9kQdGykg8jHk8arzLTWFcnx0Tx1XJiK2D0dUWymFLNmyRZD1vx75r1TazpEdItK0ZFrX0Tw5etLvqNtq6i1ojvhlg1FB9vmiFvMnncBxQnY7vu9Y6BhWMFWi2cusBWRgsCkeBptaw0iA/utooNkRsMaHsdA6yf22+6GIcd4nKdno7hTztOzzQYejwfONrDtN4RlOYbhcTzQrePLly+easy+iiIl215M9miFGPeseO8/9Zq4onRqHO6tr7ViKwXqc2ye1hzOKRgA722rajCda8xMIKa4bS8utyaAH52tjEZfjB5xAjjAsyeGz+kiFF1XHo+H60xNvaCFGRuqdYKg0KvO2itp4DJrgeuADK6rKfkh4uHz0Q1ABBkXY9zgHE8ApHukww3ZMRbGWER6rLe/iX0rUetEhwDgTLEgq6+FQDJDbyeC8C15CeC19VEr77pw9kgOnbqazJbjNAhUXF+k4y/6zs6HpOyYoI3P5xORr8kypXKu3xAHi0HuOnUl8on7jgtb7PydgVwBMcY03i1AlOSzxZjWdxXPm7LcMOV/yir/2eXUBGWL8zQ+j3lALLmY4Q8YyPfECignUMo/RdKBsPziCiLnbM5MJ4k+3XO86TC3mJfpKI5jLA6U1UEHTGe/lnIpRYr0zNnrO5z5Ara/GemcWscawGolIbJPIsG/FoWcn30EmD4KXB7gw+/552pTyuVzu5yZVw1SH1vA6bIG4o5R053OJt8DQVAz3c/L+vEjegvL8v5U2HYJmGVcBrYFuzpT/jh+T8e/FlmMDeD/KX+RG3/19udG9GNG6sSFrm/s+J6Q8U2UxkHJ2osBrRXRXy7zur0XUlkWakYabTZQj2gY3LiAF5dvG/Olo2fM6N29MhGN9FRHDK//YnrLLorj/nCiHr+20Th4PA4vWg8FMaOAMRvRQmOMwSblGptFcT4eaQirCqpUJ0CIlM/OTRc9cxbFw2s7MUi8o8GebwEcL+8xe+0E7bsLwbhaGsXzZ1Vkio6qotk5a+bMjdkUICPnPgwbGwEuBU4Dm4qgpNHQ0cds5dH7wHGcaSCNUqZSxvTG1W1DH8PTYC0NMGAaK2wA742aF9me0VQA59nQR6cTwAbkrGlcRN5KEbIyAoAUxf1xJhAS3xOhJtW9iR0CaLZ1z0ijAUuWi0dF/P0JqMB3B+0YXruGiNF+olBk3pNrJAR0amm/F3+MHnARWSy1EigNTSWRRooGk2z1bDed9VfibKdFgCBpcCPcAjn7s7Lh+KyFehxkLs1+fm0qkfNs2LcKG0Ddd9hxwsYDQdwzjT+OJ+qCDQOM0ERfKN83xtToWi0dDKVWFCm4H41tNIaDPTe6BNN5lPYh5EJewbTaqHWN+ll/QxKqnEQprZ348uUVrXVvYzMjZsd54qXcmMkwGI3beFEHgQMK1lttWtHcGff69QYtBbWSsorp3STC6X1AnHBHq6J0QIqhjwNjRFq/QJVJhlGrWURh/YSVCqgbsRLRzoJo9SASadpcjyEfuOcKRJqn3SN/P4myouUB9z+JMaKmm+zIwzrJUYoA1rNeWcVQpipZ5BudD8MiSo0knNESL8YAZ+xmhoD5c0W9vDnZl7dMEgVkRr/JE8ayC1tAYDor3Q4TdUCbzsGYn2CrLtxz8Fpc6QiCUlEsjOFME11TXUfrgCJTYKe9Go6IMGTJGwBbyKpcTsXIh9m1ybtLoIxCB+PsqusXvTKM/WJjbJFyzZpHdTCigDM0cw5Df865G54ZE7CM5DXAVmbKZjxmsJZGfWbuZ8yIXYBQL7z2nSt0CC3ZQuk2NK7/VS7H7yMSGPPjg77+/HwEcEFEuieQlFCvoZMxZcYEqf8AMPi11whyTqjN8a3gycZ67mqr+VP4PkiZ/xuBtf0AACAASURBVHzP9d/2fIrkHokbMsL3BBg/PIsscnV+xtT/qZsiGhnPHOP/cDyBxjVjZDnJp8+d2BLjV//+k2C5fC/2RQxWLmDx+V52mczlmgHqcV0X8fy/Ml15Br83MF/vvFY6CC+ycdqGczjmuiX0ODwddmD6TmYW05QZz/M+AwRrtPSjhbKs0nz+f/ykfxz/M8dvBotRM2Q2IB5Js2DqhMx1L6wPuT8eSw0ZzyHDqJPVwDJaabRaATjD4rLYxuhAD0OfBlwfHa09oFq8Po+e5KhjU/cY161ilx3yTkN2DEMRKtce9Rxg/d12u8GG4fXlBSJAG84CNxTvb+/oveP2+oVNoT2d6igEMQOSzKXn2XDbd+z7q/d/GtkTsPWGYQO77kAR9LPD1FB2CpuX1y943O+Ipqn9PPH2S8Pu7IX7vuNeHrgfD7zfB+q+Q4pAutdGioH9FkGCBvfARw+5WnaSbZihWQesY99e8fLyilKA72/fcTqD6LZVGrPNI0QSQLegH6cbxIVe9WHYdvXG4FRUhgUkmKCWiOgEmNMkETpbZ62XI7Bo8xGGSa2bE3DsePv+xrXhAs9s4NG7097TGAyvFuuRgCycF96/ewriVgqNXBuodeNCF0HpBb3F54panSXP6zXHGCABH0kl9o2N3Q2MLtsgUAFC8cRcTCNgympLpWuupMI73/sJGxq8L+7Zk0yfnPrI0lFgNre4uUHXzPeFeiuZSczmyoMGsYCG++PxcMZHvm8tBC7oA3/78b/d6GDDay2b15MBb2/veDzuUAhrghE1oSNJReAG/HbbUMuGx2OyAoeDIOWDzRYBAUDqVqGFazMBKAyjC8SQ9cCbvgJlYAxGIhUVVRWCgtYaWmMTeusDZzsBdGfdjFpiAtGRrzHmeLBOOYxir/Ego/OscY4o/XTcGLRW/OXf/oz7/R3f3+4E1vDog8u+sm8kAGkNpWyopaKNjooNozGCZ9sOWMXj8QAEqGXzJvbw/Vvx9euf8XJ7wff7Ha9fvqIN7gOFovUDgpsTUnGNFI90TiAjKFuBikErZQgZLYFNBdYMKgVmBaMLxgl0Z+8VNchmGK3hfJyM9G1K8qjWcByC0SkbBUBVQWsHRmf9n42O19dvuO033B/vGNaw6ZayQQudDNHz1cCME4LLBkFHVUCl42wkQ6nbRrIcr7fmTiuopcIcbAjMe0cWFK3o5x2lbksZAGteITse73e0QWIgWqQVzsjGHnglrVpEWxgSPsEdD3SI/eUvf8Z5HDjPg+tc6dy4OdFPROlEgMdx0EHqDrOteiuMcYIRxXCkudHuepdAr6dcFdeX6Swyb7jtGQlM751EZHQGFBgGSt2gniYvrdODeNIBQoZzGtntbJk5E6RTzZ1AJM7BYtQzeg4VjIevC6EzghHL+V0apUsEL3SBcoyIbAOXgWMwgtkNCTrouKSzcowBhZK10rGIVE1wteILsys4iRpas47WTzgV2OIckmvED/P71MuuK416QUWyviu/s1r7T0eklU5cOZ0D671ij8Mdg2nEL2NZLfpJKjodw/zRn2t4e6AVsKXRP8d9iar53CWwXwGLOx2ivhYBMBP3BqHdjEytz5eOqUK2+5ArquQuGJ5ic5n7cCCHIz4ik0/lEhNw2fV5l8/jXuFsAIBovwQJEDVt2vjpCqqeUpbtnyWbSjoMLtfC+irHshaX9WDRw3qSAq6vapihN0BrlCqQ/Oo4hvMI+LwkZF9GtjzTApcXAM21I0BGsv84fp/HbweL3SNmQWtowNC1hsOjjmKXmpfwSIioL86ow5hG9FgWVlDPh4BjaQLTU8ZSnG9PHiNyvYw0dMjyGQJnxIXAhRu03rF5LZngYNfccVElGyfIstoB/HDbMLaCU+BRUKaNqQa9uRfvO2EaA1xshl1UsFX2BjuctllBO/Pbly/UvUd3AaP59/v9dIOy4OX1JetL2EeOz0ZDa8Aw2z0A5mlF5mldHWdvLmToGT97hwkbuRdPI2StUKRhTIXAXmxrvzxfH2O4h56MWIJQ2hHd43zXrWa9aGTPqwxEbZ8BTp6jTDGGp6W6wnh92dk3sTWXmerCz9dopNyk2DQM4h46LpT91OgQoDDtvWF3D/5x8tpbZWzwcRw4TkaiRdnuQDoNstENeno0ZDS24Y5WHUZ6kOGAQIenHMf6yjW21i1yvKwCjtQaJ5EyRpnY4Bzpvfcly0dVf3Kbho24k4bvEPmzDUuwIuK1smaAMyvO5sAGc2r77H0O0Eh0fBSGwdEADGRj8lAlbLHB1hbV92SAerbOKUmos3ptGZHwvqGY6W0EX3wWQ8esTzPY2XzPdpztxNnZg3Q4WN6UMN7ECRhU0FunI6lW1ELm296a72kl82gtnqbISP3b+8OJuyJ7QNJoUJhHjinAqBTpqAoP9OgEsIsZ6WRBAJTgcoBR7r1sCTyLsQVQGx1HI3ioteDxOKEqaM33oKfvI4CAdVgfGBB0r78rpXC/tAMdlfMIvhsDMnKFwr1lw9i2oAh7VgKolf0H+3hJQG1C5k1z4pY0pITtLzYteB8HhhmqtxcJsMJUXTqh4LqkQjJaxD3oUSWV6cgIIAEC4ObOzMguCOIXU7ZccR+Wfz9SEllXaGA2gr/WNLDVSWsKCiOBIV+HQaQvbxLsJ+nzH2uzlM3F1axJZPr1WAw/ZjB4fIwZDC4YRWtm1miMC+AaQ2TsiJejK+DpmOGE4vNEOjjHnvW+4a818QySkU6M0PUCOlHF38uwwXRxjNT5tTghlZgDN49aCLy2dqB3B3np4DKPNIfhahAHu5GJMdTcyLzW10VNsYTx7vWoEuDISZvCQM2oKqLFzoxYJtnHOk+A78WwV0LuhKyM71J+jT4WI3g4iJeU0atxPTwqlMk7Ij7WuA/oOLcwsVcgsFwsHO21JFCav7epa2wa7RNcXFCCbxXJFkppznvJQwJU1/0WY1udopgyL2tPExi6w8Tm/gE0U+JjXL3NPcG/PrKoh2Mr9uAKJONcAkw67ibR4YRUlGvrmppTNs+Ne8ZnknOx3I3vwAHiXGcLIs7XEZHkeCcxzwu8sqd3E1dJ/RjlMFNvjsFIfBFZbOoJ6XLvuPwInTLbeoTjeECrpBMhbD1RL6OQ1RDAZeTTUWGeyDPtMdrri+04ZE7qH8fv7vjtNYtjTFDi64StBHiEh2JgepSYFsKNuTbg7L1REPrXZ766IHquyJBUpqwl6BdhZ2EwwMFkbuoge3CDOJStG6iMOB0YrUFrQRDNBEhNw0DEm44DUQM2GmsRv355xb5V/Pi4A0JGu1o2HOcxDfRU0j5G99ApPD0PA8VYi1R39q4624miilMaDIywRLPl1rz2shber1ZAuisUCsTXlxvp6qv4szUHpxVdWFujAtZD+fge54mzd+zb5s8Nf8du1I4A+wbtSLp2KW5UBNGJKzUqPMl5ndGKyFs3ggxP8zDQ+BiD6cbiaWOtMXo5BlMRz/MEAPzlT3/K6CSBVkFxcNsHkcqaFpv/9vrB133D/XFwPr3msY/udW8Fj7Pjfr/j9eWFzcK7eBSNxmn0BwWExvfo0MpIWPEG3vDUTNLAt5yTdERgRk8jRVQc1Ec0Vj1ye3bD2/sDgQannTT3AWCo7iGNz3tjHptK1DQ5iYxSEa+puQnsIq1QJyOgelQcQbZSKmrdoLWwX2CnK0IgCSat0+BA7AFP99q3LZ+0txMdHfv+isfxwHme6WBSjfYbXu9gButB8jNoyHjtq3o6oCnlyX67YYyB1hrOo6HRIvR6sAIxw60Ao9AZsZcNR604TlacDhVAN5gwRbTWAhl0WNz2DS+3G4YZjoOODGIWgpJMZRSmUTLKJg6yWBNo7tD49uXVwQQAG4yEYeDLyyuy3tEGhha8vLxwvXv9oKri9qK43+nAqNUBUO/48w9/9vochRjwv/z131BLzZoRRjS+oCqdNS+3G9eOqKejK8zI5DvGwGgNmzLtGEXx7es3qHYch6C3A7Uqvnx5ZbsJ33MB5Nr9DbWSrMgM2Kpiv22+Dt7dMQRAFLfbCzM+2ruTdA22UIGk918dyJnXVfbuUQMRHG2QTO2FQMmGOSkNDfmBgaMPRpPdVuyjA+cJAeuZjvNEGQLBDRFeYTYIAKE+qbXgtm+oWnCcDxwn9+ZxnChFvT48GME5h+fJOimFQjcNAYuoLwxZ1ofh8WgeRSvY9w1f9pLth+53Ovp6I7Pxy+vOuvnONN3j/nAABGcslwRKj+N0dmD2/N3cGDRE1tDA4fJw34tHL7l/WxsYzXW00kGwbRWjNdxeX/D2fkd7tKlnlTXe4vXIx3Ggx14SgYDRdzphXZaZOzNdX44eUSa3NVxKpoE+lqiJBcCabJLxLQGY2TPCgJ9JmwFSo/SC4NcjVJAEzEyfcXPXQfqUv5MtvPe+MJ0zW2bbNpbUCHVHG4MR4sXYppzzUgx3CtpgRBxVl2kw1y1+f3dcRSlBch08T5zvkbVX9Aq68rN4LtiHthyR58M/R3gekY51M0zSIXdWLGAngY/bCAnKQQcrZZR/z1jjP2Xq8jgffp6fhz6l08zT+905M4a32Nq25cFscU64Xl7SScOBOcbzXPm4/SEuKaQquZ7mcQVEAa7M7awZ/ZsRPnEb6fl5lydPR2GkFo8R/NVuxPl91cfJOQnQxrWbJfmYXxnpjMLcW77uJlCMHRnft+sjB6CNSLK47sbiPPhAxPPH8Xs6/qU+i//sxeZvbQoGfh5pgPMzk7m4Ur4tG8NEHFAaVCLaMaMN1LeudN3zZ8MwxKOdA0CZSmFem6mwkRqG1ZsJGtRDXQC73NhqTfAXkThgaSsh6p606/yYcaPO20t6byyiS3BlbcB5nlcRIxEdnfUy4dUMco6g2hYRQAt0uHfOPTgkHlFeR4WtB6STzMMM0SPLvE9hELLE/cVTYuLVrbJLVi+czbSJ+J8pTh1FairlYZF2M3KNsPkzo8LRz3IYxx3spOZK7zzP6VzAlGgxvVFzOp1fruD1yU/n3ooUiAYqcH9A9bqzrRaYKdkl+3APJlsNEFTR09paZ2NyZ3IdHlW5egtX5Tl/5t6Y6UFwkQ8Hg8EaKCthAOa8mBmkOHDyWpxwJMDBDGR6wbF4lrunbicDsXltVXj9JFLMDWLsBzfMIJ5al/73MBww65NT7Zqn98X7GrasI5l1j77mSPZhBK/+/Q/6199674OKaLAtBKQgaPddk/tKYR0mhtewIQB9Dn65v2Wz8IieG4J4psB6ex4KovfhKgKG00AGK3Oeq4p2ntjqlmRfVQ37vuGnn94I0CNiPgZKEXdsSMq/4lFNAgJGvod6unQ8txm+vOwOWDXXEGzuw1oqbhuNzhZFKgl6PfoxS6+hWrDdqvd/hNdVdjyOw/0JjM5pnZkDF+PUaaamOe/rV2JdznWUZyzGWcjprdZ0NpLi3oDue93TF7daoX320ozvRk/OaC6vgqx/JMOspBEeAkJ8M5XC2sHD58bVUuoCkfh7MUZjPYwBtTBg47lnNHTYQG9k6h5gqyNEaqMKej/dyeXRg0EnkbjOay2I1QisKxnSAN8nPeUmUG32PRtjoA3z1k6G4enKEVVnmw4+RXFDs3gkMdLL+Y4HhrnecfAy0rmGbOwNj1DomHsuQYmnC16l9RLls5mFJAgZmsKexu5Y3oELp4CIEyrG6VPXRV/MBAWr3kOKiA9HOInW38+U10h1VEabQ8csDlXY1F8ztXbdJTkN/J3Ev22qmPXvpy9cxk5Vk06FORNX3ZQTvMzWZUy2noMEThdBtwLJDxePc0OuCcSzZiKK+Xz8GnCyp3NseWCWmdjl97RVaIfNoa7GqeU6SJnx2R2fnpfL7arzn481qnudxBmNlFjTLhMDeH0GlEn85oQ0qz2Rz4Rpv3kkO8YKSJa2xNsVl8ErEIyx5s/LhPgOftqv6/Py95Pc62nh/Mr3/jj+549/ESwC/2wjqM10iehLRmMlSDm4PBTivVf8y89ycREyUfcUCy9EiTlzKIGnIDW3C8IJBMZSyI70sI3RZ/2WP+BxMp0rmgKzfm9H7x3tPNFHw/e37zQ+ykIxn2B4bhuDQWqFBcslQhHGDElGKQTeJ9INdEHUaRZIUdjoYdVkA3YAHiHkdUenEUguitjGZKhkGkGBakXXIIlgasbb4wAQkaSSxrsIoDb7mPEcWzyUc97IVmtpQAELsYy4R8kVufELDoadCAH0AEfdQRjZKgYpfIc2SK1vmMK8d2c3U6Y5p4AFGGlYVu2wjrNH3aQDGptpGqHHVQq2wlrBrfqzHsb6J6NhvlX+D9CL+XZ/R60Vt9sNGIbjwWbYJb3UfXo/w3vra2SlsA/gF0ovQa+Ye57nZnn2ekb6rqrXg/r7LyIQqehgBHUo6xVQ2BPRLEiVFC2Mdp31T6snPaIQscLppURoyqVeb8qJcKbo2jMOAogmI9okekLuAZJOhfr19JVIh/L0tuN+eHrgjdfr7HMJFKj0rFVRUagVjCF4tPCiO7uqZyKoCCqLXBkl9TpniC0GKLzeuaHolhJNVXH2gZIVHfx8jMF1YlGzJqhF8cvPd+z7jj//6U+seVXDtlX8/MubrxsCszEm2AqyK0ap+GaCPGqvFVbhQDwIa7iGixagR3o4ELVa8P2rhQCJnYs0rzmsoxeClqN1JxsR3G5f8NOPb4iMjLe3O/7+95/SYNn2ipfXL6iAe/S5hv8/9t4m1rYtOw/6xpxrrb3Pue/Wqyo7MUEOcajEwcROKZGCkJASpUUjLUQHGkg0EJECJUAmFfIjTEhADqAICZx0iCIhoYTw14AGvxIQEpygkCIRojAStgV24rjqvXrv3Xv23mutOQeNMb4xx9rnvLJTZaTXuKtU756zz9przZ8xx//4hp39o5MDAPbOXp6ToZmq17aVit725Ewwhb7UgvP5hMl7uxqvV2jrmIqlQU614uHh5FFBN4y896l6BOt0msM4AQRLnUKxz4pileK14VbbWQqw7Stu19WZpNEtnVjL4vWVfZx3FaZJuqJK2ShMSfPzrJYtwVrCrt2cNAqL8LFOVwEoI3MFit0jUt0zICaUoh6F7270Idar6/D6792AsxjRmvuUau29nYi3Wq2LZcMY4mxOMbRIqaVBqpU19B6ZMeTVpRqqbUdFrXI07IxLO29GugS5Ybs43TIaxrxTAubkJuysT43WQzFgyuxcAy4HvhURwz7OH/lu89RpnzmgVte1u94wOQLwtu+oU7W1BNdf4tm9e8ouzPFCFEqLtubxZgXJnSJBC6kmjI4dnjGe+YDYHnMDqC7p8fEY2xpZVq5QuWRG/AI5PC8/SDxLIT/RRFl+9zCGplo9I8PP9AvGYY6EjtUYb80ylnKGqdCQkXIZZS9p7ajPHIxruzlkqR72wp3pB5340/Xjly7S/TDXhv4oAkjXcABxfnmtGf00JOShK/DZOfpZChDReQfpyyjOfGqBeISUb6GcJy3gUCsb9JXWhutostHOLy9mquEwl3fXZ+36rtBQ7aIijwMB8J6pDIKm168w1SM8TYMRG6HLs/eQGfCega7nxokbG0GIqtEnUJLiCSHT0iBgdRCK7K3hoQPgOewuxEsx8AVXdEqpgKd1VZkweVuKfTeFEzCQiW29ed0TEfU8rYaNyeFKiEk3iCp2enncU647sCyCFqlJCEP4crmit82N24rHZY61Y09DhSmANgZb033fIjrH/pZQRZ0WtG2FzAVSHPbclU3xmrKpKK6XFa8eH6y1Qm+YasG8zNi3hvfffx+39WqosMroR/X37ii9oC4OLqQWeetqjbCBCV6MFIpi23ZXep3pFaJxWg3ctncHYrFaGqZ0Uggsy2JKkjZLTWkb0HfMdTEQka6BeVYnWz/ZNqerCewBVGv19jBUXM3gb22HNuD8+IB1WzFPE149PKDWCZenq7Vt2Vvak0HPAkttLFIAbZFGbQZZQ60Vu1pKo0WfBB996yOrk5NBQ6XAG20bcfTWsfUeDF5Vcb2tBuI0M2prys7UK4gsGRFyKdi3BinVFNXudXjVIoB72yFiETaC4Jxma5mw9c3PZmp3AXPOFBwFg0jxKHdFqQpxwKlt2zFNu6dWWt2bpcJ6tKf3QKqEG9oWBd0B7LhcnqC94+GseHw4Q+piTKB17PsK1Q3X1VJnLbXQDGapBQ/LCe22AvsOQYm0Yu2Cbd1wvdzQtg2324rTZA3QqZApQZIcFEmEPR0FKBO07Z6ib3vz6tUD5lrR22qK8wSoNGzbimVegGIgPto1lE3xtZ2nCVOdcJ7mcHYI4K0IhpGzTAv2fbWoJ+tWnY/WUoejpRs/7TswzwNYRVQNMGfiWhRcbjc87Avevn0yuqzVSwUQBgW2jlI3nB9OST8lEmwHltF+JvNftmXSZtE11NnqRmfLn1/3HbUWPL46D8AkKOqQSuYA6zsmKKZpQpdhXBdBtFoSKXj8ni+EA6R7DaeqYl039K3hdLL2IirA6/deuzPEMwbU0s7rvNg5EHNuhZEpFiFXGGqpqmKe7ByVammN3R0WIjCQHa/imqc62k5AAe2oWgLMBy6bzE5yoJZmbWh68dqscD4BXQtU7d3d6+9rrSiewtZuFiWuU8W+91gT47OK3sUAf5JxNdWK6dUr3NbVHTgezawTarGU5t1xAR4eHiAQXC5PfoYV7HNqSOcuW1FwdaeuAQeZPmC8poF1a72ZQaHAIbLJ+9soQzRDMbc1MXFn/DhF2bMBR4Acku66r2AWThj3d8ZKdf65u3Jsc7Q06e/5ns+j94a3b55wu23Gu1TNeQcCernOI2460CnYqVS7Uj5+CONPktFOQ5Gfs/xB/Tng+LPx4QbFveKu1JHQh07VEYay+FqZ3GmH7zKtdmuMXHGtGARwh4DaOm37huX0CvvNUpZJg7ldyf2483VvTLFNRlemGgsqy10Y0cWoqWRLmFz3OPpqJ+eFJkObe5H001i7RI/USZ5fgowJUah/usFLkJ+8KwFa6E7b2/Xqjhg/Q35eQpd1olBVA/PyZ5vTzdrJ2bt4bwvU5hywoQE5Ur3Tmvfj+RqOnOP13PhPKSvvrs/c9Z3XLHKjxf0rL3miXj4Robwc0vAO97508O8MUQxXhggZxzAEx0FPRKpJMNxdB++xDCbB9LTsYaYBE/zSo3IPiy2nKXXdPf817hmNrUcqWnj9oB4IpQdRk6FrA+/NlGemwCqAvm0mQyZP++nmVbo+XUBPtG7ADeal7NoCAIJ7QGOHigfgaXSAp96m6KeIp/laDVbrzdfcjK1eDAyERpsBGLRAXd1uq7UmKAU7Iw/zBNevDBRhqlgmi+au14vVX55OzgB9V9Tmc344m9LWdkhpUVA9wWWkC0xVHFq7kAIEhqSou2Jz0B3tBggCgjHAFYPiioQLbmsP4KujjsBYgMvlyfr2lYJtW80YV0tJjCgsBnOV+P+I2gXlOlpjFXhaHCAwNLfq9XlCzVcB0YouFq2yHnGmqNTKCNGQcub1T8KpmBJIwKLu6Xm2h90V3AmT1Dii27pH1ON8WsxBA1NG9tUajHePLFKYEqhnwmwedplQdxNKtRRsu7qzhc4aCnUXtJ0OHio8tqBF3HmQPKh1strYbbca5OihBsEkFQKvU4a1R8E0AWBNkeCyNmxrQ0dHqeQf9veuGutiNDYEpwXq1HuEep+91r3nbIs1c3Uf67riNC+Y5wXzXFFrj75/vXdg37n5ZiTkum4FtFmvQimGEMum8QEoAau3u1xXzDO93wpp/RDlANxH0xv23R0102RnprAHLu/v2G4rLk9Phn65T0CKmNvaM7VyJNGRV9dQLuFZEB1w9F9DrTRrc5oq5mkeUZppDh4a3nOYgk6devLU5dasFcg0MaoqY+6AOT88Lutc18ZWi9fZHhU/i9LamYcC27qhVPU9YRq/KZWn84LTslgtrw5nDXBM7wLf6byefItXb2b+06k6oh0mKzw+Gw4jRiSVHE6o/PP7jOZjKHHpzFBm6nhk0Lu6jOIlbqTcVstG4bmtzGbgeHX0zru6QpsdWAY4Nbm9xtTqErXzub5u1P83N36dT8ig4chYuDd40ufic7V5KA712r4m5mzoacYaxsCL7/DNY2+6KtXX1xdTgaenp3ge08djTi6Xw+ClcUd6FXj7FAo2vHxR2R+/RgnH2LxBL4dopC3IMDb9B0YU1WvdRUqgtuYXv6T3qRsocVgzHaf7Ioukd+zbhq0NXeKXFXVyej7uCUZKMQfj847ej5KMGjKRu9HJsx/SPqV1uv/eL2PQd/ffvTeM0buX38+T36ZhiqNBbfJeDvdlGsnlPCqaItnZYB3OhE8dfh7f/frpOGOxbKT5w93vrs/a9R0bi3bdb27s/t3PLxiGZIIvMBYRPPucaQGWxjAEDYUrlSYqgwCZuKTRaBgkekf0Vm8WUGb+PD8cIhG5IGqqKU+jKH5aFtRSDQChjSgnI2m9N3SMNAHhWEsJpCrOAc6wqjdHzwJp3/YkTBDGUUZzVe24XC8ASigGejVkU6Yd5f0LYW4LiFIq9ta8brKFUStU1P0djMhGLY+n/eytIdcFCKxeSFVj/KVUdLEC7HmqWK8XLPOMh/MZtVast5uhtu4dXawQvQgwLzM2h2WvxRTY9Xaz9OBpxl5MUXv1cMb1coPC6rAggvW2WoSqTJ4Sa0BC0zxhnoFpKmh7x14K5sm+Y8AQwHwy5bSgOWCD2v8hhgA5GWBSQcXlenFHgrWdaF5/2RwURkoJQAamwrH2rDWLIpY6ehaaz6Ojtx3TMuO9V6+gXfH05o21fygWhYDTnJbqW0HKtyhDOCLqAIoKyHBXVKUIljqPPVbz/O8exZhTxKe74rY3cx3MrUbdUm8NfW/Ytoa9Ks7LMs58tz29tRW6msBqrUMUmKeZ+ioAi7Za5FtxOi+o0xyGO+cKWISnq6DUk7dEANCBZZrQ2o51veG6brher5aiLMD7rx8xLxVFdzPu6oRSZtxuBhyyXH3BkAAAIABJREFUnE947/VrrLcdqh21Wp/C3uiNBT0QHo2gQmvPK9WidQUVy1SBecLttvma2pe7Avu2oUvB0+2Gb374LZyWimX2/q/LOXghldneLVW2iGCaJmzrBp3naNfSWg8wrG13p1URbHuHlNmiVqxHJDOg3S3weRTMixtRrpDvm7fOWQu6FFQp+Nzr9zAvgi984X0s8xxztvRYoynrY0kgCYROZZEnr40uEhFrOPppKcDpdLJo+uOM62UFkT5zc/N93w2ZmgaAALIh1qoTHGNvENmT8UIlme1pzBgMwyqiCxL3EdmyoljdqCv6zXleIR+H8e9pnoCrv6NWVAWauFPB+aUSPVQqsrSiW0sV8bfqpi3TObUDogOsqEjxiB9NXxz+Zn0e1cs1HEBKLELQ29D6GF2wdDM3xPkZSUaNB0c0AlQm+Rinne7gYf5Fq2/V0QMUI4on8JQ051WneQJUsDsgE/uyTh7lZBogyxpo94vdaPy1eM2vG7Yq4pFxAG6Y0pG7MwrrvI5ZMICBBJVSMXVrGdJ2Q64Fs13K6Ldr9exWKrPvzeSIVPResd5WPDyccDovKBubvdeRHmmL604Wl70szxCBFBpcY5nN8Od/EEa4OcJcT3KDtPCQGGEc9X4arET4TMY4I4fb7un81Wm9invHujnceA1CMfrqGKqhv4couqPg2A2bZgB1+95CnxqpjEf9MOswzH7MRlykTfr3cpQs0Hn5HNj6MrttGI4IdOEgkGyP3umrQ2fzdUx/C6NPAUkAfDzxzCoa0cijUWggbzlde6xDYQNV3AHxUNfms5J9y+s+u07cuQqYsS3Kt+T58SESYxiP1vHnMcLYB+qUEt/leh2j0u+uz8b1XRmLIgd+4J85MTljfukKJqaZmHH8WZ5/J/3mDIK/jbqPOz/G8Jj5gCUPOCKMzogkE+0QtcGoXZkYKQ52d50mzJ6KakX7FEZMlUvzyafJ31X8AAcP6TarEoowowhwpipDkLiySpQ7Nk63WwfT6V1RKFzDAWZ1SiOKCghG+4jeOxp5o8Lz5fvB0FcgGidzy5kywbQjppjltaSCGOxG1dfXxh8GpxsmlnJFJciROIu1T9m2PdacZEJgGcFIJbppRxFvywBDV53qhN466lQwzxMAS0ubZ2tIXjaD969TdWFLjUT9PSUU3t6snpKRp4hO7JZGJd0RMUUCwn/vHVOdHb6/o3R1A1A8simuZAxhyogmFRxbWkvhiJGlteT6F09zplCo1UFn3DmRPZKkB/eXhPqqPFOg4sgovP2NvcLyWIkGCSrjTr+9Nayk1eT1VH63yFBsZLzLohB+BjzaRi9prRZZk65o6w6qAIao262foUevX78yhaV10r79r7nD5+HhjMeHB+w70207prcXPD1dsEZdL1O+hpPqyKpyeh2guiY3mtH2vlnbl3Xb8cknn+A6T3g4Wz8/O0KuQHkklZFf0nUDW33M2NtmUUU/T9u6YaoTyjJ5/ZPRL5ubs/5yqhYBMV5lC2K9FhFKpjWc73bezSrC6bRgXoB2Jkop0B39lmDHVF6IRh3OpbxQiT4Hf7f0Rji67LzMQY8ZMVtVbb4YipU1nveopX+pe/sKKitU5k3J7G40Dd4pSZ70bsBq6i9R7m3UsQ7+Td7c2o59K9zsUO4OyhrpwN9EdVTA98sYB+WtAOhUxvjhkKV2Nixajnym/XlsVwDYPkW7AR1nMJ6onr0gPB0poo10zj2zoftgswnSE6ppVnDD+CwCaIlVYD0i0x5FYGmeYvOCDONBVC2FV0e9dJSmpCwZCh2FGaGsrxzyMisySS9J/xbnmW33fAx3iESEyv+eHsHl8fMqsFYo3QxspHUpnirZraUNdRE+gBUiMWeBgyONWjAlOaTVpzOgq6ILDQZBQsUJMD5mM1Ee5GdQvofwPg7v4JSIvb27lM+iQgHSur/bjVMSJ9esZxkwXpSMwfEcX4nDeyknlH1F87hD7nH9xCO3JIecUsnP5dk7OOdsIPIdQNIt4+848IRYs4N+OCRFfv5L8+NtZvzmVRh6GnW/b3dFpgEGvdnn412UtQd5rcwS4MjH3ty9AXkY2dj9tG+8uz4b13des5j+yx8l35AMwZxekVNAMjN+VleIoaBCk3ATpi5Iup1eQg1GA0gYdpI+i3MaqUU2lgEzjTBKoNZrjQoFMjNQYxkQ75cIxZvrBVBgWRar7+gDzt8MhFE8oVAT+B4plYTOSeY76jPH0hx66SG301BoN6U0iqwdbryKoegpmvUto7eI89V8aG2tqcASvESkYPcef1ZbZ32RigjW9eaero7ehtD55M3bEL7ruvrkvE9eKAmGnricTlBVvL1cvEmztzoBEQmLg1E0V9IGUqrRmEU9SUe97w4Mq7B0pW7KEyq6wudhtHR5ejKDf7F6md6sRk4KcLko1n0F8B6u3rJinia0XtCaWJoUJqzrjt53PDx4dKAUzMsCUaBhN8j0WnF12Pj3Xj2g947LU8PDwwnTNON2vaKK4OHhDIUZKKVMqHXB7bY6TQPf+OaHUHQs50f03SJMp8WipdItrXb3upplnlGnyZwYIDKhRQCX5YTmQBZWH7hh9ohWbxqp1KUoijs86KUsRdCLpWd3uGe4mLF+vd48Si+YlxnYWrQ6sX0r8axoBl406pII2EQl53xe4hyPVElGc+wsS7Go6OOr13h8PKFvN3zyycdA7YBopJFT2bH0XYVDKUUtmng9bynA5957wHI6QVDRWvPU4k/QW8fTmyeLjM0n9C7YLtfgL3SIGG2a42BbN0shV+uQVmGGUHUAmdtq67NvDcvecVpO6HvDL37zQ9QyeUaCgcq8//7nUEU8hc2cVbd1xbbtaG1Hax0ffvAtSC24vL3gfDrhPXmNdd/wycdv8d57rwwpszUAHeu64Ytf/AKkFKzrFdu2WUqkVFemrEXP48MDJpyxLLOhaW4rdrVo/bpeMNUJ02zjhLJXrUX3btsVkyy27pPXxvo+skaw9w6trG9rmJrXn/cdbVeP/Fs9Mo3m08nORtMB0EN9tjfLWliWGdf1ZrV2jjRqVl8NPhYtgOgY7J71K+p9TTt626K/nvX57VimxQ20ilb68K7AIsbaHYW4d2sI3oejM5w5ntbdmXkAB85x5lxqgbTm59ayBqAj5b46OAVprkhx0CwJ4JzRRseNCa9zFjF+Yenq1s/XGt8nJ6cDxbH1hj0LKA6+wvZYJpv2MYZS4E1MPdJv0cypUu3onp0DpxNbb4GlQbdtc0C5OTI1BA501CnbDGl3by2MHjrMSoE7By2rJWC4xP3E/l7yIrYHChXYZWwYglPFPBWst82j5zNUvGWL7zPRfdvesPeG8+mM6m15uqd1n+YFvXXc1g3rvvvzAe1WA58dbuKGmcoYq2s8nlnjmUldUSrCb8DWIyXxOwhGP2M6e/3/UpLFB4CucmY3NTe0Wm+oM+uDjbaslyrcCWeZIdu2gSmdo+TGI3nuDFftiNx+5Ii0rdO8LJaNwxqVnvQzHNFjPf0GVN0Q22v8cZomrF6XJy5b7Fh2lzOILypMl7o3XNhnOhyoDiilyIYiovTjoI5ifCcb66xrTiNOzxqZD9T3spNMhLXEDppHmqkV8OyXmpXHGAcN8xKymEjfRVgG0MIIN0e+jymWRIJGWNoyEt/HdCJ+GLK7x6IkU9OvHFV5d33Wru+8ZhFOLnr87HCPahwmoXfjBe/L4f7keKGvcdxlTAUKRwR14iwIRn8/mqOni95HifQhESTjxJsjV+tV1huLn3vUCZlQnDAvxhRZmE8PlBRxtDhjrhtozGBYeByHM/3w0tKLxTz6GP+xuDh/ZpdA4WlQwhoIB/0Q9dRRr9/HMNxpeFoBOZAPamGPwMPZJYPjrZlhkzmnfXBmodqxOdS/QqG7Yg9IeUXfDeyCkaNta6m+yJ7bmqWS9a1BptF3ztKbLG0F6gZG79G2wPibpVPW6iiGQgeFFXQPI551K/Y9S1GtwIYw5gudC9qhaoAFe3eAIN/LbV+hAix9gWCkCNPQIioswKgsLI0ueeC1K27XG0QUj6+mKLhXMMKjWKYS6x2AJ+p9M7WBICuz95LU3VKeltMS4ykQoE6o1QVnLViWM7bNHADzvACyo6k5D+Z5xom9qfaGrVo9pXRT4kopqBD0UjB18Ub1Hdt6Q6kDYr+3DcvpjOW0eG1Zc1q0CFl1gBvSnSnZzYS9FPR9dwNh8AgKx1KqRcRdUcAu6N70vZTp6EBSYCKdCgI9Ek3wrW99jLTr0L7jm9/8FrZdo18p06bfvHnrRmtxRa1ZWqh2XG83TLViniv2XbDtDVJnTKWYwVgMLIb8rtYJ8+mMbb0ZuA1MSa/zYimo2watFZPOqH5Wi1TcbpcwXHq37DDWunnnEVdseViN1tgOAb1hW1esN2vjoWJKwt52LPOEZTlh8qPP/89TxVQLlnmB1RsL0CfUyWo9zeFmKeLmgCkReRmpYBbpbcH+Un24oxTSmGxNmRABdbCofduwbquDfslwzCnw+PiIx8cHXK9X631YplDQRCxNeTnPaL3jerugyjSMqOp0It5iqHd4sBK3627rMp3M0QBva+KOT7aekcL0V/HyBiqNVtMZzKZ3c4SBhsJQFksphhZEviXDkQgpgchKhcz0avISGYoe5ZCZ6eh9RAQN/dieVzBQoRXd0+jNehzy3rM7aokzzbpS1rsHSIgjpSqG86FnRdaV0Rp9Y3XQgZcXCCxDgQqtFO8DCovS1VIBsUyN5kZvLQXihN9phPnV0nssw8T2Zy5DJWIq6eBDlpVSHaXTWvowdY+9+yyboTWrEcZ51FJTXlc/86UQ+bmEs478jkqPlRjQxyVAao1Fw5YKPVV1yiFrb+S80XtZQkebHZtjizTqo2PdeCFTT7t2L4vomOdTGCra1fSvQLPyVlKewsuabqjraTSCMAwpBgDyRT2RY5smOv8JgnJniaXvjejoWKcoFwq0UF8tOmz8HKjPu3vZBR0jMd873gXJetiYj48GR5NojI0yq1CTTmM6XrbDh31J86KDifdOswHzaVPItsc+P3+qv9/3d9fdMqyqlQxs22pR7njXcSzinzcGZLJu6BMemX5ytyZ5749jskydI0DOu+uzcX1XaKilpDoP4MBsRsh8HJ7x31Erxb8dUKkG7SOnfQ5ihfOK8b4cmbRnuNcSTD0aKUDqXhdU87zuXocGtdYNpRTo7rDjwh5v6syv4dXjYxTvqyrWzRBFp2V24aJRh0aDeYJAitVfdPcGQphiwTV1dVGtUXI0PlWfu4w5sCG1eVgVghkGqqZobRil1DukKKTJSNuD75t6PQr3jcII0ZxgjA+AlBEdLbA2JeIeYqurMV4Y2Xlaxi8Auu6IhtrBcIESfRYFpVC5KeFoEFjaYldFc4j6qPUD0NGxby3SNS+XLZjyYFOC7bZxsaH7hqd1A1xJWW+mrMlc8a2PPvA5C87TCW8+ufjw1NHwgIfz2VMCNaKY27ri9ev3TfC7oJ6WU5yFAoPk790893VeDFBEBJNHB1EGmIUZPBXzaYaum0VP4d5RuLNDTLG5rZbiKN0AXFoH9Lqha8Hl7RXbegO041c/fh/qVPHJxx+HodxbC2Vwnic8Pb3FRx99jPfefx+oBVYKoYF6t603tK547+EVeu+mrLtB//D4gLbv+Pjjj1F6w+Pr15CLKcTzMuO0mLFqvfHMKz9PMyCCx1ePgABPbyds+4bzspjxreo1cQXrrWEXQS0LAKtf0i44TxXXy0fYt7fY946np4a//bd/Ae+9eoVaZhTZIbg6mq+3CWgWrSulQDcBivesU8E3fvFDvH37hMt1x+OrR7z36oSPPnljiJ9QyFQwnWcHv1GINJznCZ9///OYlwUffvABqgN0AEDfG9q2YqkVUhStbxCc8fr1a8zrDbfL1QyOAvS2mbKWvK0WVSF/tXqszv6X6Jgn8keLGvWmmOvkPQ4binQ8PixQ7YaoKhOsmfsrNygbpnn2+sIRdXg4n7HMM86nCW3dsTuSrEjH68+9wrIsELmgtYbLbcO+OV1OrkzDIh/L6QHz5YZtb9YSo3h/vTrjPC/AfoGK4vR4wjLPaJs5PEyxZs2jYt13bLshxe4d2LYVIoL3Xn8OUy14+/YttnULZ2BrDeu6mUEzG13dni7mXDmf0HGKs9ar4unprdd62co/nM6Y5hmzRxFba7hcPkHbG77ve78PIpZdIb3B+sgKXr3/2hwbpaD3HW23MwJ0LLPxNuu1R8ONJQwTlqWgakOdJkPU3DtOpwdzPu7GJ3vveDzNTssWOdvWDa0ozucHj8ZRtprzs05eS9w7Xr16CATjWido79i2hloR0byHh7NF8RRY1xuenswZcZrMuXp+9YDHh8eglcdXryBThUxXyIWgPooiitPpjK4dT5crtnXHw/mM03nB5XLBbV0dBbmjTHYut62ZIloLzp9/jdvTBe1yQdsaeqFzR1xmmhPtdr35Hht/2bY1pdybAWM1nX0ge4qi6YZ9F7SdURqKCENzbe0GEcG+dbzdr7hebpjnGYs42JPXA9sxtciiGWQm71W99t8N5Pdfv4f5tEA//AitPRlarCoeX78GAKwO5DTPi6GV7w37brXOUxVoKWj7bqjAQNTL0qnV0A0JWcQcRhaytfd4m6g6edo5dBiyqlFSIhCgK3RrKKeCZZq9bt1rOHWkJ6MjnNS1WPmHurOOkeAiNubu7c0AoEyTaRqun9HhazX7FZ//3OtIM257R6kLtm11BGHzuogqHJoc5ves2LcN0O58vKO1DVJmc1i4ISjFwAdXB1uqk4dlCZA3JwhdABDvw+qOlarmECi+pnQMuQpzwJtQte9alprNk3WwvE88NZx6M/tMMl0eioiKjhRtYD4Nvmio5MWRghXzXOKZltEzB08bhqbX1hZgFmtfA/j3pwG+dIgm841KDI4O1u4av8klMsOoVQggXh7kBvehnKDQyfXu+ixe30XN4p1HJRmJdg2vyuFbw2k2Lncz3RNjGIIveJC+/edAJPm/cF8YtOGL87oDEeS0i5HmaGMkuin7UeX5Dy+fnYSXxh4ewjsjjEKVaTRhGntmxfAOhqtmeMbu557GEzvgY3P0BVi+uVl0Nh5NnqBReBxrc/f8nDMvoLIK6N3GhqGavly0HAz7wVAGKpkmpyHXlKlF4kx0fB+DKfv6QZjQkJKVaXPrmGE4NA40i1EXgcG82OssUpnI1JqtZRj1CkBWZ9jOlC1s6h717orZFu9nnz6iltLbq76I++Ypg9xzko8tjkeATHgUB824d9yUUizKBsXWdmx9x94bJk9Ps1JM8dqZEspV6y1StEMZcK9+ax1yMgfLrDO2zfrjETadgjQ82SKorlS8efMG59MJyzKbQt8bSpmwbiua9/CzdDOb6EDOFGz7ju73g2cdFk3am0K1GViHemPy3gHpsd6mNFn9mUXhDCHYzrC6gV6wTAsUguXU8erVI86nGXWq1v+sNdRScLtcob3j8++/H55rKg373uIAEECkTm7IuVPput1cSWsgIBYAB0YyYIw468w4cN1o3fZobE6BXsCG80aeZbcIfXOjuLqRRgXbjFPLQCjuoMkHmMp272pprtsWCoRqx8efvME0VVyvqxucx/Sn4unjpQBPlwsghhoMIXDSjtvtasrJMgNSXCnjYbL/EmXTGtQrShUsZbF6S09f7q1hp1PBU8FsHzYzlryWeKoT6uOjl0MHYwDTrE6nBeJ1neRD+755KafVXi7LDJ0nXC5vzGDQjnk2A4FVUdob4IpUrYJZK7RWiIPVEOG3ipjRjx2Xy9XGxEwGTwMnYm11Z58BZlUUGeAyxWukzcC2dF+jveo8Y2S/mPPU9qIUU3yr1wjL3d7T4bsshNr3HriwdFEqfW/fvsVt37B7uyEzJDzNr7eoXZ6m2XgDFeZpDoCh5kqwtZupQO9Yr1e0to/ximDvnjpJPUStn6V/AsDoADDaH3XzDkJU6cRUALaG0zJkO5y9lio4zaZkV08X5ZqZDLDIjinZ/g5HF4+HwGrW1UF6Lrcr1n3Fvq1k4e7wcr7pTJ5PqQVQ33+HKECdqtfzwspPYJky7IVbi0V/ai1QGjHuSIpMJ4x0TMuuGnVoojB5KiUisqwfzz1BIcCE4k59DRgIGiQEHCwCgAaUGxJ0yBifEBSZhm6hDZent9jXFVAiYZthy0xyQAJkJ2S4eDYJ36/qLSG8Ttv5ux11PTgHqHjEGTjojcpJ+Xk3DAgDvRqRvvia63mMOpMqQ4Y7XkNuc3If2eUYmCGcRxNO8qR0qRoaMbPanht38aJB4xyzP+vQDkbksAYvRz5jpLEPBGJ7rndzXfJn4/2afn93ffau7yKyaP+qR73IHOyz5/na+UAL7oh4sNbD/S8bi3r8OVsVd2ZY/j69ZWxLEV/xU8goFWsoIrXSU7EggmmqOC8nF45U0I15WoTJmaIk72QoiyktAlaczoHaGSoO+f/C2IFDa2/WiIz7Rr5+3othmLtxdDD8LD5Fpsu73P/jRmRa2rxmeXaS1pyRPEoMB0wY0V8gUvpokgrX6O5ZPhPShnk6LfXn5cQK91R5eiuBIvhA5dgSjbIpegGLtoeBfWhNwLH1owLLHPwxXkDRHNjDVtKea/WC3b8vrrxztpam6tFTQdR3ilh9B9EDtQ2F1tLDWijdrCsqItj3zXqPuaFF45PK5NPlAqbmtnULwaUKoDV88MEH2LbdaxdN+Oyto/SO6+1qUcRthcB6EiqGUa3dIpzhad0bWkoPNMV/9xRbS5uBWE2PFMXjq0dcLhdsq9XC9VJxu10xzROmebF37jtUBLsq9nXDulofRMiM1sUiX2vDulvt6bpbxESEEeCOaa4RRWtierSNTzDPS6ydCLA44MzjecG8mFK1rTu2fcN6fYKI4PHhIZQn8pvu3m4ztkwYV09dg69F23eABtY0RbbGtjVMU8XDw+ze8SOwlsKf78q1FEH1WtQinqKnCswWPbT9Zu2aQqQHNHohYrKfAIUZn1OdHJ3U/r+tPtbolVZwuV4BjPo2OgOqVGAjArQ3M2+bvSNSuwBVwfVmUfvihqJ9PmrISVeRtho8xtKg6LhpzQzGUgWn08nGvG3WQzScHM2V88mN7Abc1ecsdYEIsG3u7e8d27YC8x5nvU7m5b/err4nZqAVBziJ9kbiRt9kPXjtXFuEYhdPqRSgyoRaJuz7FoomipgDoFSIR1JA5NMi1qdRDFHXeqCa8dC6pYwT4IdOm+7GSPX6Lot4MDPDjE0D+RpX8/MvYmtKg50Oj+iB1xrevnnjNagaTlfKqm3fTJGvFdob9rZjYqubYumprTH6JpbS6qn16/USTh4pLCVxh4QItmbow8s0W59Ij77SSXWDZ0QM4RrRNEY2Dj2iea8r18UBzAywrAyFV4hC22NvkZ8Dr52H9zesxfnjzdoR9R7tUsx5YCmsoQW03dLMi6UaCw8/BCLVnEBdMRV4TX9xuQQUrws225VyyDKWepp3cRTZqVTsaIAOdGLI5A4gT1+d6p2u5rpGbWjNWy9Z7jsKjs4nIT2WY5CBqZwAAqzPDFHg6e0bczYWoMhkZQTTNFKDqeBI0hHFZpX1Dqb97l5KIV7gqwpUL6mIHsoSmoN/Hh7giNAWsUwsgi7RQWfyw9JdGaG0MdxhVRSv1fexaTc+QT4b6xLTqWHIDoexu0TuMvSa940FRnTvsN4YDoH8Pd6fSwNCB6ezwZ9zcOYfjEmnvahDRug8vNdQ330dBI66a1cFj887NNTP4vVdts44GoTjs0HU8TuG9+CXfObfyR+PPN7+9egRFesxpnSjJjQntdYCdviGIUZ0OHrK6Zlu3lxXk0EAoZrFcfDzPNjBAI4G8HMvTP4OV493FDcq6I0yASHjXsnGnsZz1NeAxlc4jlQ8nXQc3niO71p2KnGoJaSmjyWMv+PIx9zTPJ3hDNoI7nPgRkLhgee0Q09xniYF6jFXnxKF8/JZlUEXZPRhBJYh5MbYysHRwMpTDi7Lq+GZA7QfvbnIY1CuEh0VblCm7wcoRjybsijRcForMyTYi8yiBvle1kwWcb94d+AB3+fL0xNIRMMpYe/Yti280VJMUYPPgwIm0nJgCsK+mZHBqMe+2+fmCd25IGDKeS0VG+A1uG7IdPV6wuE22bcd625pkZNHC01ZZ31SQfWWDUUssjo7CsSynKK2T8SU8U504Go9+faN9UqaUsJ77EEooEJDC3aW7jy0/ALLbcgWItohEulftvt02Jji4j6goJ/nTjfyMYenL/y5Dxrkd7uCXQZ4Hml8au8eJ/G+lpUVm9SbB9gCabyA/HMYJpyTOg12FUNuFPKSwQtErH7MFCZSMiAozq+Gkw2+JjxnVicMbFSY3LDtrUHNOwCR0c4gZ4OMHIEjXyH/iDlyjRN/CUdcGhf5grLOJ/bEv5zq/dQNPvW1YBbEAP9IAxMEEBh09HC0qIyGnCO/5jmK+aQzfIw2jDIRxOdWX2hGpozvKmvMeO6dnzHFOK8HCB6TlFp3Spiy6OvSB49jNLpIAUqPyA7THJlObPyNchlBS6qpVir20Witp/MqhSUWw2nj7DbOZpZLwevNxvGsFjeIfN26mBwc8xqEJM67IYpi5pNHxeyPxUNkPVGg3BkMpngPWcgxkfcw+hi8KB7lWAp5L4ImSOdcFzpqBUTKzVElyjiS4ZCHfI4ELysyHK6Svsv3Uk4dspMEAc5KmhnYARKZEAVDrjJCel/OZPuWomPx2VhhSOQWpfMwZJqdIU0y/S4Sdzhv/Gw8JW9DDnpk2U5eIMDwL+g4c8/0QUmLOKR9bOYzF3rWofKzFJ/6bDGCPT4mzSN/em+EHh4Hw1IgP/iU257RAT97d302r+8c4CYp5IeoGQ+GyOEeAIdTlA/QSxHE8RnwIv28cC9/pvckM6r8FR6y8KbrEMhE/8zzFMDTmEzBzY24DychfuZEs6LEwID1AAAgAElEQVTtpogoWA347S4TnnxCLJr9o3eGWkzK7j0YOGnRg3/I+KsqAoobwHiniDPT47tBwe+PzkwvyRf7rNwffiqbg/HGPvcR1wgDypVFOt1kWEn2vpLnoWFU8vcsCMiWxtgtNe5e0bDVGgI8xsj9CLr1seEoOPidAqT6UHM69DCY6cUzQBfIaPXBlgYC99CVcQBqjAdQLVZLWNj0vR3HWqiY+PxJ796fMow7ZfozU4lknAcZ4BgzQW1Uo7gdOKKzFYIHuKdUSsFUCvZtjagg0S9nT+263m7ju+h4evuEWivO5wd0h5h/9eo9O3ddIaxF7ur9Fy1S1Dprwiz1rJaKaapYV1Oa5mVG2SvagymKp2VxRM+GMgmmeULbvOF6KW78NEzzEoauFIFoiehV6damo0hBnavXnw1+yLpcAhLhHqwCrjSHEa0o/c6IcVAP1ZEyFvKVSp4rePu2mxLlTq3h1eW7DJK/aEFLSjGjbdqH6M5Rl+5anqpi8j5eLfhrhaCD/WSzkuk4megNWFvDNNmeE2WXNFk9csazFB7ortjp9OhjXNHPEm5gpIvcbt+8bUpSGm3JRk32wdmULvKSInGAMM9z8M4AhXDgGlWPJqh6WiZQU6p8B7A3RrA4yiGruEVdgd2jOEWplJop2dVS6IgCql293tCepzrM+mfRBDfO6fHnn4rLvMyH7VmJyETCOKShGvvgkT0ax5aZUyIKbF8vyQliV51qzCUrzgGqBjszxns0+KRAvX2T8a7N0+2672fbk0Ge5XsZc2C0n+sfuAKHNeAknQ/TYEhj5lUB6GRGtnZDdba1ZdTI5s3Io9XwWrulyFrqlEFHdcxYho/PGQeN0dgt8ZY1MpyNvR8NiWGQSBgLHgB0fuQpzsL/5Cgse2xKnK3DMoVOJfGcoIUsf4X3CIcRuo24fI5+jn7G2Wqku0wRp3uuS6Zz69Fpg+u9HPZ2pFNn97Wbi+E8orpBHt5jLlxnlBJ4C+a0uI+4uatNxyuyszXzGo6dDsrQEA94NUlhdV4toO7hzybdFjvY2SGWddNMswfDN9+frpDrTveHdN30nCOfoY6Z1zW/azi+swPrYFi/wI/fXZ+NSz7NO/BLXb/qC79KSUDZWIxc6j5qhJ691AUQryh4rqm5L5ncC9/N/+brfi7BQJJBE4qMC43iEZ1QjJKgY23OMhkSINNpNk+rgwzjRlUxe80HqDgkIdQdAa6Ugqo5FbMfjLeYpwJlElfghuD1wSePTAnPdPbSRPTnsD7xR7Cuw+3Xg5HvdyMtV5i3PTHDkp+ZmFnMgcqYC3xxo+hALx6tE8Kzu3JBY4XG4r2HnM/PZJD3f/NamejTp/TiU1nwsarGnO4VBu3qNXCevod4lCPM3tM362jy87gWADNnTNhwnjoMcF9DerypnNoaUJHm+fHWJV7H1hytLjtwBIh0F1C5A6KmsE4GbBE073OjU0ShDiIxkO+sZUN6Rzkq6xoRlLHHdfJap25GxTzPVgPmRqJgpD/zuXTaWL/SO4Cq3lBKxe4onsu8WA++3nF5ukAKcD4/QCF4++YTzPOM89lSS683S+k9zYujaG6oc8VpOeF6u2FbVwP4WBaoKq5eQ/bweMZSKz76+OOYk7jhEWiurvQzyqYgimdiQOoKI+sGdURk2SQ+c73BVyVFWo7PI92zx5uq1wXGmRjPlVrinQglTJzehrIOOTrNeNClDLj68NhrViZGlInpilRyVdXBcwzQhXWlUgTiKYdMu2Nd7KgZTgZQapgtMlIdeWimUtHabspbcaXRa4Ar66UAbwNRAkCCmQY0imqdgq+KCLS1OG9O4WA9EiPQU51hnLGjw85MT/uW5aSlCzd/33BaBZ0rG2sP5Z8OnVIrtnWL++z8pnQ3pp1LRuxMz8s8kHzPz393EJTh0O2HdYno0Z0CSrq6N1QN4dp6f9YEmrH6+I3/KQTVjWc1cBsgGRA4PHOaKtrePLW7QmpB29lWaRiGihT1g0caddTSJsvR9tyju501eGk+doR4PhIv9u/2pl4NYBkRUASAHZfEfvNavcHwMJSULNCCpfgveoxCikDkruYumWBcqx6wlJ5KCfW0VolSGsviGPKYdGoGDM8+wtGT97ylVELSU8106XzOWpYMLIl7YyOsIjQ3CCfXdQaPC2RakSgpCoeNf34vl++DGdkRTJoePnE5jIt8ms+RejRSRQky6PXadITREFW1hcvKUjrfh0u9DUlChre9dOdrjGRs9UvzzL/nf+/3LffXpg5+/z3+zLrwWIfQU446R2t60PF4RX2q6mE/Yi4Y+/PB04531/+/l24fPjegfonru0JDvf/9PsL3afcersSUkPxN/s273/8Ox0gNnKkoNKjcgqzCxkRDub6/amVajqedKtM0EGNzeRdrIABUigmMmInEGPj+5+bcmDUF96fOLflgJH12sIzvvj+2QdM3EmMN+0zTfUM1grsEjwZh+kXduMivYdoN9BlN2Lo9X6O0RGkiruyGlE6RlvQEuNFDQzGPHjBhlctKiv+RnlfuEY3Kw5rRAKdMGzL4YPDR0JP0fTPw7jybkmhfjwZrvNfnYFO2F5nRUDDSzayOq0OjtpXeduhIBbG0MVf2XLDlFUakhnEtx5qw99rwYHOPhmBh64hKZ4kbCxPrTIAwuudpDmND1Y3QqUQtz+zGGNNpaykOVW/nr04F0qyrm/h+N7hcTtEm8fVrzZrJR0sbceAbN0xv6zoUEQynUWvdhbiil1GD0bVBSh8KNo0AsH5z7CM90aELMWVIaLwwhXPQMfck6CXTgfAcHG5wVEymn9piSDrL9EQDjITZswB4NJn1OmP891coOclY/FSnYEzIFURlOqFFVUR6RKd4vEaNpCtq/YUzbiw96M7WYUQlbZysIzIOk+txALGU1XQ27az7ucmOzFC+cVDe7iMpwtREME5iURHLJNHYO75PYOizyjXxdNkcLYizqIjvd44fcKcNDVUZhjzpJVqEjL0k+AQvngHWEg0n79hr9qKLNUx0MPbdf35BYeW5qjTQ7MBEzaHRk0sjJ/pC0CodPCo2wogD3i0DBM8q1Z5DuVC48jFW518hFRJN2WCDvqCUT1nHL+O2w2CGzKT8UDdkODfuCeBpwncRca7p4QPey69i8DPSwfH+LNdH/1COintXfBIqw0FRfLIqjIBnXUyDZmO/YkrkS3JcBzHaj2Jm+HrAHJ9IesY9HfH+MOTGciThq2EoZqGbdZlsTNEB7afysE5IX+GcDnfEnEfWjOrIaiD/KAC0VM9ASbNxearUp+50ZePNdiMzhZwhDT0g9kPGWPP2x7rfpczeXTnYcr+P93uRDcL79/DvMReRqJ8/XomG8DxuGHpgbPC767N6/YoYi59GgKVEtQueUdGdN43RkkxN9DoGa5cR1XpJQTn8TiYWonoIafWTbK9jmlgfRzFkh0Utigha211owqM6PkcAjIYxomBKozNon1AX8RYQ9mlxI0ETA+CrOe+Yx/MTNu7lAZT8hPRjGMFDuelgcTs/T8+926bQLXy7smAIIULj486DxZoi4K5VRhqnxpqkK8giq2oUZpQXgkxTxvSHF3SKNJUjI0VJnmY3jmqK3tqYhv+WKHedkU+OrSLMfSon5g0dnvx7AZqNWxo1+V6uzyH66ksl8f1xVmofymFxwTgiysezYItn85q8nUOkVBVBVUJe27qyiXNv1vx7QHxLPPLgGVUN72Ol4ukgIgprk1GXxREwNzw8PAAAtnWDAlhOC0oRvF3fokjB6WztDNbbBnVAGoigtdXSSucJt7YGvbTWvLjfUh/3bQ3DrvWG7bJFbRQAa8fhUWNpDTujYACW5YR5nnC5XqFqnuKHs7VP0I8+iQge19mMnDqMQiMEQ2GUakZgWqsRhLN2M1BDBS0CFI9mmQLRIQ5uYPxOTUkuPKd23zA2C1QLqq+B0dlIBwtjmAAzxY3sQIsssJrLYegcWY9zG++bJ4LUiysZWAoAHVkuWJpzCZlQZIJWz75w/kA6jSgAJAzoAOSwXHDPyfb+q4wWUuHVbs6E9Dz2y1VFgMooOqRWMFZozc171D5mBU25zgdjTDBNSzheaDgD1msO/rvuW/DRiJJDoGK01bq12ygeEaWhNLRX/54Aqj4G8X6qMNmiANjWqLVmSmsyEIMGdPBENoMP/uP3du9nN1o/aSiy7EcsIRhsnRRWE9x7h0QKKSBSHbE2GUfUoD3KRGRSy8hjnSunPxwr/KeIZdxgGoAuAFDL7Huh/rvPGeTVGqn5wcf9v2a/WNZE1wFkYg+SIAURQVONPeR8jI96FieNua6Ysgwo9vzm2Rc9DELPAnFnmGanXYfLcAnHijlR1LNCvIdn6ENjL+kA4jhpkBLwysi4xt+IbGvtkYZ+Id4Og1cjaqt/Lg0pU2L0uRTI8IBR98j1tIW9ge8cybCze1v3IDFbVkWtk59xnk3/bhl1tlHj6q3IuN7DyZp01GmKNFdgqBSZd3H/ujuuWCpS55Led6c4+aWRMSVQLeFoQhkp6MxKMLleoIU8MJ3NPpwz90VMOdNEU/bCwZC7M8xFJOEFDJ3t/hrI1/dK6FgnOg8pb7ODw0QhdS6xPrmJrx4j90O/f3d99q5f0cjiCzchIPcAWkJwaTVu83ufPfPZ58PTBiC8SZ82HnqR6NvKXqeIMCqJeygBofz3HnUd0aTVD1+BQt1Dxs/CgxmDRwDGFHg6EobwFUVKiXDmTlUxT+eFtQ1mBkRNnw16yGKNHyhkEUIkPzqvncazj97w8bcjA81juWcoIbRC0Xr+3oh29OO6xX15/nL48dn6cHUPX5HklLh7MG0oULDlObhHlsX/A5RivD/GkJXixKA/bV3yvfe/5xpAo4cRDQnBybkVCTj/oMP0vDzR7IDI4wwDXlwxp8Lh82bK6r3xW4ulP2fav28pQ+fJ1nYDiJomoO3QvQ8j2M8g+x621iCTGzM+AzZGJkImIyibI79p0WgNYRFM9f6Niu7pj70RBKUa6p4rpnDjR4rVItJYZ08swFKnDTl1ciWb/5ZwgnCN6HYoVCoLJ0p2486RYgoyPyMvo1LE9HN5RjuaDryOzyARdWVNpykgx++K86CDz0YbxFqih/PKIgM9jKA4LHdKDLC7QW11aDzOqkfeQeAMiKWcW6NvWEuEMvoo7k5veuche9E5Waze0nUvZ3W+F0iIjxFVHGnZNEqosNgyjDRLW89+ODtsCs76RO0dZbbeugq4Uu9zpI6s3B9NCM32/FonT8dt0FjbYYQc+cYwBgbjwqHenP92uctgQKq7S06oAkXx/nLDuaRorhQXl9fd27eIP6PD+Kop49YjUDrBqYqlXnoNNYpDb4in2DX7XhFGVj3dtxbsyjYbRvdMd40l873nPWPIjJiZ0ddd+GV1I9QOB1KiXI8zp4BjXwXfhcs/c+qZMqwOZhUGogtF1g0H3+qCXgxtVkQCubp4hkTbd2gjvfo4fFspqy192s57iQwd5x3o1keXDhAcI1YBFkZ6ORx4IKLijiuwN+c3fldJ99VKQ9B0Ambd0AmhyZHTu9Ok9mSousFUBGj3Al4gnsY5xmaam4Qzg3J86DDdZfZQAYfcl6ATxB4KN/nwal+v3tGTY0zEU0CpFyg8CjvqDMNwkxJ/u3+2OQg4H4mIpI/K6mop30mLL+p6hgKsqrHPx6ujK+JZ3y6yeH99WtAlZ9m8ZDjn72djMjI3JO2HUH4f3vLLGsu767NzfVdoqAcj40AJZHxDKRFXcKgohdFHpdWPKmWrCAIN7zBgpt7EoXt5PHxQsD8yDOcmohL1K9Nk/d3u52Npb14H4XLaBtY9lQ/RVmMYVmS6IzefguvYUMMEW9GUw89xOoMN72LSC0M3SPYD4PXN42N7xp0SwbfQ48p5xq3CyMHRUNSDvU/Qh7Ev9OQZoqYr9WS4SAqXK3PZWKGXv7hAsv0Z38kMnEAxR1ITF67OnCYCCewxxoMQJew2WNMakuSwoJVpiK4U1mL1NKPwe9AqWBtFI0uP65sjrpIWlYZJKIlSvLegKa0GK19CGT2cBIWlR4u5QkpC6qWhy+GF8knvbh/1sxAxz74rPdVrqULJXJYQdjQMu9eSLbX6ftu+LicDg7ndbgA8WiiCfiHYi/ed04592w2KvXfsbcO2TihTwXk52R4TTlwbtgTfLyLY9g3X2zWcNfvVjEZrtJxQVlUdoMaUD4OMb75d7Kk6+EYpM3preHr7BnhrZ3tyMI5vffghRASn04LeBuQ9l5pospbFZc+bl+WopLlTygyrlOJXC5bTggPMeDGQFNVBv9Eiwus9R9qo7bQcDCzWESbQFcK0o0dkEFAsdXKeZQqveforRCagcN3Je6hEdKiKI1i6Myw7NuggAtdJQwEH4IAtwOxw/K3vQUfp8IxU8uJ9wLSb8uzPWeYZ67Z5XaBTvD+iQGglgT0NrY7QlNNaCva2Ac3onTSmvq9RH5iiu0A3+FbYeVkdoIl0ZN/tHiVzA3Wqg2+pQtXggfp+5If2yGDG9j9nt3bm7hU2V9r9XO90aNGACKHVYRFJ459TZUqitUehkyWcRx7pYAawRZ9sXatHoNve3EK3PeoC7OtqxifSmNugn4ICVOuZ2buG0d16h6ii7epjNdCjyetWhQYdzNjrLo8pC8M94HLZWOpIb53pSRDB6tEUM8QQzhBDhS4uN9xgAMzJ5OmuKMQhcIO38O0pU8hptPe8lxhANthT/0sNx1udxJreazfn1FQ8ggoUqQGQs7cGgUVqdyiw03A1BOdSfN33FfNyDtRpKNC7uGwRrwV3HQbA3tborwsI0Iacoz6ye29ZiTNlK1fEkaS7WmzAUlQwT8XZXg/nVU1npTE6qs0B+1zOFEO2ruqtskpxGAgNXYPniTx82xs5jc0tfhtALV0VlfxezSExFbHjPCxK339N/OhowAQyN/lBdydSZaTb5joe5+nAg20gnB6uK1K3GUEApy0RiNIJZ2dPMDItmPGD1iLCeHBaG0M4RD+NRj2rKuGOvGQg8sq6yzESeI88fzT6+HPO3bL5l/Q9Tbz2+N5312fn+i4ii85BeTAOBDMsnKGy2mFR/okFr/YwAIwKJaKC3a/OjAvE0BBlGCQ8+FANRkNHDSGXm79LQ6EYnqMQgiRo8v9g8n0Uwov/qVbQjKlhlKqlayib2NLDgmCCMV3VWB0q22GzUEAnY21YJUiCk54o00E1mAziPl5hrDhgDvsDqcDrB5hW4+OSfIgHhxO+jKNhvVP6ex4FHQRkoFSMaOCNVCAbgwaDtXuObgIy0GPEgpES0tiYPyMdtLK5F5XwAl7TokNIBEMX20uTsMPzBxzqbGjESd44PUZzpDDidjT26PGNFRMK7z7GIYqOHgICQKSB2QRKpAIpDDFRxGiB6b8UMlxB+xoRAFmDw3nEt0ILo/e4dQWkGJCMKq7Xq0XgHFjitq2go6G1bdBAKehthlQ26y1YltkMKRGUaUMVQdOGtW1ovWGSCbpuaNqwbbcYW6RMBQ12iCYgAFU3DG3/VLunLTt9d6DoSNEip6muPNdSgWqpr0xvhJTDvokg+tfls6oHB8GIDoeV5fdbOuaon0F1hUps7XiGhkPkaADGfgrPgqS/Dc45DFIA8Nq97hEvKtKQ4eDhOwWApyIWGc8lSVZ/X1PAIpKjBqr4AjErStWmV1As2ssVT8clmG0oGBYRKE6DlAzkJBJyxPmrEmlSDkYSoz7da83FDRtmCBRUa+eRGK+UHgYIFZqx71TUjspMOMHEeITNj7Sqw8h0RajHZwgeGKlpab+Fssb3ODcw5xKGWNCIBwOARyzHc2z/Pa3YI2Rc291TD22/nC/WCmiPcgUT19wJox/bshG1KUWgUoaSPUjeoviUtf6Mno6G+FpUp00UowG2coFq9CAWSBhptuS+ei43DjquWajxbgUSrxj3ZEc3DSgzohQoBR1DRpLGxTePpDxP5mhq2uJ+Qz41FaukM7vvLfYfYg6BWiv23Xrbsqa8a8QdY2yzO+gGMfBBGmfYjIAJVRDjhQh29Mg+MCPZaLrAW6YQ4VsAqaYrlGqOXOhwSoCo2WXwPbbu0HY0JkLysPRDKG8V5hho0Q9zOFU5D4Vo99Qwr7Gm3uNcgcjcgh70z2IbbrJF4yp2HemdNOxbZ0DB9QdhKxfyzqyH+blJwDDmGyyeVpr2KnZnnEOXTGR3fr6YEqzpS3HyY4sHwWrSORAGG+mDwGVxBe9JMlI86uqgeZzgwUAs47zxe4esp2SM3kcy77NAGJEe4pArRydoTBI5pPLu+mxd33lkUVpw+XtvZxCSwgqhFNHYtqN5M/SKGnUYnfwOAAWqp2KIRTIqGR571vmJM4FVIt2rO4OK6JmncNhFhmrE7U5EF2HTYBniiom/X7pJDwpuiSaHChPEfj5VoCiYQsmRkOrZY67SE2Og0MiLyzWsyDeyMNwG7Ae3CCA9YLOPz4hkksHkirix6ik7roDSg1+QaggEh/UCEgACEDUyGXk1mIvkkeDQPNYUTGcUvCcL7FT7Gekc/oYStQlIz9NQFHkx3SynSNBzFQxWHSRCSEsa4+VsWNeq2g7RbCJTBjie0MvH2qgh7MULQPIccx2RAiYU1ZSZ+XTCMi+QIritK/ZtC4ZfpwroAHrhaIvTyzRX9NYD5a6WChooIbiYHrpt7tW2uqvWGqZpwjxP2LYN+7bj8dUjAODp7VPyXNt6aK9QBwoJBa4Izqez75GitR3ztKJLx9YWU8S64Pb2E6ArdLKoxLruWKRBb4BWxV5N/GM6Q9GgvaHCo21wyPx9Pwi6bJwNzz8Fqis4jr7qX3IasV3dNjNypRTMbCreRzP47Hl9UcBSQU6CdNzjRpGMr8v4itPJHRvwvRrRcBf0dUrz0nj+wWlxeDfS52LAW4lvwxWZyO549ky7Cgymn3xdszEVipq4QUpFV90gMUQ9Rr+5J/kdIoZkqtqPwDwY+k9NtXjW79bqHwFAUxr0SN02AyZ0dS1pG/wed5YUqS7TPM2Uu6P6PMtF0kbGxg+FkA3mLRLaLYOkEt7f7zX4VVRY5NEibZ3DHiST+Ov9+3uHOQHgirTtMFBSBkTsbzdFPPhnMn3vFTzDCbc9zWAsapG1OtGpwbPRfJ2MCqzfqYTOnqMNlAEiJr+qG+KMyCZS9fs8gp34bydlUL4qYs+D35sCMWq6gOAJYUwXAguN7zMdn/Vf91GZeBbSPFxfqKVEew8RwTTPxr99HlaP3rHvlpY6FWvbIyJYltkNTAkeXao5yi36qzjNs0Xe3JAKxHeIg0MZPZgBaLveGufqLcCq8ZRG2hFzpEmxpvSlumxjVNet57l63arGxpBEwyCA17cTSIj0K16ORLoD4PqfeOQ9k5ha9DshcEYdvBtxBJSyDJKCyXmijbs5Mngdv4tCOh2JsDkxuc2zgXjeaazzefGvG2J2pmQ4fDxyzcCFgjpRymxThP5IvtiVWQhZN+JzNerNM1/ONZ9GzyX+JU/NWmAA4QUDhBN7DCvGnHWtAdCjB5rPe/TSdR+RPPzNz5aEu34Y0i+15nh3fbau7yIN9c6D4QLABHs6IH44OgBRUwDpN3HTchAr0n/8X7PT6K1O3ufi73FmFFEiHecijNfEisIrE4MdAkloGNiEQhBQuIO/57vib66AZQ9R/OAzTsaB40c8G9uzJeAgXYcxI8+NV57/xAgyk3vx3PKd/neuW/JjDUPIvyBg2tKoC8lXVkcjChCTYd2oIGRMKCvJQLxT+pmmEdoOyISPqxXLf8esGcWskYajg0ZFABcOUZ/KBdfxPFUEuqvU6ukg3GczPO6FCo1QrmcIR41fYvwatDo8jkyNjVFRsXTFika98dbhsWOaCOds7b5MgXBxYoX+UEyTPbTt5sSZp2qpU23HsJ01IpX5CHD/mMaYazBa60B4lhWWCapQ3aC7usddojZOATMYBQbYoIJ5FkhVb4uhMGeP14eoQrQFHQ3HQaL1e5rnfjw7DBL/DEVBxn6QnpwOetPDw8Mwe+GM5ej53R8Saxg8kv/K3fPtHCX60uMEKdxfrFHR7J4is7sbR+gMg6YPTq1wnmjQJ4EgqESUiIjz1LkDLzi8eNbDQYUBqEYFA03ai97ff8djwngasiTYuViUmjfbO0YLJYmWIMyjyC9JjB5pP/KyHn/1h94rTlQoEeuT+X/MLmxMHjIFI+Pxdn22FM+GnLo5uEj22l6+d0haZ0OaVozv5fN8b0TCOTl2aDyHfIGRP873yMcp98f44M+luihweVPEAETkGDkRZcrxkS4Zqcp0Trl+j5Kdf6O8GzV5zrs45+NWQMEazXT+UpkGn50dmiZX3aBtTK8bBneAmjl/L/4ZUaQprygDxW96RmZ8N+AgIjQuGDWM0R3ojROnriJ3/z/UvnON1T7P7UZ4FhiNG/LO6dLXkcRhMvnYZ5ngX3nPOeKsf9BRN3xI/mBNByQLgeNR9k9tjEx57YfMqCP/RTyezx8AWfyOvPSOYOt5sTXoh/rDGJM5Srjulpqatoo8+G7vc3rn8XSO9QidpA9OR8PfhXcax5E84vm+93lu/Pv4fJQGjTKE43cOTv872fiSI+bd9dm7vmNj8XxaDgQXvBSDVDLKFVEKJ5kQqkS4iyvQNfLrJR3c7uh0w1sIAEyhc+EV0kwDYcu+y9Fk0nSjQQRIHkemz0j6nb8o0fHSHOPe+NNQLA4s2s+OMMVMBOglmPThCz48e78M6RA99pJJpyUMxaj1T7VmLyms5mnlMCUsLxo4MYC0jupSJnLug6toGI1jTIn5AiFUAYGUrHiO5wwGFAsSO0bF/SVAjyNjkeH99uflVgaQYeza+53GiuUoEbkvbwSFaxFL++naQ2l5ppwr0/RIozaWQ43qcYlAmHzOgvWzFCjrtkGK1XbVySw4Jr9MdcK8WK9Ce5ZEX731drMG9KVaio0jQwKCtlp9xTzNjuyrliZY1F84CxYAACAASURBVL3RLiTQUQugRaDdmpsTEKLtu4tOQLWhbQTAAfZtdWMBaX3UlcXiq9/RSkcvhvRZIKgAzmcb75d+8Ifwo3/wD+EHf9MP4X/+n/4H/KHf989hXQHP1TLDrVuf0y//1t+Gf+lf/iM4nc74mz//c/ixP/D78K0PPwRE8Jt/5MuHv/2RP/j78eEH30RXxW/+kd+CP/Bj/2r63u/HB9/8Jqappn1SiHSwI0JDwVEO3m3o0IbvDFOux6c4b5ymeL4PtKUjcpgF6n2U5rmAZr3WMUJoxp1zzT7ezoENB1h6V+JhwEBSPtyHcTpTknrwBY7fGlBTaRTkNM9YS021Owdt3GmO9zkvNIgVpLcelU47NxLzF4eZ4ViLiGdajLkzVev5NSKDIyqbUo45CmcezB5gZJ/ZAKwVjobcMtaIaTCFaaOKRBs29+C73FuqjMkiEN/r+/2p8V2bSI/DirHu/o0SP4/nKig2UlSpACOlffB5W0Y5Gn8ilu7Y+fug9Wh1le4vvjdHesWhXy7PiWWNcC7DWKl5b/yf2L4Yq+JQL+XGjwUnM/TTuCijgDunDdwoVUM5jroyP+e9D0MwXIPen0/BlP9uyMhSQJz2aWJJx8h24NnnOorA0WV7vHPUVprKwzpN0mIpBbvX7fL3iCr6JvEoHjKA/OdCxcj1HEtgklCShn4wFpA8LKNt8nfLculxzhWwNYl1F0OyRh+6iL9L/byJcKzJCVCs/6U5DArydhdwbWNyxrMOGAXEB7jPfPIEezq1ddDr0JFoxEkQKNelluH04ssEY/3UGXZuSxZ1o3TSaVoDpXF4rDOUu7N1b5odpVZejHRO+feDcyQpzukc2DwKIIP/Znn2zNEOHPo9vrs+W5d8p9b8b/yBL6kIe8LQa5mEjIwiXDu4frCDkciAKwYANVjkri2Ep9VX9NEkPLwsBOeAvZv3qwFSqMJD/OkAwolRNbx1BFkwJnicXxa0hlo1nmNgMj2ETm5umpWv42Eb0YpDlIK8QyQ8sLxfU2NsCiBQgI+XQXsDUx0Hcz8yBhEMdLkQqPbyMKPcUEEIziF1R+Tg+VrF+t4ZixJG3lBKcopbdnBlr3soCgVDYGHQV2ZzQ8GhMs45OWOnfhDj79GD0FKCZCA+5rn40/l5a/tQbnV4QrNirq7I3ceUDg4MN7oZ8QylvpRRqxIMU/281PE7FMt8wvl8wrreQslZZkOTfPvmE5zO52ha3XrHfJrx1378N4DNx9magx7u7/9PfgFf+EvfQtsbPvyHvohf+Md+TRi/4bTxcf+WH/0/0XpH23f89Fe/hOv3n4fSNggB7/+lD/F3/bm/CQC4/doH/OxXvxR7qMo0bFOVfuDf+r/x/t9asbWG6z/xw3j/d3wJX3796/C7vvBl/JP/x5+IlLHz/3vD3/Nv/ix0v0JE8Of+l/8e/8zXfwI/+dHX8aO/7h/FD5y/D1/5qT8JAPjaD/3b+Nf+xa/ir3/tr+Ef/2P/Ar73t/8GfOWn/hQUir/6D/w7+L1f/wn8Zf/el792wh/7sT+MAuBnvvr34vprzzGPH/zK33ClQQ90Omjq+POhsf3dPZ92vRQZpKKiOlKWs4Ioac17Z6r9OPNZOcjProVgG+NeRiMO5yopSvbkFG3lO2BOFCHPD4fM+E4wHAFaquWkIs75i/M0UV8/TwUkWZVkFBhPRsiP4rFq1kRmr7gp5cyCSfQMGj4jynV/DSVGES+Vo+KkaE7KjF7aeaZBwPtGA2uxmkCub6ehMlpV2OOcjyXaOMjAJPMi1b4/TyVmtJhtPxSjB6c5ygbvo1KtOqIcofclFFADSaI8tR+6C9HuDobA/6CAG0IH2rrzkzsadZ6jMpRpgg6Foe33bzoksvTsmE5Ksesb92f2/kiq00OQ29B1X/zuMGxwdx40grRET52m6gbRaDdx3Qygqjj91WrtRax1RkdrtkdEX97ZqqFY7amq0XrrHXuzEp5ai/fdtH1Q72N5ny3QY498fsVSRq/XGxSKZZl8PC3aMfGsa79bOCTaNPQl9N6x7TvmaXaU0JFKSSMmO0lfMiJ6N2NRwwah/jVMKnVgGcphIhdDhnEPv5t02luzFkcvtPEgqZI2yGMzaIydHxn04dk13NtMJ9UXWH3thiO22GeJNTYHXTLQoTJQmNPz7usF/ZQHvzvwCZZ+uQO67d7eKNczcj53h+EleXXkD/dySJBD3tlQBkaWmjxzCsjhHHIPe+/45tvjer67fuUv3T6UX/qu4/UdRxZv65ryjMmAh+FWMJqEK3TUoWky2CpTbYyplIzCaZIBUFfq/T57yzDQ4p1qh79O04Ghx8FSHBiIPS5H4koYL9nDquSq8WZnQC7s/Mv+56OBxm9ETTfvrfzgmKwQRqCHCtlifZyqY2oJXBlArQeDhO+Ru59FejBPgMLQ/6OC7KIfuijXgQLmZWX5MId4Ldd5GB55xiOVAjh2D1KEAQ03Or+Nzi1JqYRQQEoobgjl2tMWEzcrAoug6XEvgp7jPlek/K5IkaXxA4yakSCaoWCH0nZ4j6VzjoQbE3SllogsNG3DWAzIbk87EiD74UoCvKGklSL42h//jYAqPvgd/zH++M/8efyuL34ZX5xe44/+zH+A/+IbfwVdFb/p7/9h/J6v/PM4/er3sX1hxo//7H+I/+bD/xXfv3wv/rvf9uP4937+v8Tv/PyP4C/8I38eH3zjG/infu9XcP2eCeU04av/15/GX/zof8evP/8a/Ikf/KfxvfPnoH/fij/9//wb+Mm/+BegUHzwO/8j/NGf/rP43d/z2/HF+TX+lZ/+9/Gff+OvoCuwNsXP/eInuF0V3/xbP4ftGyu+dPq7PQXd0AxNZncoVpRS8EM//MO49Q0/+fHXASj+zM//V/jf/sE/ha/81E/gt77+Em7bDX/9a38VqsCf/R//U/y3v/s/wz/7U3/S/tY3/OWPvg4I8Gd+/r/G3/iH/1386z/2h6HaDjRtSh+ihilLtxxBeWZI+X8ZQQtj40BfSQn5Nlc4FdhKA4PW+dxsOPLZI/KV+Zl9y24f/IPfZ5rVvaMpLhnz9RcNOtPhUEvup4MfoRzWh8qS8qT4OA7MJ15sH43zdQDHSuAWY9zPI7nk75/KSsjj7r4okBTNw2Et+d9CW8gVI5FRfJcVJNaRqxvZKmM9c6TCVyRZL8NBEOuqwxQzLdn/1cTlRQ4lAUx5EzcES5dYFwoEgQYKqwAuj2S0piDdKdDdoRkZHJQSMtR7rj0Svdr9lG//H3tvHmzdntb1fZ7fsNbae59z3um+75369ty3G7qbiwwtaoFkKGVSFINapkhVEqOFMRBjjFEgWGriQCcMGQqETkQspUJapSFYDGLACAaKBpqp277Q4x3f6Zz3nLP3Xmv9hvzx/H5rrf3e1j+6yqT/6HXvW2daew2/4Rm/z/cp96l1z4vPVQf6YZZEU8ZL2Xhzab8g5foyuRQPB02W8zbPLxNp1WH/xemRi1NQP3s4X1LvWz9YvjeiWaOlUzL1RWTWi7W/Ye1BV/XHFAQo20wdSn2zw96BMs0HKGReA5d52v91bRpKT9pc5FZKZW3PwaIZlfBQtngRqJrHtOrOhfM+r8i6A8rY5sVPdWwP5eDyHqaSi+UyWMzBDEGmGkJAAzBlsma5uJz3YlPVcVn+ZSqUlUk/12TslCEtz5gXK2DuA12vs9AJZraNq6Mpi2stlsq8FnKdh3nsPqGF9VDQaBrzQ5G5+H4h/xd6YTlGD9tyBxnJ+rnFR5fXrG+91IHlN/UmB/Z4fV+VTbPDC9Wu/7Sz+Kl4fNLOYuscVXHVpOHkLFH2d4Jsy0YIFXNeN1icjYasWzAWyAZmVmDWWFIcpr5v9TM1OqSCizn7GEfdmHmGAVVnaarrmhTDgqDB2AJJqs+kwre2J5ib2ArOuoO9ODuLs8ZYyik5+K4IVpmNvNqnRxbnqsqehXVeXuEgIlQNgFk5HijmOsZQ2iXUOhNmSTT1UkPhDdMYVOE7y4r5uZWKu47Jw4cK8zou5dpLWVmVgnmlYVcj3LXP16y4Mzx0q/lz+k01yGKKk8FQlc3UT62MUS4XmIZiITDTQ9nG/IoxmRdKyqaMTY1YLgatzEV1KO0EaTYHRkp+qPGtOiimBFeWylHX5TD0U9QzZ4WtGtGshUZmw2R0VEcD4OaP3ubPv+ureerVr+F/etf38eC/+BGGfeDPfuc38Rf+zJ/m7p07XL9xne/63u/nP/y6H+bo6AE33nPC7jt/jv/qx/8yArzr772bb/sbf5Vf+eVfwhih7Treen7Bd/2d/4b3fMcP8MM/+G5e97o38D9+z/fy73/Vl3P3+XsAbL7/t/j6//2v8lmf87n8zf/uW/j5r/xOXr635SNDbY8B8m2/Tv42If0HT5G+7LWMX/Mz3LjRcXLccrTyJOOIOfL4409w/xc+xGd83a9PRpr7Wfjt3/gcn/OOJ7nzlR+fI8w/9mHcX4F3fOPH+dzPf5L7f/BDfMbX/8a0dszPGq5evcrp6T1e+y3PYsTw/u94m87XRNQgE654GSWv8zzX8uSpd+pkrxdFPk9vjcjOhhVpDpzU5Vxbz9RlNEElF8ZYLnvnFZnAIg+WNoJ+myaZqMs0zVeSV+7D5f2md2fOSkshzMo5Up3PakxVB0LvWinWq1U0Z2qXKKaZqCuVcS3hxnnDHmTgdD+X8Sr7UmuSZ8r8Q8dyHo/JnCnPU1znOatXMxd1LCYxXy3zQ2ekwihzzlOmcQ4ssvh8aQ9hDSnVoALTM1Sz+xWhxPKjOTDia683JoNdkMIrt1h0tfdiuU9eLLQq0ydU8FIXLRyBiTCt6tqEkiWB6v5F8C8dyMJMzlXOZmpd5jSDNSpkZC6frJnYgoKoLI8xzo3j64rUR6ow/uWYHRqv9bw6drnMqzA7wNMQLeZ8Ws/28FpS4MQCpR1LrTnUvRpLb2aY0RwxKlGXiEykf4eGdJ6CU/VurjLh5qpH5ucwZX0rUZCiiypC5RV6ue4TY0oJTukdmUrrCgpIu64ro8zuNbNZL1Fi+BPBTy33mdfnJJmoitaUBablD3pehIlgZyaeq9dQ0sLqZ6eFjVDXOMjEsjztifLfMkJf2d+Vgbpq+9mezKLN4qcVlZLq0QUSItdrwEHgQpAis1heeUbXUTOLJZBSZHXNLEoWxDKtxaXTXcuLdHwOIaWz/CvrxBw6Z7PdwZTFW2YkhaxB8voeVSwz33PxkgdrrgaOqGu3/G76zMM25EPHgcwspF4HGdxPH5+SxyftLDatP4iCzTb7HAXWRaERsBjTBF/hFUJMt1kwh31fKIq7CocaDZ13dZFFAtUiyHmRIKvRl8pm9nB0R5ZfZvLlnKtCo2yqxXtl7c9UC/vr6+RyhWp0TZtQalR6shQmxQhMjVqrwbc0y6YKoHxoNuhwzU2lxRQFkmfDh3lIpkOfeVbakzFT2NuQ+vP8LPOdZye0EhhNZBcPzeck9CprbLnvwbwdGNRLEcnBWmIhWJCHzywrT+qFZhUyodjqXKRZORxkexaQlXpnVYivhO9Uo7q++2QILNckUtqpTKNW4GblaScKehWS09CZSexPY6g6d9lqQQ1n1UM1GLIcjKy9raC0U9F7vur/eFFtgy+EH/2hf4QIfPxjH+GDH3g/b3v72wlD4Iknn+RvfPv/fPDcT77qKc4fnNHv9/z0T/zYVPvzi7/wc/ypP/Pn+Kmf+DH+n5/953zoNz/I5mjDG59+Cz/ynn+IZPjwh36TZ//l+3nr25/hp3/6nwLwT3/i/wRJ/OIvvpdbjz7KZZ85346MISNGq+EqXKf2grwceu6cG8acaZyhdaW2pijjqriXyqcGTyq8qx6aeZXyt0MYDDIbdPMcFBa++sMUkJnXSv0yU+Mz7cOD6DvL68q8d+ueN3l5uQlmODllZS6Xz6ysl9OEzQb04lGmrEIdo6l30VJulTMXa3gSkQ/vbR42RKsR9ZClXe9djpm2adH5bHouqrU0B72kQCEnuNLBi+u/tPj60P3qy0x4hfKief7TPF51Puv9ZT7xUK/NsnCRrntIbs/vMYmlaVyWc/7Qul1awgdy+1CIf8LA3OTwFPlfxkfK99VAfIXeXTza5Asv9eOk3x5eAyU7Y0R7J8ocMK49ZtVerBJr8V617QZAqoGQvFiHh7vlYFxYZJbqNwsjvj7/4R4Wlm1Epimfh2laQzLd6PDzrxi1xSnTLsjVOZrPWbJ6L69HnmXRgeEsc5B8ztqotq4Bq1w8CFvRFtqNfRpnodbE8glgo0wPPs2PVFljJxkyoX2q/i3vk0VKUMtM717368HwLByb5XtPLKN1/xVt/vBTzrbHPMAHvsu0lg7noL7fJ5ixaa4nWVd/O7G0Vjl5WJO4fKbltWb7Fg4o0Q9OeugB8nzng8ChmZ5GERoT8c4khKf5r8fU/moS7tPlpk9OQbBJfzxsZeXFcy7k1cFYzz8czNNCTHzCdyVPa/nAcnposj+BL/np41P0+KSdRd/MeG5rbKF4n7HVtkIjS8hFI4KQs5SIHAsjQK9TIznzQlfh5aybHNOY4pQhms0PhXfksnGsMSWDo4cUJfMQnULxFSrDJIU1SjdvJbvIpeFqzVghWkehDWWLw1UiNNZo78kZorjMium756REKdq8eFGHslAuczRXlUeqBSCL2iVtsKvKclaO+ZUbuv4+g2RDTqJU9sXgLi2U5jGPC8HFQmnlQxazes5yrh4+Jrp/ZoU4G2i5CNxXGiOJEmFeEvtMzgGTcFK5WPOv+jQ10u3sPNs5a4PpqSVLeVx9lrmXVzU6sjAxQVQokBMzzYmIlIx5nq5fDys1IlkDHeUe2c0ZFJGiJOfgRcrzvlk+90NWaP0LNdq4zLBAnloPVN2VEda/tJsMh8YbVq0v1OKCsxbbGj78m8/y5/+zPzFJ97ouH33scfb7HV0JDsUY+e5vfyevfcMbeeZzPp+/9NfeyQ/8ve/jJ3/8H0+zR3XE0TXtc6E1t5kAPP/yGQB3zgN91PGYGOrU4l2s48jZxY6Lyx3jELhxdcNj11fcvXObxx5/YjJWTq5eJZN5cHbKiy88z6OPP67XNYYr5W/nD8546cUXefSxx8uYzZ87f/AAY1wZu4WDaaeVNs2JlM3xr4K32XJCDfLkYiQsDeCDHnYle5ZIxfhman1S22TUc/9V+rlYfMXQrHVss/NTDT5j/CLAp7I414zFxCIiTHDumhla3GpJNCNEMKb0Wqy1YyCFXEEdFlN6LdZnypNhNtk5k7OfYUHMMP/toX2RIdeNbPPkN6g/sjS+inMvhzqnwkahZCIXRuLixsU5qbqoBjsfHvzpUjrWExwUDrJ9Fa5eKf1r6wOZz6XoqSkDVksSDrJPeizrmPScNDtri1fRxzcLW3NhEU6G6MKQFFi+5NyCpv59ZhPV66p8zTmRohzADXMl6gFimaCKvJjWlCyeRaToXA6N9joHpq7xJa1RMaQX+mnphGlSsspq5msuvtag9GIADp9tEWBWZyoBCS1lqQ7qUh8I1pW2BRzOVS4OyhLxMQeQWFxDx1R3xDyhNTAlKU+MnpXERtVYLcWI07XrO6gZIVOcpbagMMYWmVEyumKmnsza4qL2Sk1TDXBOJXP6EKfEMoBXWxbZ0rbLpHjg9GRTWLSn51yMR1pk0A4cNxVV2vpXqIi2eq4SxtQgwWwbzSzA9TeZ2vO6ogmWe/HQQZRiYzLtmYowWcqyPDnE85hPNgwszN3DRMscIMgTxHpeDkWf1GdGSp/SWR4cQIeLbVKz8Afn1L1dbMwqsyZ5ZziYy1c4yYsRnb5btnSbRnb5tU6afmsW62VGLRzO4aePT73jk3YWQwhFAEphQxP60sw651z6+dRmq0XW5tIXqhjvQhGc5TqY0p+KeSOICOtVq4X4KSrMVCzWe7xxCtHI0BcmRlCDu7JDigjeeaw5bExaDTaRYqDnTIoBkYS1lrZpsUYIcYSccJWWWoQxjMrOlTV2qjWVGW+1N54xS8Ws752jCuoYozqnzs5Oj9pTE0vegXOXa01DGZMJjqOZlxgPe+8scfP14qlu/qCCrB+HAgeRKYMmVTAvBfnCGapKyFopWa7aTuBAJBwcswJdKu5Dp7MqpmU6rta1VhlWI2NiDNaZCSqbyVM7kYdDVN54Uk4KoaGK2sM50YsWt03mc6YeSZMFmtXgifoVIxix6oAWQoL6PsYkUqh1KQZjLdb6xTjWNao8jpX8YIyD3tuaxbnlMQs7Xf38VJsIE+TRVQbWQlwxQQBLgCOUIvrf+xV/kH/49/8ONx99nDe88c381r98Pzllnnzq1XzeO34Hv/a+XyQleOOb38wH3/9+nPcIgveN1tuQefLVr+FjH/4tPvTss3SrNW/5jLfyQ//wB3j2A7/Bl37FV/IjP/SPePVrX88bn34zz37gN2i97kXnOmJ2nD0YykQLNbKbABNrrXOaIE5kwZhIAu6cj2zHnqceu8KHnv0Abdfy9s/+bH75ve/lK7/qq/nJH/tRcs68/9d+hbZtedvbn+F9v/Refv9XfTX/5Ef/MSEGfu1Xfln/9lnP8L5ffC9f+Yf+MD/5Yz9GNfhS0l501/7FfTR7H8rasiCvXLtzJmqGJE6R+YM9AA+r2+n8+ptq91INYjDWTXNfofUz3G2+rt7Xav2WAISFobA0/Os7mIluXw0LiCFO5xgzIzyqTK+Qo1zfS8A5M2WclVhBU9rKiljWuOi/mFk80+Gh76V7ehlQms5ckvyUgEL1D0Sk1HApdDVTZETKpIU+wcjkqAj181Wemblx+GKOp3uWsX+4bdByVhfRgPJjlSfFIUzL9TDrt6ojJ5CYmSG7tW3DMhM9OwUy304q1G3xXGWyEkvHaxpa/Tkvna780JqRhdOWp2dP+SFEAwZVofM+qGulwrPr86vQStN7VQbQnPKk26Z2RQtdUeAuC5IOWby/fl+JQiifXe7RyYGtc7v0ppmhmwtvsrzzjIgSqdDqudZSF5NM6+OVS2F2Aif5nyBLmpP81RZ6yLCvDsOSCKoKhhzijAaSSXQAc13kKwzvSV7pA1ZdMjsRCyKVhYNdkRoSNUCeRYPXaWEzTDbC9CB13upz5fr/ga6en3nRxmp+zeIUpsIoO6tjWMBBF07QdOlJoNaveXFe/VV+ZQahPlR+yP/RgZjlrixk2UKuyXSJshdl/llvWwOIM6IsU8ZtchyFg7tPr6HvUHbqgXxi8TmVfa9krU7Mz3twFAP04bU6O3jznlhu++VarQRHVT5JOf/gesWGXI5jvc/DgbBPH596xyfNhvpFX/D5OQzDtAkStQGxFCp/jSSFPmJtZTUDaz05w9npuSoLa2iaBhEYw4h3HlMa6RojOOfY7nekwujkfYOIMAzDBLdIMRKjRmmdc1hjsYV9qSq76nQBHB8dkXOiNmGuVDwiQj/uirD1GHE4r1ExVSbqsHbdClMc2xiVgy/mSEqGvh8ZhzgFcLwX2sZx5XhD2zq8deq8pVya4BaxIlkbrkMxkB2+wFSsVccy10bsxfARAWfVKa+5qqmZtfZEgJzpQ1CDb1QnNxajZRxGQkzsowrrmBJjP6jDXiLN4xiLbnZFiM91UNrYVwV5irnqc52TXGoGq8NnDGC0NkCWcONZsVXYWUoB6yzrttUsRulVNaTIkBJ5LELPGiSOJEmY7DE4ogUkYHAI2tzXGFMi3LMhjiRsbiAnYslOWkm6FrKQvSOL0ry7BEOKiDVI1HU1xJGQAzYJI4AYnPVEyXgER4AUSMbhujVOwOQRnAHToOX4iSYVinvnyKU1hEmFszWrMRdSr4ZFEojqsFnrMDRE0VYWOUHO/ZQxFBoy2nPu+c8+AjPyT775Pfz9v/23+Nx3/E6OTq7w/d/7Pfz8z/wzRBKvf+Ob+WP/0deyOT7GO8ftF1/kW//qN3Hr0Rt80zu/k6/9mq8kZjDZ8Wf+4l/i0cefJKbI9uKCd33HO7l79y5PPPkYX/Mnv47jk6uklPjB7/8+3vdLPw+h47vf/W5+z7/9Rfzasx/n9r1zhovbnFx/iouLfUHxBMjw2te9hn/2kz/Cer2i61ru3Tvlm//yX+d/+9t/l9/35V/C7/99X8Y7/9o7ef2r1nzmZzzNf/pnv4Gm7Xj5hRf47//bb+LBvbsAvPEz38af/i+/gaZteemF5/mbf/mbOD27DwJv+cy38fV/7htompYXX3iOv/LNf4Hz0weMwxZw2pvSoNnkXNau2GK4DJq9yVrLHEtza4wjS2EmLAZBHNV4bbyfVL8pTswEmxchRw3oGGu0z2UGimEWK8kDGhkHYQiV1TCSScSw1w2ZVO5ZAyk5xNjCQBoRowiJISYkRlIOIAYjjthbjBFOjjvECJfbC2IYMd6o0V0bXqeMLQE+22pz8HG7ZyTT+g5vDdYYYk6M/QjZlISnVu74HMhkkvEKYLAZZ4AgxElsCc4IsS8U/E4lW/mk7t+kTeydCNYbQk4ql0rNG2NSJ8gYstF9llD6/alusjJMSkO0asKpDLBQnI4Qowb/nCGljM2WVK5lxKqTHHt1QhGSFMczJryb0TApRZX1TuUfSeW7mFAM+NIARAxjKC6mUcdbxanDFJxkinNgtWbzlJFVkGxK5kcN9xSlxu01Q54rhJrJEU85KROz1SDTlG1YHNYowVyOSfVCceopxjy5wBkzxKSomymgWEiTNJCgwVbVf2nhZORiQ1Bq6YSUlTVcjCXJocOHgJla8WRyYVA3xun4iOqpmgnKuQQCJEOI2GyIZLKzmGSQmIlW599FQxLVCcl4MqMGO6gZdSEGLa/Bqm5MCZxAkqaMseoRmzNZPJFITqVlmCTGSGnhkMu+Li9l5RWtA9RuqutWS2C0li/qfYxgjC3yv2T9xABRGU2LvUXlAM8WbRM2owik6K8JwSSUtadzkbM6Ybk4IjrGUurkdE9OiDLnVC6Mg7KxFmfDlCBmZbet86hIBDsxPN8aOAAAIABJREFUtOrSjEXiVV9rzmNXWTgFU/MUepnGUQPEdV9RMpQJ50r2NBbGfSOTbZFqgK+MR5zuVOs5SgCPGXERi7OdUmLeacV5Ejc5xlK8aClII/17vY5+Nk/vVvdpnpx7wRCZbaQaHLPWEEKcbcIMpDizvVYeBNEg11zSUIIQE0Npmmy0CQ1WIVwpT3a0ZvMXnjSa3k0Lr3iyV5dBI2PmLnVTHagprYMOnV4RePnByKePf7PHJ8OG+kk7i1/4uc/knIqwsrWYXYoDBnVBxdCjWqXGdyo9cCaEQMxj2QTq5MW0YCQsCy1X5QK0viNn6Id9YcBSRWScU+dT1FiyJhOKUDAFj5+imhzr9VpfPs0R/JQDvvElS5gIQSuojFFDqd/vSUmNgLbTLEss0b2hH0GEiGHfD4y9EvU4b1m16vwW35mj9QZrwHm1ClMxkp3z04byjceI4cGDc9q2Yb1ek2Jkt99hjFJkVyKIFMuzlpC3c6r4Q8z0/Ug/jFxcjqSU8Y3QOou34K3D+epQaSF7EkNMEMNYskggonDZmBS6EsPAVEspCtsTsaRYmTpNcfgSMY0TzCpGpUWu2UhrNXtRo4nOOB0gEWIOOGNprNb1RSo7YGaMAW9XxXkP6tzVZtZZCNkQUlaD3gSG7Y4xaCb76PiIxnmcJFIaMa7BJhDviCljBRqXyUYYszpuxISkSBLLEEcMGYP+LcQRMY627cjAbreDnHFGaBrNZidUMRir0C2TKZBQDVQ0vkT8rZ3IQhrjICfGGMjZkG2E5BQZa2rGxmphfjEaSJExBzqzIeSeNOZinEV+4i/eIgs89/l/l//4j345wxAQsUiIhW5dIUuSE8Y2ZFMY81Ikx4RzLQppMuQxE8MIHn1P2yAFht54NUrFNgiG/nLL+cU5dtVxsnH85M99kI88f8q2H0oT5xoJlkmJLFMWFZJWDZoamHr1E7f4os99A0e+JyQQ22EwDMOOHMEah9r8I3HUPWZcQ8qZfb8np0DbdnhrSTEypAAipNjQeIP3lhAj55eXhQfClPUby3Or4YcJIKO+Q9Z6x2L+TRFljZjPmYiaJc+lXtU4BxnGcZiMm2rIG2sZavQ+ZcIwKv27cxhraBtf1kLCF1RCxpCzsB+2DIMaIlZMCaxlmpWnNQYZA8Y2uG5FSAOtdxytupLh016eZxcPGFPhtE5qJA4pFEp9zbhs9wNjEpxrsSZzsvJ4J4xBgy7ExOWwY5dGZNSI9+bkiJASY99jMYXJWufeWYMzgm88GRhCDwKN9bpOkyGlQMyBMWacaRl2lwwxEogqa6u7notRTcZ5N2VCNLhdWhTkWjumjr+IwRqH83bO6JYMhouJWFwzrMWJOtBAcUZMAScmXSHFAowpKqzdCsVrLZEDgKRO50RSZkpQoTiC9YfJiCulCTmjzBgLVEmqNuPcSmMytwUUDiqTrjDFWEaUGEukljag+z3rnreFYKW2XjqE9y2SD2X9p1xInibnVDPNtTY+RArU1GqIs+6bYlTXn2sP3Dljz2RYC17lniz6SVayoCJSVL+CTYksGjDMsTxnQfJITvosQBIBY4tzWyCKRh0/KXBTJYnT+wSZx6kVT86WsTgqlUVTsiXmgBQ7IhNI0czzxEzEV1seaKal9FwMi1ZiRQamFHHOUZ1lcnUkSm2wmALnjoAGctU5UmeTGmSombEyp1JTeTljXH1GJqdM+4UqqsyUgLX+USZnpfb9M1JZbDlwDmOMk1MVkyJ/jLFzJmuSkywCG0VumhnWW1dgjDMTshQ5N5ZgT80WV6e6IuHqUaHhukQXjtDk+M1jLnWMTG3xEFVno0ElY2QmsSmfW2yMg8yZru/qOKbp97Gu9er41qycaoNpXHW+Cry57MPp2otgw5TtrbZi5e7gEDb9MIJiqYOr02fMYo5jLmvUlWeMs7s7vbNM8yQiGnhbPOd8faa9Wsf79qedxX/jx/+nrTO6ZlWcxEwIAykHnMtYq0I4BhiGQOdXpBTpVk5x6zkRYsC7bhKQ/dATUmK1OlpEjNLE+JgzE6Q1JhUOx8ctKUVCCGTRDJvCWoqRnzMuKcpeRKMd1rSaNcuFiMYJgkXwxBAZQiT1qiCdgxh7UhpoWoexCWPVyeuHXclkaoZsfaTv0oghrD0UxzcVZVbJKkSEi36vdWK5VUcvRpyxrFaCmEwKkcttTwqJ8+2ephnY9AlbHAljSwYyC3EM5CyaRauZSWNIMbG7GNjudmQyV45WNI3j7P4DLrZ7ovHEHBnCJTkFrh51XLtyRNcYGpuQppsyf7XW1BqHMQ5jFhK81FxoraYagjElxpL1lar4i7BXg7sIXCrkxZRI8cRSQZaGuRGRwRfRPYaRznsutucl+N8wmIQXwTkVVB7PuvNcPfF0OfHqz3uGKzdu0K0NP/6TP8v2co9zjsZtuHe+Y4w9TbxO1xm6VZGHMTOEUdk5ExgrpFHrIdvWE6Kw24N1FpGIpBFvhKvXN2zaI86Gge1+h3MWX2GE1CyBLZlVbU5vnMFbg4lCjgZnLePQ08dAFMG3hpwDrXeEoIrQ5EDblAxxGBEjrDYbVTI2k4cWs6rG9hbvDbnAza6srzA0F1hvuNpuGMeBPgY1lDIgibEHxCFGWQ1zFmzKSMwEIqNz0Apx2ANRYWURYnQEAbFqgEYxtOsTmhN4cHHJndMd213Q7I2EmTV2spzTbCEsICxQjJWyj4YQ8M2aN73mFg8uB852ibbxkDbEMRNyYD/uybFBnPbH7MdBx65psHZFTpGhQBR9u8a7hrb1xBjY73vu3zKEsePohZFu1dG1HSlHYoykklk0Aqu1p3GWYTuoYyKQS9ArxkgICTEW7xzWaebj/OIC33bknNhtL+hWLZ2/xjgOGuyyhr7f0/c9m9YiWJxxdL5lHAeONi2Nt7RNo7WAMWKt5f7lKcMYGGLiuruO88KmW9H3kbPzHRhYdx2MA8eblpQy+6FndXzCumvpRLN1V45OWHdr7mzvs9/vefnejvPtwNmDPSYa+hQ43V4QU+D68RHHvmW1bnBOcFmdkWblGHLAinC1OQEvrGiwkthcaTDG0V8Yhn0gMSBNMeiwhAj7ocdbp86kybS+wdmWOIzs+kuywLprGcOAtU8hOZaMf+ai39e4U4FrC/2uZF+pMGGVQeMw4kuAi+IEPLi8ZD8MWONxzpNzMdrzXmvZjSUlDZCtWscwBi53e2LQrD/ekpOh8Q3eeExKCgogY53QWjWLUskquEYhlP3Qq14RSwyZnApZE0IcoyIdvC11UQIxqzxJ1SAFyeC8n5y6aiS6BbsjAmEM2hZGrVKkwvVTwnuHGE/CkbMagjkWhA7VoLWALb2KE7UfbMoJk3KBI+YJom8QrCsOkwOMpR/GuS5NNPNb220JQralfUQMyltQnHODECQV2v1ETAq5dtaTshCSOgtOLH7qAZJBIsllxGRsVscjGUf2BsmhONN7GjwWDymQTUtiXwJylHoyDYa5HMlVnichxgFaQCxpgGQ0E9dkTyIQ0GBD6xS1IsXZj3FUR6dwM6hRXeTuwnZQwzuTs+BKmcU4jiWjqIH4mIqTajJiraITQqA6sLk4dsrybiZbvW1bFOWjDsewH8gTTaeeM4aBDLqWYiIOJeNk5qyV9w5jLWEcycCo2HO1zawwhjA5DNY4jo9OaNqG/b5nv99rZsoociaUcg5BM+pSZGot9ckiNK0nxsgYEylHUgiz810DcgVyHKLadpUlO6ak7LTWaZ1jVnvHGqOcB2lWT6n43lpnXGrJy3s0boZ/q4+XJy6I6kDpqXk+SSMApGynHtS26D1bSgBSXvYjrbqyZHCLb2tkkQ1EyrqSg2d5RQnQhA4rwdoCH6dmsGV6bMgF/iulRpes9yZDClNQo4bzZ91da181y+hrcGOKPOg9Z761uZfqp49PzeOTdhaxCVMyUmIEk22BeSqspWksYhJOHCnqztFoudOIYdKMlrEO7xOEoIop10VdmsxKwhpt0wGZlAKC0Lim1MK4aUNn0kFDUFuIBmrUVFBITQyhkAzUmphE1/pibCjso3GW5BzjkPDWIs5OaxznsKbAd4y+gwZHIo1zNK4BY9R4MNXnqRCOUCAtJSsrvjTblSkyWwvfm8bNtZcsmBnLcxhRiJR3VqE/aGbBGEO3ivhmg5BZrRu891gi+yEScCQUyjuO45RdSqlAFxLkIZTIW3H40ggIvplB/lMdSFIIsDqxpZaBNAntCkmyYmhK/d0YlZ5aCj03aY7mBQ3twtQXUV/YOUeOmVs3r2KNxeA57wdaZwlRldDN6ydsjhyXtx/w6le/iqff+kbEey4enLLuPHEMhCGziwHbeFrfcNJ2hDCwu9wXwaVQOnJJdqKxQiOaXWlcw8n1FWKFHAdcTnhr8G2DkcSNrmPdwL5XA8R6A1HrPdvO4azFqJlDHzVKa71FrGawXONomhWd95g4cqnYWhBD0zas2gZvDJKFlEatofQrJMA+bdWgNEpI0PkWax05Zf7rP/5H6axh1W60XYvJGCu0ODC2GMxJKbWleMrWY0RojMPkTBwiIQm9BPqdTNHAEBNDH4k5EQZtOo1pyNmwkYYwJkKoEdU5czAfmYPj4Qhn3QQipAjbfYDSiLqxia6Fxja8dG/Lg4stw7inbY5oWpU3YxC8s3jnVcGlqJlaUafBipDTQAhqxL/3P7lFjpnf8Q0fJfmEW6vR3qdQ5FYJMO0D0WViKI20jeCtEERorCc3gvN+gvwESXSNxTptmxCs4AROjldsLxW6asSwXrU4m+nWLYLFiqXxDTl5jo67kk1RjW6ywxhhkxu6rPBjEUfbKKyys5bG2cklj2jWMpuEywpz2u8HXOuxzrLt94QQCHFgv+vp+4EYtMbZt2Ci4aY/xlrLauXpjKdtPWNKjCGWMtSIs7pGu8ZzfNRx1LSMw0Bf4PvioF0ZrO8QicSU6IPWct26dqxZoGQmxmjNRgUabzHWcnzUIGLp1hvSEMhJk3anl2poQyFaw7D3avR4pxlK0agm47DHIpCEkBNZBNc6ttstKRfoX8zYVYvYdjKuTFYYVetb+jBoQKk4RjjBSS2H0L2HEYaxxxjBe1uSipr1aFt1stp2pVkKanZr1mMpqbNYW/potreUHpSMSSWZw9TsnCqfmCJiqv5SOdv4NMHFKEFNXU81N2LLc2gu1ThTMjmhBDQ8gsHYlol339QscZq2dIhhciSq4661Uxaxmh1McQQyMaNIgRLsTQIGC1FlpsIodQ3bQp6nGWF1ipz1JAQbleSr6hOcn94zm+qAaCGAFJihwZVnSAUlrozaOelzaJDBKDIkJ5IpOgEhY3VXmQJjRpR0SQxWMra0mLKLDNFE3kZ5lgy1yfuUKSPP5ZFZf1bYsRL9LUWmsYp5odgkYmAcBhChaRt1cESD7UvmZmssvvF0XTtlzFJMOOPJZY1rC5CM9bY4oooMc+V7K5ptFCO0TYuIsCvyu9Z8AoixWAO+UW4HZxwnx8caWHMJWUmx6dRm2O33Wu9b1h2gxIA5lz6RqhKctXjvCTESQq1R1tlVe0rHz1mtv9fEw6xbtC5bn3fV+skpy6U0B/WjSKbWtS7qbarmmpNpxbGvd5fiF9as4itV2tR/tqZThWluZ8evBDz+NYcs7nNQU1/ekWltzTWF0xiUvTXVRAuYicE+1xTh9Pn5g3HS5VPPR7M8JQMT5GFe2ZlpbHQ/zLf69PGpeXzSzqIxIzlpJMp7g4jH0BZyCmWjbIwQACQxDCODCN61iJgCCVU4onUGYxtCUEhqwmLE0jaelDJN00wQRlMiXyZrJs3ahpQoEf9awF0irUWw+xJpjTGAsRyvNiWao7DUnMA5hUJ0XqM03juMs4zDoELGlCqzpOcbqwu9Rm+NCKlE4mKJ1GYUJ0+ei4vXq06hjcXjq+xZqShhbx2bK+3EbhZL1MhaZRTUnkfK/GqcwhWSgFjLMA5a6+g8q7VgK2GQpn24cvWIK2X8yJBTS8yJy+2eTCRkVUASEiFodtA3DVAzMyPDkBfUzg4xht32Uh3UtsN7j7MWYmZ3udOMrrNUfPtq1RFCIsWEcZbz80uGYWCz2uC9w3pH7PeaqW3U0Br2WrP3yM1HuHnjKm94+nG2l1ue/9jzvMVfo+s8v/mx25xdDFy7uuaxRz3P3e95w1vfwsdO7/PBX/8QD+4+4O1vucX9uxe8cG+kOzniTW+6yf5yz/PP36HfJU42T3By5ZgYehrT0nUdziQuzy/Y9vcJseXk6hWuH7WM/ZaP3rmH91fwKbLbD3z43jljCHz179Ieffd6uNgN9NtzutaDcWyHkRBGbqzX5JCh9WAFGyOERO8M6xs3GWJi//JdVrHldc+8kdPzB8Q+0ceR527f5vz+JSd2TTCqyY6t0Gwc+/2xMsJ6rRtuxOKtZhwffWSF9Q7GxOV+B2I5XnUcr1ucFcYhMqbIkW/p00CfRiRqdNv6Du8FITAMkYshMg5eSZ2skJ1hfx4JYWTIlwiZNFh2Y+Tx4xN2p+e6BlBnIMfZcJr/PXRUzTp9lbIXHO1qjRCwBDqTaSRCFs4vt1zseq5tVty6foRzmvUMx2q8O+sJIeK9ofEa5NkPA/1+z+V2ZL3yNNeOWHUtMURuPnINxLBqDI0VetfhncU6z67v2e56co4ENKiVi5yQlDk+UaREiIE4DkiMrLzj2s1rxCyMMdI36mQebTqEzO3b90n7ns3JhvXJCdc3nRrGCOMwIEYdw2HoiSnirKVrWyQlTDa0TYvvVsQw4g0QB0xrOF6rE3nZb3HrDcM+Yk2i3RwBie3Znn0UQiO8NNyj3yVWRg3gR06O1MlG6FaGzjtOupaU4cUHl6y9JQyR891ItAbvDaHfcu3kKpe7kfOLS9Iw8GC/5/5Zz+kQaL3hxtWWrjWMIWLo6JqO6xuI456rRy3b/cDFkNgNA/fPL9QZImOz4ILhft5jJPOIy+x3IzFoxsGL1n/GFJGk0PzN1WOGccfRyYram84ag3EekiFFhbinDDfZsN/tCDETwsi+H+naDrtqGLcjaYhYlwgkhiBcsSuunFzHWmF7WWq1yv2V/VqzjMNuzxgjyaiRrnDZBEZZiZ1zhcRIP4tobdI4jjTe0bUNw75nGIMa/c5rNshqhiFHzTJntG4rFlRHTBmxDmscrRMkK8LDeUXn5Bj1OsUhGpJmaVNQGOy+D1ingZkYA9ZAt2oU9mgU0ZNSJsSEt65kSPPESh5T0rEsVnW/7yHLVIelgc9MJVqSYm2G0qfQFFRKTEllZs5IY4oey/psxmj5hzGlzCRzeXlJP4641mtGMmeyWAKw3w+0RmgaNVyHvYojs+5gGMhhwHvHmDND2JeATXFu08iQg+q57MjR4Wwkm0RMCnXNJK2DG3cY47UELAsky5A0k+hEEcbWanas9iKtmTAjQog9ZM0wa0ZIHRBnGxWJtZWGlQJN1UOMZo+tdxyt1lMgV2Ggwvn5BcMw0K7X+Ebvb6zFWw8C4xgIQTN5u/2elCPeN8UGiIhx5ALPN6SSKKj1dJGN84VDwhZ7sayfEPBNU2QadOuO87Mz2maN9w3WOsYI/TDQdopeq5DWyt0QY6Tf70il/rHrFAk1jiPDOBJKrbjakqBQaiU6zDnReF+QZkpAt9/tOLu4RIxhve4KKZY6zRmtNXbeM/QDYYwMfdAAo7G6P0OcMnLVQYshsPQMqx9Y7cXJwZzYw2uWr4ZTF32eS9Yz58JxUeRCTqn0lIVcbNP63mqfqY1aa47VSUtUWGscQwkslr+VrH11DNXJLQSJqZQ4kCfvW4wGM1rnJ0bvLMKu70s2vBA95lqmlhcqXQMNE7GURksO0ESfPj61jk++z6LxGm02riy2xLXrG4WV7gcuz/cYLL2PmvUpqesQFWrVNV7r6wqm3VpHjG5iflRnQVkkY0w422KMY4iJMAbCMM7MmAlS9kpM4dTBTDESh8Aw9kq84Ay51BU9cvMq2+2WIRVYTM7sLwNGHMaVaGVWZ9MWaK1CQA3OeXa7vrC9mgKBUSXpXcflbst+WyJiAnGMGmm1hTXQaEZB0KjrMO4hg7WOzaqlbRxH61WBDmodXIwB6zxN12rEfwyFAMgwjJF+n1ivG0wOeFHFue9H9iEpLEM0S0kypKBwuX4Y2ceIcxZrgkIarKXzHa14nF0jIrRdizGWGDQb0I/7Em0WhYsCw/Eacp4gdLawNY77S1JUpe+cJYTAYDXrZBpLynD39JS+7/HO4RqH8RaJDm8tm64rUD6FEB6vO5555g20xw0v/uodfvHXPsTvePopBMcLL77Ib378Ph/+2Ms886Zjbl17kovL+/zID/7ffOBXP8IbXvsEf+TLPoOP+sTquOW1T7+Z3/bMFd7787/GT71vyxM31nzxv/UOXv3Ek+xOP47EFaur18hp5P5Hn+fO6W8RxiPe/Lan8Sbyf/3zn+fjL97jsVs3STly+94pH/zYfY6Pjnn8yWvIOPLqzQ0ud5HnPvpbXL3esQ+GX/7gbU7P99y6tkFy4slXP0m3NoTTU/rznvaxx2ifvMmvP/tRfuOjz/Oam4/wB7/gLTy4fU7c9jz70Rf5iX/xqzz3/EvcaG6wl5HGCW999SM89uQ1NqsjyHuONy2bVUdOA5K3WOvoNh6xMIxnjHHLU4+8hs3Kc3JdjZnTly8467dsVg0uWWxyxG0k7Acuxx7fCGIG9vueiAcZ6YylW3e0RyuOHtmQwoBdBYyB3XngYtjTmDV37t/RTL+BbIymgB6OltZvq2b9BEfOmaa1PPnkI2yaM8ZLOB8MQ4Tt7hKS8OjVE173xDVWm5aLB6cq6NyqBKnUqHVGMBIQgbZp2TeCsR3rztK1K1onJGd57WtvMfYJ8oBvQNC2I3HsOV6v8I+dkBFu3ztHCnxaM0iGtuvY73acPzgljZHjow3HmzXXrhwTEwzjwNmZkjlJzpr1EqEfA3G3Y5VXhGbENi3OGfYxYDBcnvXs+562a3DrVmGvUbh/eoExPd4PtCuw4jg5dqQ0studgzHsTeK4a/EjxKxZRt/Ayaal32d2cWDI0AeDNT23bpzw2lfdwllHGEY2vuHKyRHb4YLbp/cwYY/1HTHuWTuhWW9onMV0nluP3+Du+Y6X7p9x9/Scs9Nzzi8T66tHrB7ZcPWoQ2Lkuft32Jw8ymNXTnjTU0dsd2d8+MMvEJIhBGG/37LdP+Do2olCsS8GJAbOx8SDi0uefOQaOWeGqNmcTduyWXtySqQsNF6QxrMbAy6MWCxhP5Bi4MqVIyWqsIa2McQhkIPBeIdfW7Brdn1gt4s4A74xZCsEBsYYiTKy9iseuXqC85b73GdjWwJjCYFoL8KubTAnK3b7PUMccI3HW4dYQ98PGIySg6SM95bdbqvwPgznJLo2c/WkwR437PuBbd/jmg5EsN5hBNI4knKmW60JMbDb98SoEM22W+F9w8obTBo5fbAtBHIdKc5OhHMN4ltyCuyHS0IKnD/o6fvElStX8N7Qdplu5RER+p3C9Y0xDIOu5fMHUeG9jWWz2QCwH0f6MUDK9C4TQmJQBDvdqqNtvNbKh4T1Due8chqkiBhDP4wM/TjzA9hELSdpmpaubdieXeCd58knbuGd5c69e5xf9rQr8MbgjcWZYy5z4s7921xxDVdP1kTJ3L13Tts4jq8/AmFHDjt8s2IXRi7OzzXDaS1jTuQe+n6nvaaTIwZH00AygW0YlVgoCITE1guNX9FYhyRIyUIeEW9pveANkxMWxnEyso1Vxvdtv8OJYdX4YidpQGS12hBiYBzHgsRKdI1yKWy3WzLCyckJYoVV15bsXJ7E7IsvqG3RdCv6vuf8/AFt13F0bUXTePphZIxBM91NDTAroUrjLSKefR8Y9yPISO0ZOQatod9sNnjv8V6zmt3K0TRWEwJJs3aCgRxYrTqtMy/wyzH0DPtLjo6OC7eB/mvadqrVC+Nax8sYVus1u+0W6zxtm6ZsdmVYbZuOtm10PcVUstSV+VodwabxxJQnYp7NZkOIA9552q6lW604f3DOOEbGYWaH1jnQmj4lHdMxTlEDRZVJWtFThSCKmqHTLHxNklRmYmNKprm0GMpZ6keV6oCiH2s2U4QYCuzTzMQ2xpgpAQGlXrjAqa2o7QhMzMC5QOBrvhvRbCpIIa6q9Z2KHHMI3ntWqwZfcbFiStLCaoCjtMqTlBkJyvqeKTBozXAr10ghtHoITPTp41Pn+KSdxZRbkEFJZkQjUPu+MJQaWyKPlsYBWSOgMaYScSyMWTGRxwpXHBlDYNV1+LYhp8zl5Z4QIzEG2qbDeYV+OW9pO1fYlBJiTSnghxQHjSK1grSOLmgRs7OKQw8hEceRsR8YhpGm8xytO07Wa0I/qvNqAAwilhgVSjSOIyFGUsp0XYWOZjwW5xpySoQ80K0cXasEOlpsXiIophaRG1IW9v0AuSE06kT5xrFZdVhnCSmxv+g5Xrd0TcMYCzyjwCGsGJpGWVA3HVq7YoTWdfRDTx4i685hXYGWhKj3JZHF0jSaUYxR4VE5RULKhJzJWBJCNrZEiQIpjSXaJhjnNFGZIjFpC4SZ5ltJIxSWKxyfnBBSUvIYC500pdXJJOV46rFHIeuYGmsRCzQKzRMU3nm8WRFj5MHZKb/8Kx9kGCMvvXSX3S7wgY+fkj2cD46T4+s8cu2IIA2usTz/kefZrB2f/czrecdb38jzz93m4y/cx21u4PMlD16OfPzDd3nLrRu86lU3OD+9z/tu32G1NkS3ZffSC+xP7+LDyNve/nqwj/DS6Sn37r7EB5+7Td8LH3v+PnlQZ/b6Zk1MiR/9mQ+QQ0Caln6I7C8veezWNXZD5FeffZ7z7SVX1xbvWrh7xubCcuvIc+NWw5j2nD77EfYfe5nPfN3jPPPMG/mZf/YL/NYHn+el2xecnm85u7/laHUFd9RyY3WEt8L5fmT8+B2uH605Plmzv9xx+eCcdtWy6lqN1Obq1vvDAAAgAElEQVTESlrkxk2efP1VGifcu3OP2x87w/uGExO5erTi3v0zxFvEa8Cj7Vo650gMeNdw4+SEmC057GnEM5IY+ksuC5OlEiAYsnEY23Hr1hGvGW6yWjXk010hMynwlE+UVfyEjuLM1GiBxojuC+c5vmqxkjlZrbjFESknLi8esN0JtjAaa4JeYWYxRTVOBJCkUNymwTeCNRo/FQGTM42MOF+gaSJ4WzJ7RqHicdS6Y0maqQopsdv2hKDyLKXEZnOCkVLbkhOnZ6f0obIUq9I1aSCmxPUrR8TjtRry3pLFkChsoUZr7axt6FpV6mFMGALETNtu6MfAxX7HdlSoW0grVq2j82tCiFxvLN54eg8mCdbAnbuXXJ73GNPgvSWzZ9zDeWfoguViO7JuE60TPut3/16eetsXEFPmV3/+p/jnP/wDQMOqs2z7nudeepn9EPjCf/dL+d1f8YfIwG//+Mf4nm/969zYHGGs4wt/35fwBV/0JQiGszsv856/8+08euOEaydHPPH0Mzz19i8kxMzpnZd497u+lYvLHY1ZYYJl4zzNIyuMJK70kbut4+6DkfXG0a085ES2iTGro5ZCYAiR0+0ZQmaIowYMdiOSs85DDMSctXZRyvryjuE8EWJiTMqBekWOsUSsAzEdQkdrL+mc4+Xn7hHSSLKZ87inWzmcNxgnhDGwG/YMPQzDDtcYNs7jbSwkQT1aPpA4u38GIuroZc06CnDzxlVCvGTdevph4MH5JZf9GYil645o24YrnWaghp22sEohKJxShNgPpH5kLFivISh5y8X2QnVP1yKihELDbgspMQ6BEBWu2nWq50NMhIvI5UWPoM5BSiWrIUZr1bKS54xD4ixcklHClNYZjDOcrK8iYhijsN3uNADUWPBZA79kxGbWbcsYI2MMnJ9vkSxcubLGOMvQ95q1KugeY6DtGgQYwg4xjutXVjx2/QRah+SAyZkxOOK257e96UlO2oaX7+944fScK9da3vraJ+hsS+aIlEfOtyPGO1J/wm4Yefm8Zx8iXSc07ogP376vpE4nHkmR7RBpckSs0HhHIw1x9QhGHEdNAwnGIbDeNNpfsJSbGAGT1cgfxpEQVTIOMWNsq6zABTqpDkrk9Ow+1lqatsUYQ4gK53XOcvXqCTEmhbrHQN/3pBC43O4KGtAg1tOuLEMYCTnTdB0xJe7eu0fOuv6GEMikIhsb2sbReq+JpaSCuFk1hXDLUgsMBOHk5AhjLLvtJcMwsj3fsytQ/KbxeGcL+7NMMMzax7T1gj0+omnsnOQyQoojw9ArAVXhqBiGgaFX8kEjIN6RUiqZcKetfYzW/dds/VhIFENK9Lstu+2ei8sLcs5sNms2664w4GoQLw6BPu0wKeOMUec5lySGtxjpCTFDChNi0ynL4pRZhVriUwh6cq3rs6TsJwcyQ6mTTqVCMJfyoAJZLY5dTMXBS6VesqtZvDTVAdfWNE3TTD+nrIRa1jpSOtLsX2FpTSniXTNDj1PZ3zGRWy0bMtZg3SGU1TAznGaEVdMW806Ds4KyUrfGKMIKpj7COi7FhvS6J2D3r3M9Pn38/3R80myoX/rFn5cFFVKh4L1jDDhjcE4ds5wT292om7fCbDCMw6g4ekFZu3IkhoB1jqZptTatOjklUqI4cyV7sE6VkhRlCrDb78t1W7q2pe00WjSOvUIfnKNrWxBh6LXPYChRD2cFZz1jv8daxdR754kx0XSeoR/Z7fpS82HZ7QfaVq8vZJqyecbkGPqe7U6jJ266J1hbSHrQWs3NqiltQxzO2EJuoZ8Ro+yId871OkqkIvR9T7/b0TZtqYHKNK2bMo8xZS4ve4YhsO/DxNxmvbbW2F7uECMcH63wxnC0amgazzBk9v2AiOC8ow/jVFg9jjp/MWZiiBxfOS7CN3N5uSWTuXXzJmEcGYdBlUjOdG2LbxuNBo/qVNZon5jKqpc0kmeVddZUTLwoSYGzTrPCIoW1ciQi2AQOjb6OBZrWOEtjtI4nxoj4wigaBpy3HB2vuXv7soxZS9t62iZydm+vtVWNB5ORFGialtvnW3a7PfsHPSEJb3r9E3hruHvvPhe7LRe7PQQhGY9xEbGZFCz9PkIUzRhbQ9M1rLpasxfp+xHJcO3qCrxVmn1rOL5yhG09fhi4erRRw00ypnHcff4+d+4/4P525NrRmt/29Ku4+chVHn36NWzv3yPudvT7HWdnF/yudzzNR5475fkX75GA173+tXznHxnYXpzzP/zUTdarDenKmt1+yz/44Z9hHCD3gZOjji/64s9nPL9DkxLHm5b1yvPy3S0pe4ZxS46RELX+7/m7pzhvuPegRwSaRhXDuvU8fnODEcN2n/DO4I8cpw8u+fbv/Sk++NG7hCRkwsJHLNCXh53GRYax9pU01vAHfv+/w1f9ns9j98KHQGDVqTHojSUZCBm2+5HQD6z8Cmu1RnYctKay7Rqtbwt7cmE6FPT5d33EtcJ3fY22ivnj371X9kznSAnN3nlLEuFy2/PgoldG5DCWyL0yQis9emYYRrpG17YzQtc0GAMhKQzfmJYfav5iMYpmFrxckANv2b+Hp92v4p3ng+GtvM9/Wall03uJFCNE4I+4byfHnhxGfsr9Cc7sY6WVkJJnaR9Fy2cffYDf2f4IZ+c7fvPsOj+0+2OsuoYrxytqc2rXeO7evs8XbP8WN1d3uH5lg3vrn+QPf8UX8NV//TcIMfIP/sJb+cbv+xA/8/4zNuPzfFH/v/LotZYnn3qKf+8/fyd/4K+8n7Nd5Gu//DEeu+b5lne/xFOPNLzrTz3GD/4v38CJcXzG/8vemwbrlp7ledc7ruGb9nDmHqVuJKEBicFAEKZMICZ2BHYIiUHBlAGXgVQcE1elUkVsZnCVgyMgQFBEbGYbMAhBMBgEAoHQDJq6JXWr1d3ndJ9z9jl7+MY1vVN+vOvsJskfl6qS4ofWz67qffbwrbXe57nv+7q/+Cuo9xa849/9Iro64G98wz/it//V/0wYdjz8mi/BTvb4vTf/PId7c2qbn59DCvTec+doi+sci0XJ2XbLstmilGQxmSKixCpFXWXHSd93GGPohn7M9WmkUFRlDSln8ozJQ8jx3ROm0wl1Ob6jyM+0oqqB7NSoipKqLNHjur8P0DvPpm9JXnH3zq1ctaMk3ieGIVBVU5BQjhbx7dqxbRpW6w16tJnOJyVaK+Z7C0JKLJcbut6xmNUokYFK92jim007ZmXz+3K9W+fPn8ygLERmCCilKIqKe9UYiUTbeXZNx8H+HiIFXLvL2UKlkMpidI6BCCXofcR5z2q9wbuATPkQLIU8p3p7F/IiWMkRAJSPuowDpVIq2/8EKJEzd6qo6AeHIC9rrJZ0vacfBoQ21FXFjRvP4VOkriej+pAP+otiOoJKYNM2bJstQioODhZM6vw8ePDKAa940QP83rseI4zvsSEqzpZbvupLXoX3Lb/3/md45mjL573iMl/08vtZni25u9xyumooypIrFxYUAj5xc827P/IcbmgIJL7gVQ8xqyy3Thuu39kylUuULXntF/9nLGY1wd9lOj/gl//NWyimmYA+rUouHexzvD5jOp0yrUuUkkzKmr7vIAoG5+ldXt62Xc+u64GcjdXGoqQmxEDbrJESqmqC0oYYwA09SmXYyK5paVtHEvl9GCNjT3AkJMG2Gei6jn4Yso1T/4V4WhJIoc8XhVVZ5AyoSFijqcqSEDztriWEwMZnII4QAlNoGJ1hQzfQNA1CCCb1BFtYnPNE70flSyEFY52WPB+E++AydEbls02MkSgkWupRGBhdWvEewfgFxRSyUqu1zueTc4hFdot571ltdiPoEAbnQEBZllTWMJtOEUD0+Xsox1q3GKFpdgiZc5NSynMQTdPs0MZgTJEdDj7DsbKYONaopUyuDyOONo2RCm0URVGglCKExOBcrmM7z0Xm+NT5sn7kWxSFyWdFIfHO03ufadV9f67EaqPPz2rD0Oelg7zXrwuz2TRn12Nit90xuH6sQYvj/a1Q3IN/5fdxgmz3HTy7bR7Sh+GFvyeANPr8XeaDHy3WESP1GEPL9PV8yfM/XB5mE+/96I3/wCnk09enev3/SkMVKpFCHBH7OTQcY2IInhglmBFL77IyqBUg8uHfh4iJEdQIaVGSFGXeFiVw/ZBfztYg9T1wTj7ESCnPbTNZLcy9hXLMP9w77MQI3kecS5mqN0qGUmaMr1YKq3S2Vg6epF8gd2YbLGT8lR7R2qM9RGtCaPBBYmKmkOayeoVKmji4ez3j56hw8neeX3ZZosBYnR/2haYqCtr23s8kMFZRl5rj7UAI+RCslCRFhUiWsrCIdI9UGnHBIbTOG80i21V8yAdVEhiVH8pOSZTJ2ZUXwC15kC1t/jeM1jDWjqSUrSckSVCJQcQ8WOuRLjZkdbewBkkOxTN+X9oIjBRgZM56kGlkzucBX4p7Bdl+tLC+YM6QWpP82DundYbBpDGjEyOFkFilKbSly8QB9FhG7vtsGW57R2E0Rmucd9w6PsEPhsIYBudZ71oSnok1NG1De5ZQVlKXirTpWa535BCLZeg9T37yJsQ2WyqkxmiLkGDKAl3lrdx66fKLhTz0G2uwJof5274lxURdFmip2DY9XkTa3Q6JRC4bApIyBR667yJlabh7tuT0dMPF6RxpDAcHJYfTCXVVAAHXezbLHb7dYWwuje+8YLnuaELElpYhwff86X2c3XieUwZWHYQwcHb7iOdvn1KYCXOd0fydF2y2HdcWM0prqE0ees62DSb1VNbgHJyuttw5W2KrimduLTFSsjct8TGwNy05mFek6FltWqZ1wSePWwgOSf4s+rELbXySwKjA/9/2Vv8PP8o5oEop7rvvMnWt2cacn3ADxOiJSmZ1Qkm0LcAz0vKG85yIDwmGnMnKuWEQIyhk6Bw+ZsvMOIpllVyNFRc+0vYDOAHasGkGmsFhjcFYTeqHkX2Qh1xjK0KIdINDCZDW4lPu4TQ2W05/P30jMUZu/PQX8oa3PM+XvGLB/lTxz3/tOX73z1ZYo3jxoy/ldV/z9VAe0oo5b/j153nbh1fcf6Hg1/+nz+Rn/+CI1758wc33fzm71Slf9rr/im+JeyAN3/tLN3jvk1seODB879e9iIOZwcQHePdvfYJbH3wfCMHdf/MlvOEtN/mKz9lnUSt+7Lfv8vaPdxRGsT8rmRpDYTV/5dUP8uZ3HrPrPAJ487tO+MovuMA7PrbEhQhacu3yIa981at44vmO3ZCw1vDuJxve+K0P80NvvsOLr1ievn6Lu3eOmV65wq2nHueLv/bbePbWj3P/ow/x3DPPcP2528wmisf+/L18/X/3XfzmL/9cbpxI2Q61a1vawQM5A37lcIrA45PDWM3BpEaO6tZ0UlGUGuIEISS7ps3ZGaVJCDabnJ+zSmGVIsjItSsXKKylsAKIDEMg+OzC8D4hidSVYVJmABpGM68KQkqUG4tVhu3qGBci3uX3g5aRqsiLVKXuvQcUUmpm0wm20FSl5cqFBRo4uHCYFevg2EhJYfSYe8s5/7KwmWwbM+BmcAO93+FdzsorbdBGn1N573XrZjp4fhZnu5gcF5sKKQ2Teoop65H0DSlFhvU2W/h8hsaUpaEoLEoqikLl90ESNE0eyJXSuBjG/mPPMAzEJMc8uyC6e92qY2esVKOVElxM+Ai4QCuzlVYpRVmUpJjzq+ddc+leB7DIFsaUVbDS5F7EaVkgRaJZbxlRQOyGHu8GunZgtduy2uacuUHR7hraoWO7a1itd1y0JV3Xs+17jk7W3D5ZEd2OPsLtkxUPv/Q+zlYNy+WaajFw+crDvPjhFzGfaHZdQtVzju6csgggTf7szuc1d06W9C73uJZW0cSG9XoLQhPTX4ACAaXJi7Jcc5WpvEoJJpPyfNHuXUSQK3hijHR9x2a9pek8RVWM5xCRQYEy02qHoT9foNSVRev8bLYmk4rVWIFWliVSQtd3ODdQlpqysvgBYtD4qLBdwI9/FzvC9oLP1VnWGqy1zGdTbGFp24a+I6ve92yIPldPRARSJsyoHCqtcC5bljP1Plu6SSPLYeQ5CPkCffOeqg2Zu+C9B0b1TJKhgynm57GSaJ2Fgb35lLIo8vK87xmix+osTEDOBLoQ0FJSWo3Ro5KaQJQVtiwoyoKYIkOvR6DUvQUdcC8axb33Q36/aaPQ47tgGDzSS6IcybvjYGd0zrrei2upUWRACJQyODdAk3AkopbnESmt5Pm5VQmdfwejAwyR+1hHLj3gIWZVXIqIVgalBFbnpXwIeenjfaDrBobB0zvHvRd5/nfy1y7MXxgrRAQZ0UKi5eguyFPr+H++AG1M8AIk7NPXX7rrUwfcxBofBgprqEb62qye5Jvz3oNJJ6bTsReQ3Hk0uIBQlhgCbgzj18WEJEtCzIqaG7JvXBkzksTGDqKYxpeQp5zYPAyoLGtP53W2pIpI8Dko7+6VYhcFRlskgugDRimUqWC0LiitM9WvMDlvByijSFi6TU9VGKzJErvShtLm0vH8wNEUVo83ZGBa11y8MB2DzBF/Xk6fEcld2xGTZrnJN+quUVijiWM3UCJjmpXWTKRgkBLlfa5mWMyA0V4gMvlu1+6QEoy2KC2wk4hWFdOxm9G5QD/kg86sug8EtL2jd4HeZUBRTUDLnGvoY0QVBW7I1ixRmTFcr4gRhn4gjLjuSV0SU2K3OSOllA8DVlOgabs+V6ZYQ10X48tDv0CF9Jm2Oru4BynQtF3GX8dxy68njEDSXGnicyF4RLDsB4g9KjUIWZCSow95UNufTinKkkPhaYdITJqqsBxogScwZA4KC6WJnSQIR9Ka6TRvo3vX44Tk4v4CWxTMJhVGOVySdC5SmkxCbPtI5yOFzhZnHxJFbPLnk8SkKKgLTdcP3Fk2XLm0l+2NMdtKTJe34kZmO6FVeYD3Hk7PNgTCqMpbvAiZIkzgZLPlt955l+OzDXdO1sQYKQrDlcM9CiX517/9IWqjuHB1QVVbPvDY86zXLbPJjOVqle02EibScP+jV0nRE5Hsmshb3vK7JJ9QsxmLQjI3klvLluu3Tnnk6h6H8ynDELizXBFVpGgit4/XiBg40RJVWXr2cc+e0LQdp5sdtbE8ceuUBw7rTJUcN445+yAg3qPr/r8zjIIRBZ4SpZbnNp3U7Hjyox/hzo0znA847+ldBKWpynxQ7LoM6zk4nLNZ5poHrQ19H1ivdtRTw3Ra5/tdCMKopE9qS3fUsd7sYa3hpOvJ1RUZzjMIyzBkGFHXeQyCWVkwrfPGtO8Gbh+f0vYDC2sREYYuYgpDlAVN77I7oCoBwcNn/4zgHPB+Htq9lZ/+3l/jgQce4n/5jh/gTc//MD55vu4bv4Pf/Jkfpms2VLN9fvTbvoNffeMb0WXFwez7eWT9Ft750x/k+dt3+W//6b/gf/uRH+Lt73g3Z5sd2y6yWq7517/9G/z8G7+HN/3LX+CzX/Mqfv3Xf5mf/5WvYX+RVf+vevjDPP17f0Rz4SG++z//On7tx76P9PKayuxR2TlSRF5zbUA+8ztcuu+DLPZnPDx/DVceeg1veNWvcHx2Qj29DyUK7tx8ntd9BfyTl/1bTo5v85pX/S0m5SO83v4Em8fgka/+fn5dTvid9z3OV7/+CzG25PZZz8kHP8zrvu4b+eAzZ5weH/PN3/rfUJQVz90844mnrpOSROpI8B4tJI8+cJmqKnnsiRuUdcnVgwsIsstkUmZFsekbOpdpl955+i6TFJPKVQZ//M6PEIVBGsOiUpQE/vqX/0fsmh1//tFbnK5bdsOAtYapKTlbrUAkXvKi++mHgbtnS5KWKJswQrBvZ+zNS7btWAKRQBGxRYEbOggKXRUURnHtaklIM2azMt8BIbLddqQYOD5esuta7t45RiRJYRTb3uG8Q0mBlZJLly5QFJLoIgnFxYMD+rbDKIktK8qqxAfParVi1/aEmAEZyij2ZhOmE4sIPfNpxYte9plMphWNTzxz45i269idbdg2ucKFkDDGYouSqjak6EgiMplW7E2nTKop6/UaKaCeToikXP/SNiQBbogole2HMXgKazndNbjekyl4kjtnW3zKdVjERIo9L73/PsrS0PQ9vfOU0wpjSnwaqHS2J9Zywn1qgRoc0giU8Pl+TIIPPP5J7t+f4pRlF2B58zYHBxPe9fGn2Ww6vI9cPSiwBJ6+cUxUkrKY8Mi1ObqseOrGEbdPT1lvO4oqIWcVh3LO9dsdF6YnnK4acAMveeXn8OrXfhnN3ac5e/oOqxS4ebQEU+IHWNQ1PiQ+9uwtws6xXp5w+9YKIRKnyxVN6xBFhTJyrGKCaV0zXZQUpcVIx9AH1ssNVVlgS4ktDNvNluVyg9IFhbFYk2nvm9WWqDRlXaKtxQ0e53rSSHp/4MohWiqkzUCbMAxorSjLApKg7zL0TjHQdZ6maSmMRSVJs22yO0sIbGm5XER8ncnt1hS53zYlSHtokyNFvfckFNvgKEuNNhXTKoNp/JAjPkIqtM4VG4JMd3XDQN8PbHe7rKL5cC8nREwQk0BrxdBn8UApRYqOrnNoJVDSEsdFvQ+Osii4dLiHMZmLcM8FZ3V2rvSDo5MBNa8yXG/s1tVIpqlmUpVMyzpnQ7sGpTVFkQnLwzCMOcgXKMNaixwpUFltvxdnSOmeLTXRd1mh9c4RYkRrTSTks6nIBG8l8rCrRkK+D57NejNCZGQmCgfPZDyvpRTxLts5jTUUhaKwBc55nPNIqdludqPbKy8mjDZMJ7PMp/Ce4CLb8Qw99J5+8ASXh2+lFPP5NIskKoOXlNT5j5I4/7ouQMJm9oXI9WP3MpAp5ZxiruOBwd1TUD99/WW8PuVhse3W1OUEoSQpjUW0MqHUuDHR92T6vDUyytB1juVqR+/yoGi1xGhJ73ZoU+VhsutILmaVyzjqiaEoS/qux3UDPmQUeu5Jg97lG66qDEWpKY3Cy8jQR3RdZaBLGAgkjK6xBvb3Z5Bqhs6BTCgr2O62aKkwKnekuRBQRlMZjTJ5SBEih6zt/pyu7RAxd0edbjb0g6c0IW/mBIgUsUZSaUXXB1qfC893XY+mZz7bywoacXzIkMmSQhCTw/UdVw/mDAMMIQ9QIQW6tsuWhphLhrXNPnAta1Ia0DJSGIk1OQPivONsuyGSuHzpQbq+pds0OJ+BGtYYos/U1xBybYLUknaXaYu2EiN+XiAwrJY7UopZjZwZIpLYN4SYkC5gjKE0hs4lDBFrC9CGZrfCucDewQKJZHs6goC0xBaKfsibRqvNeU5icAPd4Njtdrh7FtiRlHuvj6q2U4RKtG2L957Cakpb5MOGTCOZN/9Mi2lNPwQ2m44wJExZ4IfIthnYm0r2ZlOst2y2Pe1mR9v29MOQ60JCQOkSjyLFgb7LQ9YQA9LmQW++ZxFCUpdlfvD7QCUMly9OqOoio95dzhlEkdXU1jmC8wxufCmperSYSLZdz7pxHE4m9H7gk88+h7ElWpdYLZmWGiWy7bEfPMnkF0kf4YlP3EIpwd5ikWsXygGpNdqULLc7lDU89rHn0EpwUFcoIThaLTFJ0acTysJQWk3TNiAkjz2/Jt1Y4YaeECNVYamK3BGqjEJpQVVaUoicbpucU+kCZ6s1e/MJe/MJiYCLLltwJIxMeiBT1ECMPeNp7PAEKTSKwETnF/5m8Dxz/Qb4gmYZ0VYRVWLnO7rW44493udsblWXnGyP2G13RJ9f4lrbrG5uAyfL42yxTpam77l4OCccLVkuV1z4gTtc3Nvnwzd6YoT5rGQ2NQxS0bucBeu8p9t2nK5adCGpJxn01LiIS4Lbpyu6xnHr9hkxCYpJgZQJGQb6PtMHL16cIEcs/Fve/BvcuH3Ex67f5W88+SRpcogG5vsX+Fvf9I/HXHbeyO4fXKAfWrwbeOJD76CaTvjQEzd429vezld+7Tfh7CV++3d/n6ef/DCTyYRXvPIV/K//+88QQuJP3/fn/PkHPsz0wkP8wR++E4Cnnv0g070p/fYuxWTO5YM9jlYDN0/uMplK9mcVIsGlwznd1QMQY99ggl6AtZa2Hbh555S2eQbsG/nir/xGUkp89M/eA8By2fCxp5/lJ3/+rbz+274L4da8/11/AsDFi3OOjk75Vz/+o3zH93wfRhve954/BeD+q3s0jcF1njiWpBMTd093LHWPcx7lWvohA72eOzlhWtVcPNxjMS3YtlvaFrqhpd84rCm5fG1OUUsu7B9wcDAFleiGgZmZ8Cfv+hCrdssnnt6wbT37B5YX3begG6DvAn0IfPDjN7h7sqWcGrqmJbpseRucpKoNDz54Hw9ePeDyhSluc8Z0NidETVkorl2cUdcFd7cdRydbhjvH9O3AZtey84bLFxYsJvm5e3hhn4kqoJCItSBNNYUSlGMCo+16nr+94mzd0rlApSUvvv8Cg/MgO6RMlIWhqgq0tkxsgVGSoi5xydGteuqypjIVu03Hex5/mn/3Bx/Ce89sViC05v7Lcy7tzehi5OR0xfqZHckFBiV45MFLXFoESrWjH1ruu3rIcr1l1WwI3lOXlnoyJSXHdt1kd8ngiSmw9QOzuqaWJW3nOd04QuhI0TOdFNRlyYsuHoJ2XD9ukFLS9YnT9Zb7DudcWGiU1Wx7MCny4suXeOb0LmsX2LSepl9TVyUXL9YcLRuaVUM3JKpScHS2ZOgSi2nJ3qyki5Fbu8jzJ6ccTksuTAt2R2se/8QNorJYJTmYGKISzKsakubWcUfXe6q64P5HH6Fb3uLGB99D22xZUfHcs3d4xaMPs9w0TKqsbIatoN5T9H3g9tGSthuoy4LFbE6Quc5GRBA+stsM3NpsUEowtQXWWBb1lJDg+vNHGToVM81cKI82AQnYJCjkhKA8TT8gRCD6bFHsg2MyK7l6+TKFMTx1/Ton6w3L4ybbIcvcnWkE7O1PqWzJEBLd4LhcTeV4mqAAACAASURBVLDGYkXAqEQQoKzCJoEozBjdgaHrsy0yJdyQFckYoLJT+q4jFDYT1WVBWVqiSrRdGG3Tgs2uJyVBYatc7+UdWmZ6NaXNWUM3IFJkWlRIaWhlBrUoNZL0C40bwgh5yIvGpg3Z+mpVnjdFGAfTgFAKpQyFAOezIusGx1RncqpIgsVkQmErms2O7WaDjwFtEydnZ3lwjdmCXZYVrs8gQDvWJJVlkRXSEeyWlbrMg5hUNVVZoKdVdk6Njh6lLAnoe4cfAsEHOtegtMRUxdhDSraKqgIlDZNS4WPA+4SyJdoo2m5LCJl1EceFrZY5+hNizvD74AnEzJgInr5rsuAxWlqr0lCXOU5RlTXGGNpuyEPpGKhU92pPgOylE2NddsqLKz/QNDtciHS9J3hwLuRWA6MZxj7QT19/Oa9PXVlUEzZNl+soUhw3DHYM/gZC2CJEYj6tSAj6weGDZzIpuDLbP5ebg4/ZC+4bplWBWUxxaJp24Przp/TNKUmLDCYgcWFSIXRibzbDuR3eh6z2mYpmvaGcSBZ7NfNFzfp0Q9MFbGkp7AB+Q9M5luuBshaURQa2CFtw8twRh4sFFy9exhhFXUCtJSvrIeSMk5Ae00N31mfVbdyoyBjYn1vCUBFSwFaGylqUENRW4ipPN3ik0rziwZeijWW52zL0Gbuvlc75D5XtjJttYmgN66TQBQzbliJpHrp6lapUEGJ+GMWErSoCjrt3TplMJnRNyXbXswsdprBoUzKpM8Fyt+yZzEoevDql2e0YekdVTzA2sdwc0/UBEfdQQrJ33wQXHFqXpAT90CJE5MUPvYTox5Jc75BKs1oZyiovDgaXawgu7mW7ZQ5YORaHF4nJsd01aG154Mo+MeXhPwXF4aUyQ0H6geQdYWhZ1DMWJRxWUyQKKRNd3BFrBTHbnptmoC41F/cXGYKz3uK7HcbWzIoSHwZC8nRDJNzdEoXKPWcqUpuOg1pxOClISTCkgLGKB+5bcDi/gnORdttiRSKYKS46/JB7Mi/tqzzoSsnBYo4xmsE5UvLcOtnggyFXsRj2J3NcNxBFwmtHlBGLRFmDEOShtI9IoXEujhbjiFaOwgpO154YprzsvqtIJVn1O9ZNw95ikbOnpeXKhWnODmpF7wdap1itOm7eeo53fuM+u8mMv/ozZ5ytdqiyRibB9Zs3UdYwKTJAaBAeIw2ltVhbYLVmtznDFPlQIoRkVl2gKi1CeDCaQhfIGCE6fHQUZcmuGyiM5v7LF5iUlj4OLCYFxQemCNEAAzKQ+/PudbQJkQFOXpDQ5z2PUbhs6SksPsLQB5SXHNb7pOYuQ4gIXzKXFXXRIitJXRRMa4OPA7dun3Lx4kWilLRDR9t3FJVmGDzRZUjOqlnRu5Z2c4ZVmktXLnDYCp7/xHWOPAzR40K2XoY+EHwcsfMV5WQGQAoeQjjPnFSFRZKhNX3K1OjUe1IITKuSR198P/uLOckluiFvgEsDDx7MsWXOGSpRsB22fPLpp/iu//G/J+wC82nBfL9AC0E1v8xXtB0feeIW7/rIdT5x44zffdc/5pWvejlf9h//VX7xZ3+SH/6Rn+BXfvnN+aHtU87YpWzZb/qW22d3AfjJN72NR+6bMik0r309PL/qODCGSw8eIkzCBcV2s0HPD5Bacrpac4hmeec2p9dvoOo9jtdbdJV4+PIh/dGz/Oh3fzvaWl792Z/HdnXKyeqIlz4y5xc+/ii/+MQZ31z/BJODKyxPTrh765RLexMuyDPe83/+FBfmhv3qkO3qlAcOK/bvu4g1mmUXuHF8i7PViudXHRTwGfOS5abhYyenzCYlX/r5r2HXOG4cnfDEs88wLzSvfMkjFLXCWs1uu6PdNhinee1rHuXO2ZrTsw1Hd074WHiOo6cHEp5iVnHx2pyXP3iZwlQ8+czTY+48crw8QYjA8V3NetMyqERZKx7dm3Bt74DP/qyXcevomMcfex7XKWRxl1vLWznXRf78ffTpu+xPFF/wqkczWVPCzaNT/vzx69x//yGH8yl7tWElWop+xn0XLpFCy8m245Nrhzw+5Wzb0ITcFm9kwBSJ21vPzaNj3vf4J9n2A9NJzdRm10wx0ahCMk+SajpHJMmd5YZP3rrB4Fo0iov1jL3aUNUFqILt2Y7NZsN622aFanC4EPBe8OGPPUU7OIgVh/MFymwpbIWVNV3v2XQ75vMpD1yYcvHCjKgU1y7MuDSd8VCluHO24bHrt4lR8Pkvu0xdzvKyTOYy+UfuO2CbIheuXmJ1sqPZblnsWY42m5xP7xyl1JRK05c9Fy/PuaIkN24vecsffYykJXeOb1NVU4TUHB3fpSSyf3Uv33OTA1wwfOTpM47urFg2DiUiWsPB/h4U+0wnBRHHdrtl2EaqmYDYg9CUdsbicI/3/uHbqCcV+3uX8WqBah2v+byHeO7ODeaHE7arTX4u1CXBS5LwXL12FSUFQ79BxEBEjyRiMJVgUdW8tJpl2IoURCFZdht2u4arFy6hTM7ZSwL7k4qoAt5HVNAkGTnuB0QHm82WbugxuuDywSFWKp546hlON2uWZ1tEUiwOKlLoiDuF1oZyNqFZwSY2uNCTVI55nDbrvJxG47aOvhuoFzXzvTkiRIZuhwTmk4phcMRYgJmNMYKEObiIVIphcNx69ojCWppmx2w+5fKVyygpSE3Ex0Sz2VFUNeV8Qte3uL4j+oHSVhzs7QGJzbqhaxuMzA2eeUkPdWHwVpEiFGNMQBwuiD7XXPXO0XQDJy6Df1LKveF9O7Db7bh87XKmB1cFXdtxenKCc4GoBO1IsL//6jXoB5It2G22bDYblFJMp1NKnevX6ukcrQ23bt7Be4/zOctb2AJbFrRdpPMNqu0praG0JSerNX3XI1QWKILPncA+xEy6jYnJbAYCJpMJ/dAydAPOR57pPFolCiOZ1hbjcq2dsRXrZqDrO47ONmw7TylgUmiqUrO/KCkLw52jW0QM9eJSXk72a0DC2GfuU+Lu2RrnAkVZ4n3AOZez2GWJc47Tk9PM2dDZBj94TxKg0eBz/U5ZFiij8G6AITcPlEWm1n76+st5fcrDotKZ6mmswSgFZHQ2ZPKlLcb6hzHvVxpDUhrnJGfLjsE5CpsPRSnFfEPoAj94nO+QMbG3KBkqcgY2VufdwdJA6x3OBUqtmJQ2l2EfzFiuloh1T4qaFD3WwtA3EDSTylLXit5LjAEZI9FJog8kPaWNmt3QUCSFGySrZcM6ekTU+K5HKZjNKsppxXa74+Rkg0Rw9coe9I7tqkEogWpzebwfHNZkC6hUBq0Mz966Q1lXbDYtMUWszQqsFJoUAlJCPanQtaE764j0KC0YiDx/tKQqSoZhIPgdWpOHYAS9A9l7XMgkU1VAYMCHrCDKStGFge2dBqQiRIeUiWGXiBtYb0PelKktIgp0kYeD0PqR3OVwviWISAwyd1u6XPqrlaLbteekLjckcANFUTHWD9H160z2SpGYPD7kguCUchlzgUGgEUSKKiEwY84gYRQQEz50GbogzPlSQgmLsZlgqJTmYDFHSsPgO2KCSlX5+4qB5B0IhbpXUhwdAFLlz58WBqENMnmGxuVeMKlIIofoB98iMCAUTdcz+B4lFF3vkUrSDtkqlfulNMF7nM/IeOc8iERZWbRR2Y7qPU23zSTS2TTbbnp33s0ZwgBELlyoKIsC3wWGEBCmZFZprl68gFSalAK73S4fKKyl6TqUMixqxfShq/zRA5q70TOfFhRWEVFooaiKa0itaNuWvh+Yz68wKSfUhaQqLWVp2O3maGXYtvn3WRYFWmWCr9YSqw3BhfNDpFCCvWmBkjLbvaVkfbxlsJJyaignBUMIo/Usbx3JWf/z7qp7+YV7V6FEbqSLmVg5YHnuzin91qGMZDbLZNnBQ0yBKCJb12fw0aRAqQyWMVZmKFZITKyhlALnAtbWJEqiy7AbYxRSKy5dOGDsO6f1faYnk6thXMr1CkJm4qDv+5yljtnmqIBCT0gS2hHxXlgz5mslwzBw5+4xf3r5BxFV3sX+tf/kb/JLv/BzXF5c4P6HXsQHP/I+qtJw7dr9fOHnfj7vf/d7QUn2Lj/I8598itletvQ+9fwJy61DG82jj7yYjz32OB997DGm9YTP/dzP5k0/9bN84IMf5u9+w+v5uZ/9BV7yGY/y6s96Je957/vObb9PfvIIrSJ70wqA33zb47zutS+hOR54+uYd1puWh58Z+Pp/8A/5jR/9KYRMfOl/+fm857d+FpcSV2rDlfkhbRK0Q2TtEoMPzGeWL/ry1/H4u/89l/f2kSqwV2lONwGt4Uu/8m/zoXf8Dg9dmVIUmv3DGcNuw2o38Nf++lfxzj/4LT5565RZsUOMWZ1pbSntPtXEo43iitX4KwvW7jI+BG7cvsu66Whax9XDBfO64Natu0gFh1f2GYZADKAldO2O4CJITVXNiL2lvBYxVtEHT10aml3D9dUpy1XEVgJjJNNyxrZtuXKh5EXX9jk82KcoNKV0KGn46BPPsN5l2JkRBpMGptLSOsft1Zq291w5XLA3NzSdo+kDPsFiOmM+mSKSYLna0jaSui7YrpZYEs3Qsly3nG4Ci4ll2w4QBoySNCGrUalfcme9oVCaydSijeBgvmDbD2gh2SsqhE9sdw1nqxVNl9BYhEz4HhA6GxADSBFQUrJrB7oOUshZwShyhMSiMUZTTiomJXS9QCZBaSVVYZjP5lTlBK0ku64nInjOBZpyh98rcQhedN/l0QkTODpbZ9eIbwj9wLVr+0xLTTUpGPqe23daNmHgxp0zSgOVNUzrOU5KHvvwDUpbYArB7eMNx8vNGIWZUNkSgcQvZiwKi9WaoqrYnx1itcbHhr1Fzf6FEh89IXq6tsFKy27dg4RCWSa1JAySphmYFgJdG4Qx3D4KmE3H0dntTIFXhuX2JsfbNVpbVMoQn6IqOT1do6WmrGpiivS+wViLFhNqqZAqoWTCyNydOLicv1MaDqYl87qkbzMVXspE8I4uZMqpFDIX16OwCnQJ0+khMebcrVaGrhtQWnCwmHHl8ACrNHsHs6wMyRyP6J2j245dyzqHZbU15/AVpSWmLrOyJBSCQHAeiaAwGYxUTwrqMts5t5sdw+BZzGuElARvmFSKuq7Zbnfn8ZTBBTbDgA+R3a5Ddw6pJVqOfanVBKP1OXTPecfgPNPJBFPYsZ7jXh4yK3t1nWuT7p6cEBHEJGk7z3rT4GJCyFxbppWkKgP1pEZLQ4yB1WqVbe/GjJwCwbSqiTGxWzXjz5KhdgfzfYoxg3ivD3O12o45YtDGINWYXYwDbTvQDwHpczynbzq2MgsgIcUX2jFGYJV3DpA5yzlkYBMxE0S7EeBUFYa6zrTobOH1SBlRfaLtOvq+I7iAkVl9FTIhlETpgpQU3ucO8aFZ57hTSoCnafpzwE3vXP6dDHlp2g4DCJBik5kTQpGiRHgIHqQYoVwhAw0BXEgMYchCkxAoJYmEvwC++fT1l+36lIfFyUThhuK8R0YKqMps4YQcZpdS4ceewdJkpYW+J/qe2bQkD5aCcr6XbwQpkarEaEEMkZktEVJR1CLTBr1HqTpnOLRHysS2C2wbx53VlgjcPN0ijncYsaQoNfNpQakkqYTFvKI2hkJr2nYgJHIFRWG4OC8x0mILjYsDx6cb+k6xN59QTySbMORuKBfZk4p6sUD7jBAPvWAgF8nOphWkyGrXcLbaYSdTtIA4ZFlflRq1Gmh3GbpRVjaXWDuP0QqjJdt2yMAaD0oH5vMJSUhun6zo2zsUVVYwe9djzYBVEFAsl0uMMezNKw6lPMemV6UiScFm2aBVQYq5NkQphTH55m878AHqOqKFwQ0ZEa80Y+0JVOWUZuMQYgxwA91uy+zgEmEYCC7jlTGWMHQMo0c/bwm7bAGUCl0apIahcbgBEJ4wrJFSo2XGSJMiPnmiT/SDgygoijHQ3Q9ZRSvUaMtUdF2fEeHJU1hDoTReRrRNaBVRQIgmE/tEJpAqa3KvkGLMFkhikAw+QPCYGIkjgrsuKg4WBa4fu4jUBBcHjClHy2RgT80IQRKHLYXRFKbIQBytiES0VFhdkoDV+hSAspozDI712QkxBhZ7C6pSI7VAMUEJlaFRwbM4rAgx0g6Z+lrKSEouA3WmNWVhGLqeSVXmvisSVVkjpSMmwf7eFCEUKYaciwhTkki0Q4V3gXrsYuv7dbYo24J5rWm7hnoyo+scu7YHNM41TIsJVQVUGjBIOSeFRNc2aK04uLCPLQwKwaWrE/7qF7+Yam75s/fdYNfsCOc2VOB8aMxBVZEbvyEmLBHp8+JDCAix4WTVUxdThBTcPL7DtulyX5bKWTVtJQZJ07RIpZhUESHF+KIdKKxlUleQ4CAmBh85PlsRU6TtHTdet6AuDnjV72yQItH5ks5HpibDCLZDRAhFZXNtR98PJHTusRKRQhms1jR9m2EDJudanMvZHZVSzvSWFqXykqWuDT/yk2+kns74pf/jJ5iISNt0vOEHvpO/8w1/n6/5e38fpTSnd+/wwz/4fbihI6XEB568TXSJQiV+8Pv/CS9+5JFMr1yu+aZv+XaikPzXf+9beOOPv4Fv/0ffhveeb/imb+Xuyck5aOj6rWNiDBzu5168k+WWGzeP+Pgn7/L0nQ1CwBNPv437H30V3/nPfwytFR9+19t595+8g7MmcP+jr+Ez/8oX8aYf/SHu7jq+65/9C77m8BJCat75h2/lF//lGzna5oLv7/+pazx4qeTQfCcfe/8f8/4/+i2unzSstx2v/dvfwuHFSyAVv//Wt/LGn/wJnA9UpqAqLdNaUlS5c/eBecXhXs3zJ2u2Q1a1SfDRp55BacuimnAw3cMLeOr6CZcOF7h4wmrb0LnIfFZz/6U9bGU5rCwvemCfvaqkOJizaxpqJFIqlj6yComrBxNQAj8M3Pz4DTbbjkdffI3DiWZaWnZNz2NPPEcX4XA6Jx5ki9fNsw13l1tabbBVyedcu8yssJhKEwX0IWQSY1GwXW1Z9w4XI7XVlMKwWzt613PnbMntVU+KkUpFjo8VbYqEvs/WsmqCiBFH4PLlq9iN4/bxips3T3jpZzzMq/fnEHqO11uOjj179ZQHD6dEsoJS6EjXeprxOX64mDKtCz72iWeJHBKiYAgdq82SYXAEZXj1K19GZRQ3nnyGSVVw6eqMdnBsWkfwEeUUhSmzfVjleIm2CjkMHL7kJVx64EHidsvtJz/BR578OA898jDTSlOmCWWxz/7Lv5jl43/M5dd8PsfxOm/7t2/nmbtnXDuY8FkveQApBX/6ro/Stw6UQhWabdfhW8estFTzigcuLui6gW4IXDiYs1cX+JRQQtJ1HdvgmE9nSAHN4HBRE7wgOs1sWuYYg9IYZcEnVr2j7z3TYgLS4IXirAWBI502YyegJYrIwbxGiYIgIn2EzW7grAu5yH67JqZIZRSyrEhCElxPpRWqMIDkpOlpWj/CaCIHixkCya4LyAFcDHRdnxeZCurKUkjB2dkaWZv8e5hULBYTHrhvn0pptMqqj1KSqhAQ8zlq17xg1zdKYi9DEJLTbc9627BqOiZVxeGsRhvBvM4uFNQ9S2Wu6yFB3w45B2fzYCcpiWN2EQLGKMpiSooZZKiNJRfUOKaLAqstpb2Sv2a8B2HJeUY/LmGNhouHC4TYR0mVF9ExIlNCmwJCD8BytcnU4aYh5x9kXt5VFXtlHmSczzDG7bbNuT6f61i0VihtESLS7DqkSPm/iWxt1UKRjAKbnTJRJPowQtBSHry0VhhlSSnDzqQ0iGQy26DOgMcYI64fxvoKTXKefsgDmjYKn3KfZllMkDITTL3zdG2H0pJJqbDasqgqBh9yzrTLlR62EAzO4fqewhY8crCHljC4DHf0MXJ0d50pxEKg1IAOeUkE+fftQy5FiSmfx2JMeN+gtR6hfSOcUCnKwuJ9trZqlfOZzvkcL8m4b4aQz3BaZbJ4Alzv2DT9pzqSfPr6//j6lIdFUv5QxxAZhg4lJJNpTbzX+5IghTgqSZE+jNRLGTFWUtix+0zm+gqldX65mPyC9gKSCyigshZBZBCRciz/LJUmkti1HSerluNViw+evvfngBvTabyHg6lFqUDTOYYhIdOInB9pVTFBVYBVAqstYRxwpSkojMi9P3sVwQEI0pAHlQt7CyZFTzNk/HNRWEprSDFQKJk3roXBjPJaCIFiWmXEfhC5C0nkbIoC5rMao3Phb3Q+dxjd61CKnhg9vesoJmVWIl3uwRLJ41NB2/qsTsXAZuOoCotSAiUzyCelhNGZ7ihIebupDUHnYnclM75dInEuIZKgsJoYAy5BUVSkkXyrdFZDZfAE7zM9T2ZYiBACYSQxeoaY+6J8VCPlLA9fSuQ+oRgSkCElAkCpsYso02F9TASX/fDqvFw2jjTV7P0XMhete++JDAgPdVGOJD5HGPv/lNKjupm7wazJX0/oHHQXMZGSzzlOla2FMYKQGikiRVEgY36g2rIiyRKjDX4YiClR1RUCTbvNQ810MqHQBmnkCz1HMX/PzEpizKS77bbNuSckk8Jii0w+00Jki7IXNE0O7BujEbIghEi7azJMShtmsxnWGJJ33OsjjAikVgjhkDLDCzIYKaCK/PkRQFkoUiKrelLigwaR1WOpMg7bmkwOtk6jjUKJgrKylFU1Pg8ylEmQS+mRgqIoqCYFly7ucfFgwmdqwbZJ3Hx2y1PXt5yjY1+ILo6I9kzJY7xPtcgI8iQ0RWW5enkPhg3GGBKJzS7ll1ECaRRaZEACCKLP+cfNkLHnSEaUd4eL6by/tO3c+b0YI9z5nBqjFa/69+tc98JI+kvgvMtgAfIBQ6SI97lzS5CttUJLwqiGTqsCa9R4rxoiIsMXhM3340h+ffef/B5PvPNtaCE4brZURcnZ2ZaPPvYR/un/8A85PNgjDB3T6R7WSo6OnuMf/J2/SdN5SiFJMvC1X/t36T1jxtrkLlrgqaee5sv/068ef6fj4Wsk8pnJISnAcmWpSsM3v/4reeSBA0QSWGu4dmmfsjAUQvL7v/mrvPU3f5X7Dqbs1xZpS4z3vOPtf8j73vUObp6s2bqBN73hu9HKcOdky1M3TnG957jtcb3nv/jB/4u9N+mxLM3P+37veKY7xI2IjKysrMqq7qquHkiKZJNN06YFSzRlWIBkQLY2BrSRAX8Cw0vDC628MOC14C/ghQEZggANECzAbE4SKQ7d7KHmIaeY771nficv3pPVtFdWAzK46LOqrAxkRt4b95z3//yf5/f8KYXV/F33DzB4pC746LLj7tDz9//e32cInvbYkWQufFZSMttI9Imhj3iVDyl6mhi7gc/uWg5DT1PknsjLu5ZV3UDUSHUkKU8UMMfE/vKWQzfhRbb8mcIgEhgt2TUbSispCk3XRcpCk5JgmhKmtJzuDLOHg3NIo/jaO4/42tsXFDLSHVucn1Aysa0K3n3yKFvy5wwViSkwliuM0jxaV+wKQxABpTV7HzFKUBvFdAy5iD4pClOwshY3B5p6xWEYUcdAUQhe21ou7ya0EajG5rx9WZGco9zWbKs1L++fcr0fuLwbqMqS187WdMfIizvPblvz+OKE01oSRaZ6K+HQO8UwRQIiEx6toSktTb2iqAom57nfl0zjTLGu+blvPUGEyP7ZFWVtOT8/4dgNDO5AigJbKLRORLLQZ4ymaRSzjMRyBXWNGjsQgbIoeXS2YlUqyvoBp699Ax/geDhwMnrawXF5c+Dqds87r51SmpIQ4MXNgal3nO0aJgJudMgI621N05QYAV3I7g6hdYZiiYCMMmckiaytYZgcx2NLSjmhXxUFhV2cIDLDOOaULapFYbGlXZwFUJaZRuqXe1giZsE3La6rkAeBKWWY1uQCMaT8XCryuWkKI97NJGEISQCS4zAxjRE3BaY5kORIDJ445YFnCp55DiQfUUZBcrTec3t/pEprpm7EuUgSkt12jZeBpiwptMJH6MZcCN92A/M8EqeQaahNiTKKCIyz59jPXN4f2SE42ayYZ8/sPFUR2KwyWRip8C7iJsc4erzzS549b8akJK+a0nLPT4l5zrk3sdQ6GDTWaMqywqhcBxZjfuY77wneEZMnpZAt3UZjTO6TnsaReZ6IgbxZc5l6fRwGjsc2vx8iY+Dz31FQGI33uUZmXgAzMUZiCChlKMsqw1fmMYvYpDwALsLEq01sWuqTfAxf/vPk8gw0xuC8JyHQEUjZlSdSQisyHTkESCmfrUTu1DRSIKRAKiD6XJ2iTKb6C4k3CjdBUUgKuwz4VjGOAyl4Cq0pjEJZRYgRScKWWXSTyTPNGVQYQmScZmbnWDXVcobLD8sYc84ytw9DekVX1QJSoCwKmrJCieXZLTOtNqZAigHkK4eVX75vtYAoxUKtzW6ctMBtvf/ZZvEv6/VTD4tz7yjLAqsUUel8AEV9WbrpXICYmJcKhnl0CLJSI5Tm0A3YokRJxXGYEClRFCVt1yPI3YJIz+xGdJ9QQlNhOR7v6aeRWdTc7Ec+f37L7BxmKaCvjCFq8CKxaXI9hhD5pnd519IdR6xV7HYbukPH/b7n7jjw8PE562KgMpYost9820hu2juOneHxozNso3DeMY4Zu22LkqKsiW6P85JVU+IJWXmRkroukTgKramrihgSslDEOLOu6sXvPSFEZLVes1tX2R4rV5ASXkjG3tO3PYiEsZrtpqHQIIWkqkrAQdIYY7h4+5RAYJo91/fTkq2IJBfRSlOVcHN3zDjrQiONYvSO9pizk2VpaSrLOLZYbXIZrM8U2+ADQ+yxhcTahFSJ4CRVU7HvelZVSalVVuVCZFNXDM4Tx5zhOj+psCbf1KKPJK+pixXbdWSce0KwiyqZKKxEyZJumAFHtTJYXeV/q4jUJ1tCyN2Qbo5MSz9m01QU5ToruxrSqJn6XNCttaCy+XUTWoOSdL1jP7TsdlsOf6N0uAAAIABJREFUbiaM47KJLUGLbIUZI3OIFHokJIUkU9ekKJaupAhLZ5Oye8oChKqYJ8dhjFid328hskI3ufzfp6s1YsHFr5oNTbPOFGCTQ+Lz7BhToCoDJ+sVVVlwdzxCgnVdYxrL83EmhYgk4ieHnxxIgZscdWEoy4KqrrA6H0BP1k0ujvcTyUi29UkmBMdclTKOE8Jozs62r7ijFIUlhYsFcB3RSuUhTWqikIgkcEv/5zCPmNJyct6QEtSmRElD9aYBl/jFr3+Tk9PHlPaU/+UfPs8HKlheRwEiZtDNMlQLwGowRuF8otw+4K233+a/+a//C1588iPu9lfZwqzfxeqScRqZwoxcVE7vMjnPOc/Lqztm53JnXEh0w4BLWYwKk2OeZsrCElJiWGBPISY+vTwgkTSVwhpJ66DrR15/+ADvZ55fXtH1E1VV0ZQlSggmP+PSESEEm7KGKOmOA8ehp2xqbg/7hQ4nGMX4peX2s2e3fDg5KqMxpeIwTJyWa9alQWvYrNeIGLg89hgTKFTF5HJnV5KKMUQ8KQNmI4BHftkztryj+SO2DOgCkiK4nIm5ur5le1Lz83/lXcLhyLMX91w8fMCDBYDgfeK2ved2P3G7P3C+W/G1rzyk8JH+eMBoxburFbPzSC+YZs+mqfilX/gKaA3DgK4K/pk9gZioDgX3fRaZmsaipOBsnS3XKQWm5AlTVri1EfjZE2J2J5SFIKrED19ccRwFRhoulwOfSIb74Lk/7nl+eUNpYHe+46OXz3l512F0tp3dtyM/+PAFUjhKYzlZnyBVJPYzY4gI6Rknx9PbnpHEW9uSQ+vwyfDXf/M7fPM/eIvrq2f4KHn+7J55nHnryRtsNhWrxnBsBSkK3jw55Y3dhr0ueXnf88HnLxFh5qsXG548POXjq5Gb/ZG7w4GdTUypIETJOHj0o4bNg4JfeesJQwh88OkztFKc7U747LKjLOC10xqjNfs+EEOPnzx/+MFL/vzDT5kmx8OzNbUo+eLFnqcvXtIPnm9/+22k0ry8vlmoqAE/BZ483rGpEvt24pNnl9wPjscXDdtSsl4VSLmmfHKBkYGqKfn+0xd89MUN7z+7JknJH374jDAnhn5EStjsLBFH8kDMHcqlDsyz57ffP1CWgq+eG862BU++8RjdjRwPHvPz3+Tk7ff4o//tfyL5mT/+Z/87nzzf88bFiiePt7z9+mOe3nTcdx3nuxXqgWUaO6wteHzSkIB2ztVEP755iQ8QYkIfW+a6Rq80Ski2TU1Z6CwgBY+bRozK5fPrumbynnF0iJQY3cxM4MnZKdviLJe7x0Ac4bwpiUIRVpIUPSnMoDTKrglhZmsLysJSFg0qDIxuoqpytdL1zcwwT/joaIoKlQxjHxnnjn3XI5C5KN05nBd4P+FD7rbNLQW5r6+qlv7DMLPanSCiJpWCMY5c3x8XQSyTebVU+BAIUrPaVOhpz+NHj2jdgZv9FXMEP2eLYmlLBAI/jdzeJqYRDm3L3aHDKM2m1hTWLmyDLNqN84yQUNVZcCiMhOg5P90tvY8B7/OgCoLUjkiZz2phjrjQcX1zjVIabQ1lUxJjpNYZQjg5Tz/OpHigKAuMNjjvcG4mRAjhyDBkYmuSiiQqdAKFyGJwgrafuZ56nM/vu9aKzXa3DM/HRThdnC4oNtstEHNm0yicc0uHYY6zWFNiESQf6WTudZx6T4xTjrmY3BWeUmCcJkLIkZV6yen5rDRQ1AahJHrRYUPIAEmlxCL+C0qr8UEQa40xdjlbONrDwDjMaG04f7DF2MxSmJ0jURNC5ObYMYfINPRopdBKc7bbLP2HueM3sjwnSFhjs4Alcv3cpqnyOZ6IlBLns1g7znkr6P7C+1oWmpBAqCxIC+QiYGdBxrlMkCUlrLUUpgT2P+1Y8rPr3+P1Uw+Ldd3gfS6B3Z1sMtmxn5gX9G9V6uxtrzf4acLK3KE4TxMxBM7Odmij0TrXYkQkQ5D000h0kVpq1k1BrBLOTRRFHkSTKLlvR1RZUpeRs5MC7xTJCxKKAYEOiVoKfu6rZ6QkaDYNQiT640CpFcM0M0+O9i6QXGJjJc2m4HgY+eLZAW0VbzzeosY5H06qmrZ1mMqz3pgcwB8s1/cHQjuzripqaSmQCCVIJj9EUpLMc2Q/zjwbjnjnOT1fUxiopUSIrLqJ4Bj7PV8cj9RlyaPzE1xMjN6TouRkvUFrUCqrWrMP7A9jzmyWUBcFploxzZ77+xtmN1IWNVM74fxMO84EJ3n8sGCeE71PKCfRY67fmNpcrO6iRMkJlSRFYXImUGbrQIoK7yPtcaauK5SGY7tHCNhs1lTGIOOrh0Cmsl2cnTJOI7ObKCu9ZIUCIeatpUgSlQTBWWxhl94oqGuBVCU+SnyUSBmIwuPniapqKGyJdzPORU5OGuZ5yS/q/JCPIZfMFmXDyW45CPhsgwg+h6ljSNzd7Xl+11NW62yXdp7SSPqhRZqadnK8uGqJSXJ6WnB3v6euM3LbuZ4QwJrcuxQjhN5jjUakAZFShqikRNt3lHWdyaUpEKLjxdUdRWlZNRuKIpcXd10LCIqiIvhcTXJxUSGGDp88t8cDfgokB7udRamCY98Sk0fZisIW+TDQVDjX0o0tYxiZ5xmpBC5GotSARiKpymrBWwcg0LdHqrpGWrJDIOVtACpS6pw5SAiEzlbM5AXz1CIIlHXOzNRVia2KZfYTpAhDiBxuj9iTmqZQPH58gkATk+MnlRk5F5NU/pmE/L9DioSk8EjON6c8fv1NHr2+ZmvfxLkHQKApi+U1C4xTn7feUjEMLXVVMTqPT5rJw4ubnkM3cnl9TRDZtTDe7em6kT/4kw+5vm253bf04wVSSr54dkthFa+dNci6ZFevqIWEKPABdusVD05PKG2D1BYZE2N/oJ17NtsVu82GUmnu7memNiACHI4TK28orco0Pin4e3/rNxmcYJh7JiOpfM3Nfctv/tV36GbPXTfx/Q8+xvuEj5EnD085WzUcDiEP1yLhQvpJ1nOpAlHLL0PKVmmWjcVSdMeyzn21ZGR/6Pidf/1Durbns2cvUELT9z+h9Qoc/RAxSLa14cMvrqnKil9475TtquH26BjHgdc2FW8/XmFKy2QKxiTYpAl1uuZf/akl+si3vvoVUmUxRe7K7LuRsevZNBueH28Zxxk/jhRa4yM8vb1kd/KAjS3AzyjT8Np5yF1oAQ7DSD9NjPue2WXKdQiOtp+4uR1JCJ5cPCQEx93tHePoqJp13rTGyO2+J+Lpbu8pVw1jyHkokXJO9eVxoutHLs5O+fVvv0N7PPBv/q/vcXayzcXmQuO84fnlgOUKoTQhCZ7v71Da8q+//z53x5GYJJvacthFfvx8z/d+8DnHcWJMgcsQMUVDVJ6zWPGImpAk3g3sh5bj2DGO8MXLAe8cRSG5vLlhdBGjGr711gX/+Lvf5cdP95gEb7x2xuuPz7k83nA89rh+pC40f/rnn3Bzf2BdFrz7+JRf+/o7bNZr6hPFn/3gC9ousbUVTVnz9sMtoxt4dtdipeTNswZdGD578YJ/9C/+iA8+v2VoHQZFfCX4qITUoK8zvVwvB09PdoAwg+c5UiV+uNWsG8vZwx1f22w5f7DlV99+zvD89zmtPYe55P1PXhKi5CtPXuN+3/PZ00uuhhYfI2tbMMeR090Wa2pGN+KXvPccJYMTNIWh1JrNpuJsu+Phwwu6vuXu7papHTF1ycXFKbMb8obG6Ezp3o8YmR0Z2gqCdFTWMg2Bu+MtQiisqRn6kSQkjx5foIhcX17i3MRv/LX/mDgfedAUNHVDWTfsP/2Yu3kgqMA4OD75+IooJQ9OV+iUD99j9KAit/cdsxdgDFbnLGRpNFe3e2xhMdoS3EwXPb131FZxcb7BGMP9zURSkv19gBC5ONvwjXcecFJvED2M04SvDKKwvPh4xrvI9z9+ySdPXxCixAdQMvHukwsenu1oyoqYJDeXtwQSm6pACri8PiKVRGqBLbP9fho7jCkYOsXtTcd9e0RqwXbV5moGF5imEb8MjlordrsTjDV0x47u2KJ0FsSLqsLOeZDRpyeIJLk/jtwderx3FGXmY4jF4uhiXk4MU+4BtMaileKua/HBY4zNDhsyTdja/PtTSLTjHu8D0zwgZRaOrTFLjACCn0nHFi0lq6ZGkCF7uVgpn22m2XG77/Iw7jOXITMLcrIike9NRhtWRrEqi9yz6AbGWbDa7GiPB7quI8U8dEYyvO5819BUauF8OA7dCEIxL+ezTJ4NFCjKEJgmmEfHPE8UhUXrPOz5EAlV/jmXQhFcZBwdbZddQWkZBlWKKKnRymRXmNZYawnJ4+dA9A4lJcIqhLBLX2pJfFU5IrLl2S1b2yTzs3kcprwZlgqhJMjEui5y/ORn11/K66ceFhO5k0brHOhNwOzdok5EtLFIJVAhEIl5kyUkRT5HohSE4JhdzrUVxqBCRn9Hka1uLiXc7PNmMjiSA2Mtu/UKVWrWVc2u0VkJiRnHjbR4F/Eu0BQFxmi0zjSxk/MTIgLnR0QSvLE7IUZPEo7zt8/5/PN72nrGGMGcHJ9fjRA91zJv9rSCsjK5A1KpnL0A5DARTcDIApAoKSnLkpU0mRYrJZNPjOPEurEUpeT25oBWhvVmBSkS55A3b0YRYs7/jW6EpBEpq0jKSsySYUPm7WsMkdklHD0pRjZ1iTENZyeZIDr7mZv7I+MkWNeC0QV6l7cmXT+Tome9qjPkIZEtpVaCygpRiPm4Oftc7HzsJqLIOct2cEzjiJCGUU4ZHpJy9+R+mJllj58daemQnJ1jnnJXkpQBmyIRjdB5u6WVxFrJYT9ji4EoIs7nHETTKMY5QJqRSYGIWKsAnwtqU0HelOStcHeUzDJQrzRFkbOQfTfjXEIZjdUKv6mISqNxGCsxqy1GS9wwEoVi3eRqEec8dWXZVBXW5uqCcVxgNiKxqhuklMzeY5TB6GxJLG2BCzFvbFcNgoRInhQj+25CCoESmhQiVktEnYc3IRJlbdht11ilczeiSKzrGm9yv9ngJsYQGWZPCB69b0kcsYWhLnIRs/cBVN7fDcPM997/jIRkbSWmMMyTY3YzLuYskULRdgMu5aEwJbi/P+RtgDUEH5iDzyXjRYm2hqE7ZsqdylCctm3Rw4BSCoTEO0ezqVmtDHVdc9cfuLl8+ZOI4pd3lKw0pviKbpMHn5BYfl7h4sGWb7z7kO5wSd8e8S7nHiTZBhRCYDp2CKmQhcbPM13IWeIoDUlITmpBU5asyx1JKoxWuLUi+ECKjqubIzc3R357VYKQfOPth5SFRivBODqubo/MzmEnjw+Z8KyUQQhPCDHDslJAaECOzIPPB5HZ4QO0x466tBQqW6PFghsffaCyJW8+eIgg0Y4zZ+88xJSe/d2Bl9ct27rGGMXrD88ojWIaHIU1mGXDLZf+rleK8LL3Bv4fLSXLa/2TO3nGyCdikvS949PPrwne0e6PaGGZlhnz1caltAIrFZXR1EZTl4ab+56umxmHDKwaa8mhq5kOLR9dfcH1wXGqQTSaa1oIid//7CMwipACs48YKdlWBVd3A3ddh9aSqpCMMXJ333M4zJRmoni1FfE9pIjrZuoyl5RrJVhvatxiVQPFbrtiu1kz+ykj6aWAJ+f44OnGQGlydgZZkHRkSLnyZr8/Mk0u02OVZJ4mRudYr2qON7d8vD/y/HbPg90JTWk5jhOfvrjCB5DznjlG5pC4HzzFUn4upCRFQWE0wxC4nR2zz0JXoy2vn1ScrtaMfqYsFI0WaBl4ettx03ekCFoK5pQYcShRgoNp9HTpyKdXmh98dk07ZxvaMI1MQ8cXfZ9pnErhkqJWgq+8dsqDbcOjiy2Dn7l9fom8kXx2tWeaZ+Y50A6efp7p/cBtO1BbwzhOaCV5cZ0L1ldlyaZaYYVg9BnCMYeZRMg2ukV0UhogMMwGaXIvnbWalbGopLi56gl3nvKy5Wr0fP/iA3aqgxSAvA3qp/x6mUKzVnmDuFIaFz3GVkSRBRAtFbpUDCJyYrfsasuqtFSNpdYFlZJErelthROB2Xl826KVRYhEiDBODp8CIeRtdGk0KEvX96QoKauKBEgS641FSoWNPU1V8Pa3v4Wpa77xlXMEW/q7A8fDnk+efsZDI3j46Bxdatr7A9/6ykCzqjg/qXAzfPT5NTfHDOj6pa++RT95Lu8PzPOM8ANvvHnB6+fbDITxHl9CURq22y1NZTi297z/8XPOz045f3BKdI8R0vDWGye8voO+E/zw6XNu90dGBLML7K8PRH3HZy9v6CaHwTItcZphDOwPA7MLuOjpp/y9uTnfbDygUo6b+9kTvUcohYuBeRpxLncgpySYxvzckxKqZUjq+gy4STHiZ0eMnqapqOsydwYi6fuJGCOfD5eUhf1yc5ULGjKdXaoMC0zRLz1/+Zua44BbOhpDTHif3ScZfAfRLYKpIMcfBAuTIZLIcSXvA4IcNwgBHJFpbokp5s5JctwmhPye5PNSJptrqbAmW0tXVR7YyjITWnXK9tsQAj4kQoy8vLpmnCbmKZ+ni6qkKixaSSYPczuRvFu2iZ6YBJOLC48hH4Oi8LjbfYbCyRzXCDF/bssy953qVOYqESlIyWNsYKsrlDYgM7U0zC5HjJRa+CSeFDN5NsWwvFwJiUBqw6sntwdSCDg3Y0y2ny+hH4QA1ZjlqR/znyEEWkt+dv3lvUT+0P27X//pf/SdNA0ZHlHX1UKzmpEyZ5+UBO99DusmQOZAdWlUtmpNE+PscDFlO6eVtPsWay3ZwRqxhUAkRVnmTYhAEBaASQr5wyGVxKgMBPF+zrY6wCPQSSNF3prk0HkOq6/qIluvrMlf7QN2VXJz03G2yRUJ/+S7P+DDZxO1HvEhEy9TSpl+FSaM1WxWDVoaCA6lBFWVaVlSiDwYa4NWHqU1SRicCxgTscby/MV9riLYNFkZT4E3X9tRlQY/TXiX2K4t61WFUZrgc8l4YikQz/c0LJL1qmK10kQfqavcjxeJOC+Y58D1Xc8wOjaVReoISLSxeJc7DKvVCqkM/TQzdD1nJwXbkxMO+322oiaRc3XO41xavPP5pjxNE2++dgqwWBVAScmxG9is10wuw3S8j8SUkMLkrwOM1lSloSg0bTdhraKqNHfXA7ZI1E0FSWGNZb3WXF/u8T5bCE1hWG+aXL7uZ0TKeTElHXXdQATnM19L5leN1WqF84FhyhnLpimyENGNCKVZbzYYK2nbnsNxpCkLVrWm6wcKa1FGIRd9xVqBNJppmNmuVhhtGKYBqSR9O6BtPlSEEFhv1lhrcv5FQoyJOXi8D9zfHoHEdrOiqWsQib5vM768avAucBw7vHM8vjhHK8Xx2NP1A/uuJ4hMKF3VNYfDAWM0q6bOw+JyEP3nv+zop5nv/KtsCd5uaqzJYBbvff4sKE1RFAxTz3ZdY8sCoy33+wNlYTNNNi2bPyE5HHs2qwarlgLiGNlsGqQS1KsGZTRSZ+Hk0+t7Zh/44Ue3/PiDL/jjP/5zvv/hFWIp5M1Pe5k3PEkgRB5gkhCkAO999T0ev37B3/kbv8C33tlxvP2MIpbYusyKtkg5g2sVSmi8DxwOHd47yqJESLEUHudeLaV1/pzKLITI6NFC4Uj5YDB4/se/2ZGA//7/yAPlPMOhm7jtRqSx2LLMWdVpRilF7xN3hyPtMDENEZUSw2FPP8xEadG2yHAeN7PZ5Oyvc57/8+R/QArBd77479jUDZOPjPPM6bbk4fmOy9sb5AJS2Kx31Nrhidwe+pw/KQr+6e/9iI+/uMbNGQaRM5mLlUi+2iLGnKdcspxCxJ/4UUWGFxAF1ijefvOM1883DN0RY2pEiAiZsFVBWWlkFAiZrdwyOtwcOc4TMuV7RSKhNaRgESTmeaSfPbUtGeeRdpiyEBCyvTUlATFQVQXnp1viONLUJZvNmnGaGaaROWRiaak1MWSr8luPtqzrgnbq2K1WTFPO4Xz1zQtWJvL8tuPZTc+6MNSNZfARKwTbyrCuNcMcGfWab3/jTU42Nb/7hz/m9//4R/xXf+fXqYEvPnrBcXRgDVpMfP3rv0o37GnbO26ev0CZFRcPd8zDQPADiMjUDsxzYnu25dn1PS/vjkxjYG01qijZdz3OedZVzju/2LdMUaJIyBh50EiUNoxJU1nFo12JtppnL9rcmSdFzqtHyXV7g0CByv2qwTmeXd5yeTfSTyOnFw85vzjn4nTLeH8FIRBjPrCdrRW71QpbKcpCcnc7cH0/YExJs8oOmbaPHLu8IQqzZxhmlBIUZZG3OFKS8AgEtW3QWmBNJMYMz8g55izkJKGYxwHnRgKGEAXRTWgJVmcYnik0inwwLYsCK8BZhwqC8+2aEDw3+xbvwRY5f56SwI2eoXfczCPRe0qlsrBhMzCuWJc5J5UWZpbzbDa5RsGHvAmdJs/x2JFIGKUxWiEURCmyvS4kjFRIpeiHEURCqoV6KSIXTUNVFNhK89obb/BX//pfw+CYP/seXdJMh4Fj77gcHe9+6+tM88Dti5cIH3h0WjF1A26KdMPA/QQu5Rqqdy/WdLPj+x9+zv3dnouL13j85JS+9dzfH5h8ZHuy5e2vPOSHP37O4dCyaiTKKOqoqJoCFxPt5Lk9dqR54va25c8/fsq+m2gn6IeReZxRUqD1YnOsS6wI7DZbNpsNo/Pc3NzlLZdUECNKZFInvDrziOzaiSGfwXxE6bxI8GEGmdApv9/GGIy1JBLTPOe8OfneL2XCWkNVVvTDyDCMdNOS+ZOCpixo6hohQUSAxDD2C0tBczi0y6CfB5yQXO6A9AFrDZvVhnmeOLbtIq5FhFS5K1Aq9JJjTCkufeF5iBFIJueYXBaQvHOZnwAYkzOsLGwILQJKKawxORcvciyiqmukFIxDzzjNy1Cq8D4TzYXIz6eUEkYprDWsNiuMlrnCY3/EzR4hFdqYLzeMxCzMaG1BZB7GPOeKt8Ja4uJ0IkGhFMZksUPK3Nda2GxxbUqbzykqA3rSIisqmSGMPoTlsSFyDlIq/JwHeal03m6GsOQ483tjjM5U/JRy1zSgtc0Z1PiTr8351Mjv/OnnP9VM8rPr//uV3N2/8wr3p94sDv2Y7YQhgnBoozL5KOYD4DQFhmHC1CCRzFOb1/pFLkwPMRO0kBJdSIYpsO8mCgTz6InOsdmWVJUFJEJoiNB3R2JKREcOR5Mw1qBNLlh3bsoH80ITjcKFEYUnRUPne4ielE5wPhD6ERkjKgT8/YCMAbmtcNHjYqApFE1VQlTMhSe86uKZwLnI3W2f80KQVSSdD14s25EYI0a+UqeyT7soJVYVzCP4EBDqDmslUkI3JTarGi0zXnldr5CiQCpBDJko5UMgCZnrHIgUVUVRGOraMo+5520KMzd3LfvWEaOiGwLdOPJ411AWktPNlrosiEaS3ER77Cg3DXMIHLqZ0kpWK0hR5ZzpgjYWLlLXFd4HUnDUpaVZFaxX9QIG8UTvGMeRQgs2TUU/C6RO9G1GykslUBFI2YKglGCePMduonAChMIW+eYkUdjSsG4KrM5WotEkbg4HXIyYosYHSdvOEHOWUJvcVr3brWGA4TAtVSMT69UaCYzTQIiJqighc0oJDoZxJCVFCJ5xmiB4ojcMzlMUBZMfIWhEEhhbUlZF3qQuSqnSAklgmnK25LjvESKyXjdMQ5+P6Vrjg0PqgFFgVNamV1XFZr0CJfKDxYecadCC092K/thSGktZlKSQCG4mFRrTVKxPNpxsNxyPdX4Y2ILgcgfqNAX+9p/1rE8v8O8GogdTK6QWHPcH8BFCfsD13mOLgqoqKKsKW1YUVYawjH2LFIuaLiRd24EPnO92SEFWIYWgbFYUJxsCERcjuij4888/oR0m/vm//BM++/QFdzf3QMh2tXzryr8GXtFuXv1WQvLg7CHffO9dvv7OQ07qntuXkcoIytLmXMmcMyG2LChWNWJ2pGO/FA5HCmOXIcrTHodFhVZLhiR/ZDUKUxvKumBVFhgzAomHDyqIoGTJG1IjrcTUNcI2uHFCuxlrFMcQue0Gjp1jf9Pi247r55Z2nPAmUxNjWCi7BkIUjOPM17p/hBKCi20NyvL9j54z+Jlf3b7OzU3Ls8uOr79xzusXJxxmIMy8/9FnHF3kvXff5vRsw+tv7Hh6tc9grr+wq81K+aLWxtxzliTkkpK/sNqNr744kURkVVneevSA+85idZ2L31IAI9CVJk2JIAzJKMZ+ohs7jC6pbUEg0E4T++PEPE9s65I3Hu6whQRpGPqR+/uW0QUO04SLnkIqDIkkJIObSNPEepWJes+e3TCNE9vzE0yTgWV+8Fgt2VWW0/WKs3WF1YYrt8cLqK3irDa0XcDPHV0Y+PjlS5SpebiuUXFmmgTtLDh54xHb3QnNyvL09sgffu8T/su/8UuMKQsOI5rV1iJ84OHj19nfKYgjfWG5uNjxzW98kz/4kz/F+8ij8xOaBzsOx5Y3v/4OJ1d3bF/ecf/8hlVpeH6T81HSKJp1QaUUiZpOaJgcaRjwPnDXOVJZ5eGpKBHKIOKeqrTshxFSxAgLXjIFh6xzXm1TFDx9esWv/JX3ePbijvMnb/Lg9YdYXRILz9AN3B56fAh4Dy9vjhzCQKUlVlqC0EgSiohICqsU26ZA2ZI0RippmUMgINDGUDcaY7Ogo1KB1JLTTc4xC23ywVtnp1H0idvbO/pJYYuayUWM2BHdTEzZMfT6xYpVZVjVFatyRRgDf/b8c8Z+ZhICYzKd3MUsSIiQBY8hSdwsSa5HC0Ft8zCrBJxUde7GGwPTkjHz00CwBhkTmoRSktqUuGJmmkaszi4JbRSyMIxTiRtGpiEPLNZYQnCQAtZqbFmwqVY0dUFxesL6wSNikkyHe44vnnNMFQ9Pdzx8cMFXmgeM2xNufvQ9nn7xglXT8M6Th9zedNzcdoxzz6O3n7A+Oc1WTH/Zqq/kAAAgAElEQVSPEZKT3QprJL/w8++R5Mzt3VMSjvPThocXWx4/3vC7f/Q+T5+95Ne+/YTf+PVf5P3fe5+nNy3Pjntu9gc++vyKlAzBOcYxQNQZIGYrnJ9YlxadT0sUpWZrLGfrGlVaJu+Z5wmhNEnKfO7Sy8A3jbyCqgkkWqhMm49ZzPchMM4OrVOG2sW8gWN2+JBFe1Lu1VRCoE2Rt5HSMU4T41LRpaTgdLuiNCZv41JCa4Gb8ybx1RVDzt0ZrYFcLSFNhjhZqzk9O6FrW9p2jwv5fqlVht/4ZdAJISxiRCLIuNgrE+M80c95KaGW3J2W2UW02awWGBIoN2KNwdgiWz5jFgImn4ExN7dH2rajLEqqus4bz5QwUrJZr4jRYwS5mk7CNI50XcvY90QEZZnFZ+8dgoRVBqUVIYkvITFaarQQ2X3hA+Oct4JTCiiRWEWW4Rxiyt3g/ZCzlNZItNIYWy5/VhZN85Y1C0FR5Pd7jLkKhGxIw0cWkKHCWru8t1Mm2Ih8xssAPUFMuS87xsyGiPFngJu/rNdPvVn8zf/wl5Nd7AAp5hW+myPTmLG6SmZbxn46LlSFkFVJoxHITKrLBjKk9GglMWXeOvkYMyUzCqqqIoQJrfMGcd0UixJlkApcGDKdkMXfGi2FzrTVwzCyqgr8OCKVxTQlIk3UWtFNkXmK2MJQ1SUKj7GSQzuQolj6+iQ+erwXSJ0VGEKCmEmHh27KhDVDHlhSVl2EyvaKcXTcHw6Q8gdXCsG6qSkKzfXNPZBpcSl5kIpSa6SSOCGIYeL8dM3+bsRPAWsEzdpS1ppxcPRtQCrF5qRgGgNXtz1CRoL3hCCx1QqF5+KsxljB5c2emMBKjRGWbpq5ujsweY+bJmYfiEEglEWq7I/fnax5dLqisoLtOhemjiFx2B/oupGqWnH2sGZ/P1MqxXaVh9jrfY8UAh8tpdQUWnG6K9hsGm7v7zOFa7E2XN8PfPLZc8ZecH624bWHWworKGxB3w2kEFmt1jR1QwwHqqZiGBzeeUSC892WaXIYo1FK0I89/ThRNRXTmLe6pc05w8oUSJnoxp5xHglBoqVEqkyrzYXqAW2zhuJmh5IF2/WWtx5X3B9G3JRpY8PcYaoK5xxT3yMFrOtNDozb3N93dr5hs6oxWPr+yDDNDC4XtYcpIRWcn51DSpSFpLCa3nkObc/dfa4rePTaCU8evsX+5o623yOMQtkMA1o1Va4s8XkQb1bLINe1KKPQWkOSaCVxY6QsFUqRe72Upj04ur7n7OKUk/MTko8ZRBFiLqKPHm00ycVc6qw0tcxFuu9f3hIVPHp4ji4NGMkPPn7Gy6sDP/jgkqublo8/ucRNjhdfXOf7RMpCUhYc/1/3HSEgSZBhoeVlxZoo2axrvvr2Q/7m3/513n7rgndWCuFmpr7NHVG2QJIhC217RCrB+ekWYxR/9r0Pmb3nwTqH/fdjy35/pCoqTnfb/N57R1mVDN3A7B1Sa/7X35gIMfHf/k7D/f2BoR9o6ordrsEt1qrCFFSrFUnlzVhRWExpqesCGT0+QPKOYrVDFoYYHWHwtP09CosyJXa3oTAKd2yZheHOJ2KIiEPH/vKW9UngpNpQ6pqX00yxWWPjjEJgih33XeAf/M//kB9+8IL7wzH3PMZs6wlxea2/nLzTT17rv3gtpF4hwBjNf/brv8Tf/a3vcHn9EQFD8gpiIhKIVhG8w0iF0YbKKKq1ZVOtc9WO9KTgcUOgXFcooxhGR9vOHI+H7GjwksOh58XVDXPImRyxHBgu73ruDhOkmbabubrvqQrFu289oClKvvnWBb/4jUdA5OnzG7wYMW7DfpoYQh6khhnOGklA5fu+SPzRB8/5uXcf89aJYX1+ysOvvUdVlpzguP34Uz6/uuZHx5mVaXj34ZZ/+4PPGOSKd959zH/ynScwD/z2d/8tnzy749g71tWWTy+PJAOPTzV/67d+jdfffocXQ8UX7/8Jv/xr3yL4xNRPfP/3fpfD7Nh/ccvjrz7i7Mnr3F7t2X/wEd988jpX7Z7eC6Zo+JXf+i10e421El1Z5v7I/fOXXE6SB6+f8vL99xn7AVlbtH2b1dZwum0y3XI+8OLFC9789n/O2B744sMPefb0kh999DlivmO126DMmnlKPFhbGtfyou9odmd87c3XsXHmhx9/wnf/zadEAfW2IEYBKW9HIjljrGXe4MSwRCFi5O7uHudz2ba2OlN+yYC14B2gM9U7RVbrCqMk66Zhmmd8DNkWt+SVnHPMc8guidUaUsrdy4pFWBUYUebhQia22zVVVebC8gRKCKIPuddRKfZ3t9RVQ1kUjLOn7TsmFxApLdsxlu9XIa0hySz66hTZVA3toc1OECFyj3JKbIoV5ztLvV1Rn+4QCDaNor3pePnihi8+/YyNVbz3c+/y+CtvUm3WXN/2fPrFgX/yj/8F7zze8PV3HrJerXj3yVv4GHn6+VOU1Tx8/IgQEx/86CN+9PFzAtDUJeuy4I03H3D58pZPPvmU7XbN17/5Lk1dcnt1yYDNn33naeqGf/ov/4BPX9xx1UeEEjQWSguzF/RTFuuncc71HWVBYTWVyUTNs90GqQR9P3N9vWeeA2fnO7SWdMNxqUHIZzxdyqXKwZECWFswu0wiP7ZddokZg5aK090mb9EFCJXJ92khYwbvvtxKSpG3w845Ygg5JxfCT4Y5n0mn2mhIkd3pbunx8xRFuWzX8gDa9x2zm3nw4ByRH2/sjy1X1zf4tDhNhMA7zzAMZNEMjFaUTbPA6QK31/tlC5bFNqVztEqpLAQ2RaY3ay0plqhSkoIQA5MLTG6m6waAZbjTGJHw0ZOkpKkqysIQfd7yeg9zgGM3Mk0TZWHZbiqszhvY/FoIjFJLL2SiHXNncp6BNSoJTJHrOTZlgbGaqjDYBXA2zhPT7AgpO+8ub66RUqCVzNF2IioImqIBHTNAcYpIoRnHPUbXGUaZUv4gCck0TUgpUSo3HUzTnGnvUmXeQYJ5qdLI8ZFcMZRiJsh+9vJngJt/39f/r5vF4GEM85f+a6UkggFr8iZQKYsQil2UuBC4Pw74EKisZbtpWDc5KJtC5PbuiNKWafaEoLAakBGSh+gwpl6sZtkXbsqSw32bVS6V7WOKgFGSzbrKkJXgmcYRWwiUytbW4XCEFBAn2/whnB0mCHyc8XSUZc2D7RoRU1ZghWfucubI1PrLbdd2VXFWF5yuCvphYr011HXBzWEgBklVrrBWE5Pn7uYO7+Ny/k2ZgghUamROinpVUhs4Pz0lzjPz5ElGIfFMwtKYgRgFSoExicJWtNLRj3f0buD2eU8YI6IoMCmhJSiRMDHkTEQ3cvP5kZu7HmEUpVCMbgChKMqSqiwpy8TYz5AUxpQ4P9PNE5e3iZt97plrCkujC5JMTDHgQsSqgctDQ3AD8xzRMv8cjNOMC4lhmhZiVuL05JSLB6cYRpzz3A4z7Rh48fKO6D3ruqG/3PPs7sgwDFR1gVGZLLYpK85Ptnz9ayek6Dk9aZBCQoqURmFVyr56bZBqRURxf7mnWW+JfuZ+aIlBsV7VaKsYpsCxixyPLVrlQbYsSxDlguQ2VDYiS0sIMA4t3/2T5xzvJ959+zFNU7KpLSKA0IpJaWIUrOqGar3i5eULru/u+fxmz8lmw3tvnrOqJGVVU6UVg/M8//QlSktGORCmQDtoFDCOR7QukF5yGHpmd0PwGhkDtswdlCKAMobGGHo3MrmZ225g3x4pi/+bvTeLtS3b77O+0c0xm9Xs5rTV31u36vb2vU7cyMQ4DpZNgoSQAg5ConkgvPDAUyTeQAKRAE8gkJBQMDzkAcmSgQfSQmxCiONr+9q+fVNVp+rUqXPOPnvtvVczu9HyMNY51w6Oca6EycOdJZV0du2jWlp77TnH+I/f7/sswzhgj/Tdw65neKOjXtS8FWoqZVh35+QkuLr8CBBc7XoOzqGUJkwOFT3a1khV8a3ffYeT0zWmydRVA8YS8bz5+TcYEvzOb32Ti4trnl0d+NKX32UYA/shMPvENPpyinecRD9/OKQ/aMOYOUa6nvcY8/MvMowT7z14zC//0t9lvWr5Uz/+Od5+7Taf+dgdFl2NSIl5GEsEh2WJZ5uKWhleeu31MjxQCSUybX3K67duMcyOKQRkzrTLJVofFTc5kSX8pa8uEE1L92bHS+PE9bPS46uqCqME3pWhS5gjUw50RnO9vSaJjNSKMIWj+wt8uiDLzKI1LGvLkEDiaKxn3bXomOhzgQrcMgrlPb4WLF864SY4om6hqritMrObqG2FMRXffviYr37jIY8fPnnRQSwxcEF4ETF9/v7+Y4aCOR836iBkgY0d+p79MPDpT3yc3TCx3e5JsdDwzs/OkAhaWxeFiozUrWHdLQkpcrndMfQDSnhMHDCqYb1ew4nkcmtRxvKLT/4l4kng50//O262A1JXhDTjvefe3TMqqfnw4pqbfc/iWU9KcLpuePOVFT/8yZcQs+P9Dx7z6NmByia+9eADpNJ86s27nC9bpt1MGz3KJB5utnzwdM9+DFw8ueCzP/LPcv9jb3NvYeHZezzb9AXatbzF/XPJw/ce8M13Zl56/WO89vZnscrx4Te+Rt7v+Oa3L/nye094th34sc9+jE+8tuLB3HDn/gmLxWvkvGbz5B2++eXf5os/9mNcXl/x6PEFv/6Vh2xutvzQG6+xOr/LN955xK/+6q/zydt3OFn1LLvA8v7LmLtv067vg7/ErgqVcbp5SHQzb3/mR6i7ipoCt2jXLUqeIbTHdBbvE4/fPaC6M77+G7/B9vIhv/nV77J3mvX5PT77euKVNz/NK5/+WdykeP///J8IfeTP/os/D7On3R8wgF3eJq0+RnADNu7xPrDdFWDQ+48fk0jU3QItEj4GNlcDMWfaZYPJmqbu6IeJq8sbYijwIaMN2qQCAZKZ7U1PUpqbfSDEwKEvn6/FalUGVc4xe4fWmswFJ6s1ldYlvmY02miE2LO93jGNM03blk7q1KOl4I1Xb3N6suCl84ZhGqnEKUZXaG1YL+HO6QJbG7SCaSq0U0ekrhvazhbyaErMQ09tKrSo8EnRtCuWzQohHE1jcH5gGA5cXt5wdb3n9GyBoPTbbt2/RasUn/2TfwIfZn7r136H3/3GB/z6Vz4kVIY3TcP9j7/O4mTFcrlAW8Xu8glt03H56CnPrm/Y7Qaa845V13JmaoKL7C72dFZztl5z6/YZAsPDR1u+/t1LNoejnmM6oLVkP82oRnPLqqJsch43JSbvGMaxKKqOAQMtEpUSrFaLo1IKgi9qodu3T9BGUzc1OWdWJ6XmMM8e7yPDOCJk0Tloq6mqMsivK8myrbCVpaoM8+yoa42W5V4TQmQYJ/b9hK0brDaEGDkcihrHHgmv7aqlqqqy8XTFeWkR5Pi908SuWxX9Q+gZXIm2X13fQIosmpbF4hSyYd+XiGpKiW6xZnc4lIFhSkiRMVqVupGSSKXIMRXgzTQTvTtCCfVxlllUYTF4yLBcr2i7msvLZ1yPx3jlsZNnG0vOUDc1WhU3oZIFStOkjBRFyWKU4jAEtK0xVmFiYtEsMc05tTEsK40UL7Ih5JhwztO7kpi5t6hRpyuELP7Cohwpv0u1KbC/eXaMsWw4UxbErAk5EMOEj+kYjpOl1iMkWRi2e0/EkwjMKZNTgVFKFamURSLI2SMpnfWUZQFzxVgGA8Rjl3R68Yw3Sr8YbqaYMaYoVn5w/dN5fd8niz/1hR/Oq5M13rnj5McUaA0F8ytVieetlqfM88RhGBmnyKH3IDKnp+VkRArB/npCyopMmf5YW+HcjCSy7FqgYIX9XMTZlVU0ncboCuZMcIGmrclS8fCjR6yWa26fnaHrictnM+8/u0LmxLptsW1H12XcMLPbTzS25Wx1wpPNJf08lwy6BB8TGcP9+7dKVHAaCXNkDpox7RDComRFCEVyfLJec/t0xTjsmecRIcrJ6XrdYc1zNLInp8AYHK+dr1HKFgmwH6jrlvX6lBwirt/RNRXVUnF17Xm62RJikaafrlru3jnlcnfN4yc3tNWC++cr1NIiCBwuB1IQnN1f40fPHBxZJiptCKNBV4JxLD+P9y6vuOkdH32wpR/KidwQA1lkFsaglSCK4hQ66SzWSqpqQQiFYBZzwo2anEayUEgDWsK6tjQLw+ZyYPYzLnhGpwgxo2WZKraLBbap8fMWpQIv3X6VTMKHiXfefURIGp+OJevgUChOV0vunnX8yS+8hQ+Bx08vOUye22cLGmsYJsd2N7JoK9567YwsMk+e9uQsuP/yKc+ebOj3M8v1ktVJx2azL27NuiFET1srGmuZppmgIMTMzVXP1HteuXcXbRMhBc6XC77w1qssO8OTqyuCUEhp2N3s8G6mD4LD4VD88jmzHQeSUMgsuH/ScbJqiNowTz0/+sUfQitBf7PF+5mz89JpIGliMEQyNzcXVLVlXS+YnOfp5oZxnHl8fcmtk3POT044Xdbs93s++OiK1aLl/GyFUhIXRv7ynxtQUvLv/o+SOSTun625c/c29998g5gz26sbxkPPoyfPuLjZscBzenqLk9MztOoJ0TD2jkpJzu4tSZ3mP/1v/ia/8zsP+PCDS1Ion4XnG8BCVy0TfpEg8D1ozR9yKyqAmxd/fr5Z5Ei5k4hsEEJhqojRFZ94+3W++Pk3+Vf++S9weyHZbS44HPaApm1amqbi8nqEELhz+4S6sThpcNGS5My4uSRPjuWqRpLYHiKn6xbdtiAEFs/NbsDaDgnMfuD87BwCPN4fjh0PWyJMwG67IYaM0pYoXelnRIHM4HIgJGi1JUlFihPBe/7Wu/e52uz4VPtNlusGi0ORWZ2sitpjdZ/b65ZVa8B7qqrGJIeyhktX891HW/7Kf/bfcnF54PL6ACIfycaURMcRhw7lfcy/J6714p0/urFUearzkz/+ef7VX/gz/Oxbt5hdUbNICUnk4u9q15iTBRiB0ZZpf8DIFbrrkF1DFIIgIAzXuO0Of73FSI3QGiky/+bf+EmQkv/6Z/538I79ZsPq7BZ116KtoG6XOLXg8OwR0/VDtLaslvewQhCE53CYSEEgl4ph0/PoMJBudtR+pG41zemaKFr6MfPeg0t22547r6x44+23ePvtN3CbZ/zOr/4KDz74iN+9KF26GCO7/Z5nzy74j/79f49lnVieK54+u+Lv/s1fZ3t1SdM2PLzeoozgL/7Cz/P6536IX/zrX8bMM595pcMNI3/7b/wuP/InPkFz8il+9Ut/ny9/9bf58Y/d4bNf/CQ/8fP/Au+9+4T//q/+Eu+//5Cf/ok3mHYb/tJ//Je5dfsNVHXKb/zyf0518gk2uyuWccOn3/oE6q2fxp7dZXCZd770txkevUvYPOaH/8Kfh+sbfumv/c/8xu9+h2883OCdY103DCR+9s/8KF/89Ou8caflE//MT6HMLb75K7/G+7/1a5ysb/PGz/0cL7/5Fv7yfVLYYk7ugr3Pg699jeuPvs6D73yJm63jS7/9HrbtGEfHOM04lzHGElIsPfYQyVFQ1zVCFAiG0eXPi8WCw34giwL50KLcJ5zLKNuwbCxGRuboCZTecmU0XdugpebQj0UHFFLpiLnSk5pi6ZTlmMihDDJCgkPfE1PxJDvvsJXljZdfwU8j3jukgXpR0y5ahv1InIovum0FtTFMbi6xwZxZLpcYpXjr9ZeRIuGGHSklxmiQBnKKOD8zzzN3bp/QHyIXVwNCZH7oUy/x1idfQ8bEN7/6Du89vEJV5eTpC596DRUD7eIUl+Af/vo/5NnNDd43uNnhoyuJCVPzle98wDS48vVUNg5vfvw2P/WZ+3zwdMPf+q132Ow9MlIAfyIXH7GQVKosvp878IIPxJRo6tKvyzlTVxVt12FtVZ7LTdGZeTcVUraPgEBrTUyBeZ5wPpRKiSgbIF3BcrWEDNubG/zsyELinMPUdekD4hEETk/PIUFO+QUkzqgSh711tkYqwX7flyF2UxG8I0aPPAJWPnp2iVSGRWXRlCqSMRXa1qXbOI4Mo2PfD4SQWK1a7t47BQTvP/io+A2VJcXjyeo8IqSgqqtyuukiKSZMVRNjZJpHgiuOzhgytq6wbQO5vH6pJZWBtlvQNDUhePY3O05O1mQy01QozIKSVMo5I0UBdHnvEVrx5it3uXO2Yr2qjhWZ4uR1fsQaRWtbdmNhHERfev2xCKDZHnZ4H0ryTmu8T0QfCMEz+omUE1aVSP8QIzkVUKOuFWfLU1LwhYcxlWesjw5jK5SJxBhxcy6ni1kgawVa4qcMk8fWCi1qsshEHHMMhWaaC3FeZEE6njgKW1EpASHifTqyMIr7sfxTYGRkwXc/3Hw/W5IfXP8E1x/ryWKZlEQyAUShJ/7etLGbPc45XJBkIjGEYx45kSJMk8IHV+AjOaNUYp6Op4yiZMZrY8i5SMS9y3hfnI0hSOY5EAJkn8kxYcsJOLdurZBSM3iHDq6U03MqpE2jESLw9NnEwmrW3QKhJEMc6JYNTWeZpkSIkYXKtLbilfMVg5uJSiIXEqkNvVM4n5iGTFSWj71SIDQywrJusEoce1GatmowRjGHmZgCRlvudB0hJUIuJWhFzTgFmiYiSRwmz5gUdytbtBGUB+E4e+arPbshQMqobJhj4snmQBsDk3MM1wMqS7wsKGSjJd1CM6dys1OVoWsFKcOtZcOqaVlLw2GaStk/yyItTq4Us1UBhhRYCy88k94HQooQKrrWYCqJlOUmIYUk+kCSIJTGZIGyx7zPUYqugkPOGYNECFviGTmSUuDWyQnTFPApgzDEVAi3SMF+CrzzwSWCRD8MnJ6tWXYdbV1k2nVdoYUg5wrwNJVGCE2lNC/fP2VcO7RStG1FZ9bEnDgcPDHB4VDQ+AjQqvQRb58sqe9VkA1ZjOy3E49dpLaa83Vd3gfBiw6cMjWpjyxunbFYNEgEs58AifOBx0+vePDompPbp+Ss+fJXHqC1oK0FlZLsR1d6NRFikGitWHaF5HZ9KJP+HBPWaO7eOkdpQ8iOLDTdsuX1lyN13RYqXM6EIF+Ux7UxaCNwfmK338Gjj46Tv+Jwun225ORkiVVlyl5Xljg6Dt6TRCBVkm998JDvPrrkq7/zHk8+umGeyutNKRUENuUh8Xyz9wdGTv/A6/n3/KNEtFxKhTmRjoOo5BIhOD54+JgYHKtO8bGXb3P/7pKXXr1F7Ac2Hz3h8nImiA7wiGvJcq6RynK5u0bqTC0zyij6yZFTQhjNwc2YnFFIxhA49CPT4DCVIgPbbU8lC5gppMD+sCv9GCSjcwghaVpDVdfEPCOFxUiLd555nrBGM4VEFAYE/MrwzxFM4qdfH1FSIFNCa4VdNsg4YnWFkSWwH4xlyJLoLTor7Mry8ktLTs9WXGz6QrUDng8A8x+6Of+DfwJKSLrFmtXZKzy7fspuNzG7Ea0VqqlwfWDOe/qcyTLTVjVhmrh1tmK9XlA3LSFlPJJGROI8MO33IASnqwVIeSQjJuZ+h8xFj7Lb7hmGkdoK5m5iCluS7xFJEx2M84Rqa9zki4BcKXJSmNpwz66ZtGCzcVxNAfFspO4MRlnuvXrO7ZdX3H/9Jc4WFtFfge85vbMmW8nyzXXZ2IbI1M94l1g2EUFAiIqua/nEJ+/jpnOMtdydAkpJTk/OqJXi1WXCdJKFgdBoPv3J+5yfNHh5zeuvnaLrz/H2SnPvbEm+fojoN9w+qZHhNq/fPSedV1g9IfQOrODBw2d0Q8vpqWR9doq9/0nU+j67p+8xjiMfvfMdHr3zLo8ffMD9n/rT7J4847e+8YivvXuBixkpMpOPrFYtTRjx2w27Cj764Irp8CGbzUPq22vOP/5x6qVl3F4R9nsON4+IF0/BfMg3vvxlri6fsN05vMt03QKtKxpbaM1SlK7aOM2MfiblTPTlBEaIAjrTWmGMLlAzrTD6KDDPUNcGbRzLrsa5yNPrPd57jCmsgegz01ji6LPzKKWwtSUFXnhqYy4deSnB1sUzt2gbmrrUPWKM7Pd9WZMIhzSxpG5M0QC4MRJ9ZvZlc5ZlTfDiGJUHKNqfkBKb6z1CJJIvIJbtOKN1RGZBJBOzYNdH+t3M2M9kEt95sGHTJ+Jw4PryhjkLWq2pETx6eoXzkRhu8D5xuZs5DBlZAZWiznWRriPpmhotNHmxQFWa065l1dW8c7ll13tuLdcsbCRlCeK5j0+X1FUsay51pC4XmEvZ8Dw/PTRGl42iLOsseeQn13WFtQY3O4beI4UqlZ3aInXpqoVQIoQ+RaaxaB6aSrFql4WemRN10+JjKM+bLBG5wAaFEihpqIKkaVqi98VhLDSrrsRI3VyYFAnFPDucc5AougdVNlxoQ5KSfpyYJsc8e0JMCARdtyjQttEzz46QKGAuOHoEM0oW2JkQZWPjj8O05P1xIJ5BClSlqTuDkAIfHCkWCJsxxa9dGYnKZZBxuloVnYSUyPpYZzlqvWLpCBBTwkRDSoltPxJCYN+XDXuB3pQNdYpFHdePx7hozJBF4X0g2E1DIZ4KiZKeJCGGhMgJa8rrjb5wEKyRCFHioVJl3OTKeDZLTFVOSUWUKE1RZQiFlqmQUV0gSVFir6IU3YWARGCcAzGXykqhvx6f4TkXMB7Fb0kqHI9MPK6xJFIWRVuMBc72/R1d/eD647i+783inBzDfioPihhJKVPpqmTIRdkseh8Y5iKnrkyBuHS1KH1EV6SviDKZmv2M0hofIv3kihKicozeY21FSpFMpG40AsNhfyg0QynJOTJeO6LPnKxqsgiEMDD6xDCNWNtglCno52lESUulKyqrGJxnt+2JMWKVwuoiOM44hnHi/Q8uSKWmWDoTRtK1LUZ5WgNSVJysDM4Ftoe5TPWyxuVAbTU5QsgFEGGURGvJsm3w0dPPDpcSRkuSzmibUFKySAvaumYYArfOVpysl4xTZH8YceNUipho2uoAACAASURBVNsiYauK1rZonenHGTd5NoeZGDOzzAz9SNdUQEeMEaE9UdRsrm5KsdxYlp3h1TsniGMJe3SBFBMX1zu0tTS1JXrP/jAhlGHXj2ilsEZilGSaZr714ILTs5roEvu9ZwgBMWdyOp4IKcGi0Wil6IcZpQq51AdH8AGkJoQblFBIYWjtAmsCWZQonzGGnDNt2zBME/vxcJyURrzz7A8D03Q8sWxaQvRcPNtTWc1i1SKEoO8dXaewrSG4QD8MSCXofeTipi8xRFVOTiptyFKWIr5PoDzjtGUY5+PEEL7x4AkSwd3TFZWFfp65Okwoodnv9mhT09UtbVdxflbjXZEQP7vp6UdPMhXr9ZKr7RbIzNNIDBFTLekPI0oLztYdTVXRfPwe46HH+RlbVSzWHTEFGBRPnl2zPwzEKJHC8NIrHXYSuLnAnmbvmLxBENkeBqzRbLYzH14esI82xOhZLDsWXUdrJEZLrvxMvNgQp8CyWzAHx923X+XZbuSv/a9/n3/wf32d3X4iRfkiMiokx2HQ9wZW8Z8wtZCP2PtyiReHi/kFWq14swRFbLy52rC5uuT9h084Oz3l1ddf4k/96Cd55XzJqyd3kTER/J6qrpmdJ/U9wV3x6Nm2QIoai5IS7wKzc7x+74SEYtVU/Ic/fyAj+E/+tyVuGpkcSGtBTQw5slzUyOCZgsMhGZzEzxHvBq63BxZNw8XhGbVa8Mq9u5AS49Qz1JJby3OGQygQAyOp6oqzs455nGhNR1c3tMsaoQLzdY877Bh2nt4XkfRmpxEp8uarK1aLirdee4n33rvAWnN0hIU/MH76B/40XvQXy0JZSsnqZMX5rVPU7imqMSgtUJXBdAvmPEK2GCTkmWnYIaYB3zv6sCPompgz4+xQy0WBhbQWJ+Dr3/o2SI0Lf7os6IeJCMeI2oGp9+yoUF3g6mpP3VQYnZF4qumCZxL8EInB43Pgeogsz85YW8EQA0+o6GNklUFMA0rN5OwI2fHhI/ju/hrrEzFDEAJtOl59+RZtXejdOUpQGn91TU6ex+/ccBgmhqChsqi64mxhsabi6fXMs903WFeZeYTLmwmpBPas5RuPnrCd3yNKTb2yfOg9z77ziG+/+wxTST73qTtk7nFv2TD6JX/vV/8PultnLO/c5vJwQC+3tLffhq7jwycfkd/7kK//9t/j1t27uMM1UxzYS3j821/h/ceXrNYLfuKLn2bRVmSTOGtPySng/Y7vPnjM+x/tGX/zEW7aISpF1a740sN/QPOlrzAME9KP+N0F3s3U7ZqD75mmRMotttJIZdGVZZxHQg4oLYhJ4CgiewE0jUFgjhoZSd+PzKND6shy0aFEws0TIUNtaup2waF3bK53PNnckAWcrBaEJIjJMw8zIAq/IEWa1pYumC2gjf3mwDD0GKNZr1fs9ge0mcg5s2hbjC7y78N+4slmWxbIQlIF8G7Cz0WXZGtLzplnNz3TVP6fCImUkuZmRmnFux89QwpB21RF6O5LZDCmkkDyIXCxnSAKqiNl+J0Hl3ztm0+4urnG1AYQVFqxrC1CxeOdTKGPfb2cBbhA01RYQ6lDkLn/yr0yHBESXSvOlw1uDLx3dYHRlk+99QZGl0G9lgVIlwAhJJWR5BQK8Cwl9vuxSOaFIB9fp4QXnTOBYPIe74su4aRrSU1NPsloo5nGgZQSzXEgdDhMxBCxncVNDsgsurooo/xceqi6xApjWBBSxNYNIRYI3PXNHqkzRWGYycIXIqlWzM5xcXmD0oVCTi5xy0ppjJDEmPHH+KKfJ+ZxRgiJUAaLKnAXXUGEq82BaXIoY0EK3LEHWTbWAlJmmhwxF/AiCbwvHkApyj3RGElbtzg3E0JAKklXF5roetGQc0SmMjQWxvJse4XUGqsNQghqa4vKg8I/DDGgU8LIAotyEbY7T1NZMvORoqoZ5sR1f413A7Vt6Nqu9PxSRCrJreaUEMracPAeKUAbSa0bRBZMs0eIhLUVIQWMEqyrGi0Uw7RHak2WzxOBhaIqk0ckg4CiiLOahKBddaAUjz/c4FKJllodaJRCoGhMXf6OBB8jMUeUsqWKMTvmEBlmR4wJIdQxZj6XTXksDA6tvu8tyQ+u/4+v7/snsz8MFNixxLl4LBZLTBl4kBMIZVBCEhPEJBFKUplAEpEUElKXCZFPjnmKnJw0GF29KDZLWZLZQilMVbw4IpXVo3MBbQRGlS5DP/RMg8c7z2LRsFzUzIVZdRQmC8LkiUHw8v0lUmT2vkQqiJLtdo/IifUiU9si+d3cDAyD56RZYhpJzJ4UErdPz7A1xZkjM4d+Kl4dApMri/IYMz4LVBqRMlPbctNJcea6zyyO+f/DNFFVmroq8nkhBG1bc7uzfDjOWCs5rQ3jnIpfrFKsTxfsw57dfuRW17Bea55dD6w6w+A8hynSLptjfEzigwAMRnliClxejYCgXZW+Z9cWR1ZMkcM0IiVlgicFtZWo2nK66kBqbrYjq4Vl3TU0teLZzYavffshQnREisdRxITK5TSkyMATIRdNwziXzaKuK3JOTOOMJKNrfUSxK/ajozLiRYfK5ExdaVZNhRaBqMrpZx8jm6sC2xGioLzXC08SieShtg0YiZDQ9zMXN6lMX4+7/5gcg8tMTtBYuLWqSyQmC7ISx6lyOT1rapjGzK3b51SmQIYOvWcOiaY2xJh57/GGNGd2wx7vJSIZlouKe68s6A8OmTK3VgtOll3xkyrBnXtryPDw4cQwTrTdOSkNWCU5WTfUuiIhmEOGpDCqAqkY55EPn15xcXHDbj9z6BPjGOi5Q2MKrS0khwszzq2RMvH0co9WgtkLfMosF4mUI7spU20dFRGZ5uKJGmfC5HnptZdRSvJat+T6cc+339lyeROoTKEgxpS/V40rR0YvOnC/Z8fy/35DebFnSbzYKAqOcKzn33TcPaZAFM/vXoLtrmd3M/Deu4/YXtzw+c9+jD//536S2+sTwv4djK44+IkYPOM8gEqQFYdhJuZSj56nkd2NxtoOrwqhTSqB1AJlTIn3pEwQR+fWoQfvicpAVROjgByYp8B2v2NnKx5dP0GlGqvAaMluGEgmcdaclWjT6IgxIHLm+ulHJO9R1iJtDaFDWcPmasc094xupu9nhmFmc5AQM3pacbZuaYxBH4EKMX6v8fn/uP7QzfuLXTkiOUg7lssKu7AI1ZQFZaXpmgHTrFitVwgc26ePkNNEygHnQWmL0hLtRiqjMUYhqxqZBN9+/4IoFFMIVFozzBmRI7bWWFOGAHunCCFzmCMTDqMjVglCgu2ux7myCHHR8XQ7cuYFOx2ZY2LrIGaJWQDzyBgC1zdb9uPA3j2inxyLqiK5xJQyqrLc3Q+sKqhVmbr7HOiqNUrMvPPgCZubgd0hg1KcrCraytLYiv2iwrse1S652vT4mNBacHF5zePNlr0r0dYUM1nXNMCqucXZWvLq3RZrDNe9Y3Pdc/PhNYuza85uXyFNGShuBknc7/Affou82fDtBx/gIlQnC27fO6XpGvYXT3H9nk+9cc6ybfEx0UfH62d3ebq54rtPDwyzh901l8/2ZK0JUuGmC66fPKZdLDj4iBQJmWbIsFoO3H3trDhuhwnvNdM80CG4vtkxzhNRCJQsSqzsyue3a7uiJoqBylQFajGNNE2DkQofw5HcKIkBsrRcbDYM/UylFLLSSKPxx+dEImOMIcXAPM24vcPWmqpeI6ViGAeGYUIpQxYTN7siQZ+8p20aaqtJKdEfPM3coctcGasLOTLHGdtULMSKjGS337PdD/gky7pGSJpGU2lNih4poW2Lt3mlLFJrplDSPPM4sIxrKluxqgxaCLQqMfyQEzkG9vsJlSVjV2Pr4qGr645KK9LsyQhcCAwuYWzxENemQqiaSGKeB4QX5OBxsyJlSSIziYgQCsiFGSAVSUAWgq6rMFLQNU3xvDqPD5mUE+EIv5KUkyB5VGCEVCB5wXmGfkIbVeLxVuPm4nqcRkVMFGp9ypyeWbq6BgFSFVBWcIEcIrPPWFOxWHRFZdR0jNOMd4Fxmpl9UVnFGGkbjU+ZMGd8iAzDjNQBbQy1rZBCImSJj+aY8Dkzj1MZBCaO9NFC7JRZMruREDPTXBy4whTgzTgNcFy9kssJnkvH5xayAMZy8R9KmRFSoMo0FFKJSdvK0taWEMqwRAh5HJgmYpiZ3IwInmzLJjxlQYjl858pWpGUIm2tOV0vkLL4lgUQfakNxfTcRe4JKdMZQ2trUk4oLaiMoa4s0+yZXCBk0BlqXQCF+z6yGxzrrqy9xyGQDShrMap8toWW+FR6jFnIAq0SopDtUznlNKkk9xa2KsO0kJiToNUKU1UstD5+jkod4znRVMRych1TYuxLv9X7dDxdlfgYIIcSVc0JzQ82i/80X993Z/H1V97IldGFumgKGGJ2jrLQO8pSpcSIWFwssUynaquJqcQDpZAoocokx0VOzjtyDsfytaRtV0TvGacRY8piKKRCSxvnEpM8PzmhW9TENDOOM1k01EZSG9hsbmiahpfvrEGCcxGJwtqy2dhNAY2gkRLbNlxPE/tpi0iJSjRIA/t5YF13vHb/jAxcX4/M48DgMi56rJEslisG55FDRlUJLyFniTEWNwZqC4tWEEOmspZhGJligpxolEZry/VuoDPFdbMbPTFnXn/9nMN+YLs9FO9QU7PtD0UQ39VoIfHBcNiNfOGHX6HvB7777gX7PnBy0rJcNFxvex5f7aisZdlUiJgKoCMktKroaovSgX72BaRTNQghaKpIrTS2rgkpsh/64qdqLJNPzCEXqEq/5+G7h+ODIpMzzCmRVEREQSU1Siic8KRcNuIFKlsEuX6aEVFwbG2TUyaSj36m4oOzVcJoEFGTciRRvXB5nq+OWoKUiMkxzRNzACmfqzyO7j5Zpn4hJbSy1JUlpR6y5PxkWRxRSoEUpOxp69LjKqBaRZiLx297GIk50baWurZ0leXeyZKq0oxxxs0zbpRkEbF1RWNr6sYwTQPTOGJkTbNYoFpFv+uJ40hbG26frbF1RRKJp5snuJCoTYumfCaEkqisGGfP482WcXT0PpMC2MpwetIwDYHPf+Yeu/1ASiUis98e+B/+rdJR+Zf/q54UI9vtAEqyHw5kAVJVCCRWKU7WK37s828wec9hdFxsrnj57hm7tOXXfvM9/pe//o2yqE6uPCSVKYvG8LwLVya2zyf0Banm/4h3o+fUzuf/Kg/v8lh/fupY4o/FHVjiVUbIguzOgSASSmo+95lP8MUf/gT/zl/4SQ6Pn5TYtq2JPjL3PavViqpSuHniMBwgZT643mOriqfXA3/1XyuLqH/jFwPn50tOu4pxf+DZHLi9OmEeD2wPAwcP2lrOa8mtVYsADr0/emETV+OeVbdCJMHF5pK+P/DxV19BqbLY+ysf/tvEGPmLy/+Spqu42V3TDw4fNYfJ8eRiz6pd0DU1SpbObG0FSM12AucC4bDjS1/9gKfXB0IsPaP4j76n8L3N4vNN/fNvOXYWIaO14s/+zI/yr//Cz3B49FWEWZGiwgXH5AqRMibQx/edbGlWLYnM9uaASL7EsirDxUdbssiEnNjfDPzWgw8QKL7y2n8BZH5u+A+oG8Wqq1l3LQjJ5mbPZnONMZpKSqojbdDrmsMciHFmYRRdZQg6MQ4jaS4Jj0cXN1wfDgWTj2TRNrz68hmLTiFmV+LAJrE/zFxtekiJrOeygEsaco3RCdRMlg1KS2QOLJqaxdkaMY+IrBjnwMNnG5ZLS6Uq+mE+Eqs13cLio+LeiWJ0kjlIWhkwtWVKxaF5c93jXMKuDK0QkCsSgcRIVS24urmhkSW8tR0FAcH6xLDbJyqK4uBkueTdxxcsW4vtLInM3Du2u+Mpi4IoSxtIkli3K1zIBDKV1dzqTjj0W1S2jN5zsbtimiZSyMxeYo2gawQ5qzL01YpVW2OkwBqDNYXuWyKFmpCKL2+/PxTZuFFHt1vpSQmlj77bMsDcbPckL8pgQWlSgt0wMPY93nuqpqbpGoQANxfw2/NaRMrQz1OJu2WOC3ZBFvJ4OhSKJkErTKWpq4p5mog+0LY1bVvT9wfmOTCOgZyhqRUZSVW35BTJMdFYzeSKAiLlzOyLp/Jk1UJWjMOIVlDXmrpqMFahRIl9tt0CtKZTCVtJxphIogDk5n3P7DxTLN7Kk3ZJazve/+gxwziRQ6kZVHVDSnCYB/ppRGZVBqNGsu4KNVyIiJtLLF/bcoK1bBflPnQYShVm0aEU2CrTWE2MkmE81hmsQZviPRynkf1+hxCSum4Y+p5KVzRtzfPeYibjfInbO+8J3qOloG1qMuCdO57EFd/f7Et0cnYJ5xJuGkgpHB8PkpAylW2Y5gk/l5PdLMpJZ9dYqkqX022RSpqGUsnY7l2Jl7pyAqir6kUdwsfivi2E1YSPRcWVRC6k3aNaQmRQQmMqw6o2mOPpc0bQtvZYB/EvbpnGGObJo6TC2vrFRjLGsuGZQ9n0OhdpKv1iYqePG/Gq0oxHzUjTFH9qSp7WWJTSzDEyzzNhLN2/0buyibYNy+WKeZ7ZbnekWGLmptLcXF8Ts8T58qw8WVpqFXEhcjOU4sb5otwPb3Yjt5YNtlNc7EfSVN5jKQRumoHMya0VOZUTV+cDpMT5smEYHdvesR8co0u0dcVbr66ZXeLqaosLDlM3RDLb/YCUBqQqrJJxJolCWK2NLp5J58p7Y2pSLIdNUoLWiscXuz/aeuEH1/d9/bF2FoHSbYsROcHzhox8vvAQqZw7VoacI0aXI33vExGBQVIbixKSMM8oK+j74j8T2iByYp4PxJhougKLEVJysigOpf1+JMUSDQ0pUNeG6CashKqWVFYzuJYUMns3FWLUfqa2NekYeW2WDUqWRea425KlZFEvSDkyHAZ8H3j11XsYWchOOSVqnQsqPht2Y9n0nC5qTrFUpxZdJXQV0cZwdrbm+vGBtjGcnFn6wfPkyZ6P3z5hTDOD84xTJHmBazV375yUaOSDC9w+4IZATIq2WR7R4pKFaThfLrl3a4VRkn6OPMqZ8XDABQW0GDXRNooxRUQl0UZxGGc6WeFTINuGwzTi/I7FouO8rYpewpb4hhszt1crDsOep5fXxCRYLDrObndsNhsO/cBuN/PwYiCS6JYS7wUxOLSGpV2ChqnvyRTr66pucG5GiTKly0kQSRjbMToPOaAqXch5qmyQpKyQSiKIeDeVzkz0uFAgSUKWGLGQBcqTUmQKjpASjViQs2S5KLGlnMriPKVIXUusFYiwIsaZnDxJ6LI5zOBz5OpmW6iD7ZJFU7FsE03XUduKkDNTjOz6mZurnqePr5FKoK3EVrmcTFSSw82OlG+wtQU80UfG+Qb/UcLFEuNdtx1sez64uEGqCltbYnJoKcmyZ/ITJ1WLlYY+jOUE2wUkgvsnJ3zw4SOeXg9sD6sSa3o/USmLTgJBpK4UXVvTdQ1f+Nw9Zue43t4gpGZzsSGmjEuxuCnXK6zWfHB5RT94hj5ysjS88sYpv/x3HvDeBzckyvRTZIWSBd/9fOPGsYtQoKb5iBjn929Yfu/1+wZVgqMEkOdgmxfHjUdUuTjCbnIWiFyE4Fmk0l/K5euqUgiReefBQ55cbvjRL36OW7VGzRv0PLA6WVPZAi/wLpBipJ8diMxbH3+Z6AL3VmtOumtijLz1+ilSSLaHgSEk3rz/Em1bofQClQVBd3ipkX6is4Jx6LnaPqQfJ6Q3eJn4yoN3mCZHEiUdsV5fYbQmxUROZWH4xivn1Oslq8Oa8TAhPPgQ+PzbEhMVKkuiCngpGA4j14eeQ+hZVJJaWCpVPGBFg/G9t/GP1hd9/l5nhJDoyqJsx8FJRAzE2ZFiIEsY58QwOi4utgXwUBnqpuWN189Js0NFyTyNXF495qOrA1JC3dUIrfjUG68yHEa+psok/v7dJcu6RhvDfp6Z3cTsIkLVnJ6dcmddocn0c2TG8PKqwVaKMI64KdA1FW1t8LFMrNenFcN0itQdwziScuLOaced0wXXVzcgND5HTs9qXr9/C6OK+5boGcdIP0UWS4G1GiUaXHQlciYFda2ZR5gj+JQ5axs+8fJdDt4zLzKrRUPdKiY344NhZRObG8e8dQw+crbUNHi6puX+2Yqc4Wacefl8wTx4tFVUi4qbpyM6a14679BKsZ+LqmLfP0NJx80NXG4nLi4PDHFmDpFlFmghiAnaxZooDoXsmDgKwxVVJVksikpn9pGb/TXDPNBYjY8RIRVVW2OyoTk4hIRFW07omlbjXKDtFoUILhKVkoisWK7WIAXPNhfE7Dk7a2najnkoJ4umkkW/IyS7fqYfJ/a7A1ooTNty6GcO81A2GuOhdP9NRYyZw25gGEdCSri59Oa0MuSUOQT34h4SYzkVUsaiFLRWY2RFZwwVkRgmrJFI27FsFuQUOO1OSR0Elcgk0hxLBx9NSrJAu8gIo1E5omOiMTXGGm7fOce7xNQblouas7MlcY74GJhdgeQRM42WtFXD6Ceu+r5E/BAon3GhvGZpDcoWuubVbiBnSXN0Ee6GPXVVoZWkqRtEBltp5pyRMSCSBOnJOJCSfooMLuHmgKBs0CIwJKiMwprMOEpSVlxf7fHeFTVZijRtjZClIiQVWClplg1Gm9IrC5F+HEHkF4OBm+sDznnOTldUzbLc08bA0I+cndRIrcjR45wnhEhtLQu7LERbpVBSlmiprYr+SYHUGh8l49jT1vaYwIEQS688psQ4OvwcMcog5QIfPTGlo78xonzEVApblc8RscjthZKEGI7Po/J8koBSmkoXJ2Rbd2ij8X7G+ZmY9fHZlLG2KVHdqWxSlT6mXqTE+8AcyqYxpJIo0ErSdR21rekPA+TMerkuA7QYSb4coOSc8G4u1a1QXIgxBiqt0NbQGE2VAknko9ZL07Ytbp6IsTy7F3WNUgZkYj+VQe7pqkZJXVRzJG6vWlZdw24OjD1UMpFTLkm8W2uGw1FzpzJCJpTJWKloreW7D6+QskKpmlUT6TqF0Zl+cLgsEbotKTbniC4x4/EpsNuNSFVc0c/1KDGVbqdUkhjmwjsQuXztH7dW+MH1//v1fW8Wpfzewu73gRRymcKlmFAJZlEcPEIkpEgvvj/FhDOFwBeDRytJTOPxdy+VGMfxv9na4GMg+3LKI2VmmspUEwlpCuSoSalIpUOMEBJaKcKxgyYQ+Kr8so1uLgCdlEii5P2zFNRGUllLIkH0uCyohCmb3Nm9IAka+3+z96ZBl213ed9vjXvvM7xjv9130r26mgUaQEhYYAqBTSLs2AwuhyACETGGOIBDYggkYJOyE2K77DA4ATMEgokqUI5JHMA2iEGGIISEGAUauNIdde/t4Z3Oe84e15QP/92N7HxIoqq4+KBd1dW36naf7j5nn7XX+j/P83s8SlligSnIgqcoXDs4wFWZomTaZUhztg9iSWIzCRPN6NBODk1GgfcK1IKDvQXWKqbr+/SLicoKhvquqmqVJk6iKtbeYazYC/bWnhyh8Y4XPXKEypnjQ892zAwhsL+oGcbMyXrFFEZMVdH2PbtuwFrH2hmULrNqkOW/tUwSm8oRouQdtu3IE8/eZhon+jFwdTXJQrXSjFEJ6EMrSslYpckYUkmUlLDZQBELRir5XhG4VuCtJiMWGmM0RguogRJRxctE01gKCq0tigjItNDXNWHqKSmi0CyaJVpr6qoixciysdTeoLSl7yYKiuXKCWikFVuO9Q6lNSULfMcmhaIhlYzBEgO4lSGXjHUGC+ig0D7Tp0yIAvYxBSkejx02OyHRZhmolBKgKKYgBbklGwpSAix5DSBHdl0U27J3aFcYQmYXI9GKZXmIE3HKQtu1E9oKBKftBkqG528lnPGYoskpYC30Y4Uymg8+1TJNgWmQoUk/TmhtCElsSSnvKDlKn2QygOUF9z+A0oUnHj/j1q0tioKe8y1qto/cUxFLmQXBj1EI1f+dvvn/eN17Xtw9NH7s+iK/4B43tRSyyvKQU6CLohRodx3truNdv/kR3vipL+Ulh0eU0DOESYYizovKGCamSSa5h3v7nJ+dSz55zhHtL2uMEVuyiw3XTg6hRGIYRTFPQSAceaKpPN5bDg/XmMphY41rNNE4dt1AChlrPYfX98WiHRK2l77Cw701uqrJBWptyUNgmCzGw8LUVNoRdYLKkWNh0265tt2hCpw/c3uGWNyln949+vGvqYjqX30vP/Ytnw/hRmkq7zAkfFWTiyVk6c2qlxU1imUdSVNh1w1kldBGsfRO+iaT2JNXywrf9lKIraVDVEXJspx075GhWInokshd4mqQQcjUTYyxsO0ngWGQGWNmTD3brmW9WogVbtezXjTUy4o8W5u0MyydQSuLMTV5zonnBH2YCDkQcVSNoa6kPFthUbqg0LjaUtcFZyu8rVHaMaVIP0yQoFp4Kgx15dmrKw4P9zBDx9QXvDcyENWejKf0LcaAbzQuO5wuXI4TqmiyKaAKaQp0/cTF5VY2hXHBZtPSjQPdVGGsqFlRZYZxoqSM85UA5bQSm6/16KIIUyDkRNU4lgtPyZkcMhSNQaNMwVcOp+Sz3UwDzmhWjYfGc3QkVmNXLGGUzyKicBacVWJz1pBSpA8TvVIYZYiqJefM+eWOKYxY6xliIY6iKDaLGrQixUiYZGAGmlIEIANCTVeqwN1uWBQxJFJMxEk2w1XdYJRGK3nOrJ3041pj8M6SstyvlMyqqandTF/MkaSFqYDSrJtGKnxyomhFVALOiioRYsBZyRfqAmOcyFo6GyutWDYLjHOs1xUla/pK0VSWRWXpiyKMUiw+hQA5EkImhkwbevoxQilYCnW9AFNoqppFIx2WwzgQc8ZZ6ajUCMROYhxKLJMKitZ4Cgtv8LXDVA2LnEhToR1FBdOlkOIk4BqjWK1qrJY6Lj1HhPQ8eBNgkZaf0aQYxRJoDNYYWWmL1EooVWSP1GkeYQAAIABJREFUlYpAVJTCOXm/+r5n1EaUu5S5bFtyKXTdQD9Jt7BzE97I76m8QHZyTOyGAcjUtcfN/16lxRI7dOO9fKGanysxg6sctmiMtYx9pig7q4maHANKg7XCHRA3WUVMhTHI51DmwbFWRYrjlUBxtFaidMUwmy3kfhN6aU/fB7quFw7DUqioIcE4ToQomctUMkQZDBZtSWi6aXbiaKnksHrO0FozE0nzDC1UBIS/sL9Y4L2l5ESeOygNMmijKFLIcmcoUWhjCXgNlbVYo6lq2VPpEhmmANqQyFRWcW0l2VhvHauFxxpFPwzUjXRGWmuE2poLF7uR7TDywFzjcrW7YsqJi93ArgtMIYk9OCWGcaIbo9R2oHDeUnmJSZQi63SKacYoqTm1otBKzXVo/9+3C5+4/s1cH/dhMSLFqKUU9PyBFyJaCQEtlyIVFDGhtCYJQB+DQRdN2gWMHdFarIaSPUzEWFDa3iOjjUMk5O7e5vByFyg5IF2OhqqaF7Qw4CpLb0dKJxQw52q0KrS9ZAF9LfbFerWkkEg5kYomKyVTOwWVka2WWa0oK8X5ZseicuTUy9RHW46bo9naoyAbTrdXbKfI8WpF1lJ0GkLi/LLDFENpC2ansc6jneKJ8w1xEEqUd4raKgqG87Mt1ikWjWO1WNCPA94b6YTShlpbUrBUjWHbd3RdRDuFNwqVjUxM9ysWdcPBesnRKCjteP2Aacq4hYOU0Sqg9QFdF2jbgT5CLoYUE93UzcW9O4w1PHRyRCHz5LM3efK5C3LSKC1/h/tWFUVlui4hrhOLorCbBgiGKShiyoKT3optOKtEIpNmtL8qyOFQi3pstEJpQUznpDHGUjdeNioxSPAkJTnMojisPLpExkEoa02zwmpDNAFtLKkYhmRxyswVF9IbFLJiiknysM2KOE1onah8zTQm9tYrFEryFTFIVkZp+m6QCWHtONlbE4uinXHbtdMS4J92TCGzbJY4q3HaSr4kRKZQsFphrWEKkWF+/b3VmtVySUqBXGbFPGVINV6BMomjvSX9WLPTAWs1Y5zYv3bIfjkg9BNaS/nuFMQSZ7Rj14685H+4SeUNv/esYOFdFspZ1Jm95ZKV98QQudxuAcXZ6SklO5ytUe4+nnjuiu/+77+flDLf973/kLe+9cv5zu/8Xv75v/i5j1ENMzGU+bByt65hPrZ8jP3xrgbZNLU8tLMok9p4tNV0M91N3+2wShlU4Ud+6Ht572/+Dt/7D38IlKwVaj5kf/obXscPfN930TQ1Tz75DF/+lf8xZ3dO0Qp+6qd+gbPNxH/yJa/j+PA6L3jN5+BnWt8HfvPnuf3Y+xiGyJu/7BvYOzgkZcHw//rx/XzZc99BVj0Pv/R1fNrLPpPVwQnv+ZWf5pn3v4ebTz9FNoZsa1ICrxTrtadpHKW4WenQuKrmdW98FHLh7PSc7TCyWu2xMOK+cJdi8aKq6IcRnQqLuiJ7h4+J1PYC07CFOxc79FVhf2+fG0d7PHTfIXFM/Nrzp5RSsFqyluVuGOnuuz1P0u8eFTVQMPIZKVFttVaQwFhH7Sxhd8FHn2vRVpNDovYO11hOTpYYBdYYzi5b2mGALCpQCJmr3UAmsao9Ve3ISbFyHmcMN88uQGte596O0ZoUNZcb+bz7EGbq8sg4Rs4vdvK8UEJP7cNE1w5zL2wFKCoDfuVYWof3loP9JU5rrq42jCScsWx2isurlnYaGEpiz+6xnVrOL2UqrxFblHWiPpTLyGY7oM088U+JbhCL/nq/ZuEMlXVkLKfPnOKLqEW5RHKJLHzN4eGaUjT7a8/hcc3SVkz9yGbomKYJZQp15TlqPMMYuegHXIhMQWMdnCyWtFNg6Lr5NStMcRxWihuHa8lYTSPLVYXVltPbl1yESDsVcmo5Odqnn1pMVWGUZRwGxkngL5WDojJ7yxUxRtaNo2kc670KiyYXmFJF3wVun+7E1j9p0Jru6oqhn7ja9aRSxEYYAjFE2TAqTS4Cu1qvaxZNwzJbAZr0PX3bobXi8GiflAu19Sxqy3ESKrhSBxglgLw8Lx1VXc1VBo4QAttZoVNJah6qqsZ6zRg6jBJYVSkZNbMSpqxY2BVTHEhJwEiSD+ypvKMyjpw1rrIsvKOuNJVzOO/IysBscfdz/9sQJ8I4kLWSP3dKDLc2JCx9inSDWFW10uRpQhkhsa+cHCysUZzcOCJTWDeeynnaduRq2PKih69Lb7UVF9MwJmyzQJciB8cMp+eXeKNYrhsoCms9rjLonPHWksi0bc80Rfme5DT3Hmo5HCLwwdrbec1Q+FoGMCFEupLlOZIz45iIMWKtqO2qZMI40Y9iGzTG4SvLMExsrnbyejOB85nbp5Jn13IgyBmUEuK5N/NQ2BiU0mzbbt4vmtk9ktHakpNmnObnr7d451gulhitBQxjFAsj4MRcMtZYKu8xixqjFX27leotXYhJ0dSilKeYmEJgGAMhByEyR0UMmWnYMQwjR4cHaGO4uurph4GYElPIcm8q2dsWDdMUyOGPHB0ysCt02ZBzopu2ONtTYsB7R9u295RiZzWTloFxTKIIx1TYjQPeOSpXi4k8p3kfkoizC+D86krs5shamPSIUoXGLbDZUtRM5M+wqhwxRSadWPnCw/trSlhysWuZSmGIA5vLkfNNxzW3ZLcbZkKsYRxGQpi4fljzyANrVrXnvWe3udVOPHOqIEVKSnNLk5H5rlIYrXBGejaFKTL3eiLWXZCHkXfCrshJhKO7EKBPXH/8ro/7sBhypmsHkduVTMeUEYUQXebSUoWdewuLyjNmWqY91hq8qzFGQx4xWoMqQiLVsqmPCUKAq11H09R45zBFrFJ1syTGREzihVYmE1Oh5CiUzBRwTqbtl7szoFB5mUKull42BkUOJSjDZtcSU0OmMMfFUcayG7dU1jKlTD8FYoocHMmmy1mxCIxxYiwDm/4Knz05FXLMkgXQAgFZNw0nRwdsFy3l9hUfPj0Xi1ZV2OZI30vRurFg0Sz8mmodMbGgdxZnNY3ThCFzcv8e3Thx52wHylBVlhurwq07PR95NrJoPMfHC0o2Qu+jcHrZgpfDckNP4z0lKcZ+IJqayleSaXABpaAde9qQiHnBqq54+QuucbBoWO0fse172nFkO2SSTZzd3JAmTSyJlCcurwa6UVFZKNkSKfRdLz74Mkq+JiCqnvIzsluAGHVjUfouVVMCUmOaxJ6kNLmkObuiMDmwa3cYLcphTpHd9pKUCl2QPXLWVqywgHMKozPVlVQjeOPxlWLYDOy2LbVTrJYLul3A9AnjFcYlTJOw2rK/f8DY3WRMI9E4scgUqKbAVOTgWWm4//oJ7TgyRQmqX18v8Y3ndLPl1vkWXTRKK8ZpIJQoxcBFs16sePiBQ4oKhFC4fdoz5Mi69qg6sbe/T4gaUmbdwOk2sBlGQkrcd3yDw8MVqoxMIXK4anj5275WNkwho1SeF2lFiIn3v/T/4H3r32CvaXjV5Rt5+H1/SjZAygr1MAi2e/mcY/s3f4N3/fp7+IZv+M/5ZvV3ePQrXslXxm/kc8Jb760HBfi18ov8E/NDJAoP5Uf5a/m//VfWjDllCAp+rPm7PK+fII2RP9d/Ja+f/hQqKsGjq7uZRHiGx/lO+633XuA7448Dir9m33LPtfq2H/0BvvIvfx3vfOe7+bZv/Sb+znf8Db7qP/oGsraM7YZf//V38vlvuMG//5e+lve/++d57Pd/j4PjE978ZV/PE+//HUJU/MQP/l3uO97De83DL3st1/70m/lIukkfFtx67mne+97f56Wf+iaeeOI5nnriJrc/usEsPCcPLFg4z9hHzs5aUim0faTvWhwJXdV8Mpa9dcUUBk4vNlzc7jlZGZqF4esffRvKGK6SIoYoGyFrqKsFjQFd1VxOE2d9x0eeeZ5xc0nl9zk6XnF8376oqP0ovVtFDoT3+EL8EShIlMM8q6+FgiGrTFHiJCjke5/SOCXaduC33/cs+4c1K2+ovGMYwdYykf/Qk8/x3HNbdjnhTaGqYewzp6cdIUb2Fw3BaBbecf+1PVxtsd5w7WjNA/cfo5Vh2HWMXYfWmvNtRzsGYoykKZJyoVl6vNKkKRGTZ39doygsFzXOeTyJ/X3PjRsrfOUw0TF1iU3Y0U2t2LJqxJJpPN5DpS27fsfp5Q5VLEZFKYJWilQEfPH8rTO6PnB6viPlgqv9bIcTaJfRGlULDOvIO9b7NfuHNTlnuu2Wpq45vHaAVQmjM64SO9j9QySSODg+4GC1hqFnQnF4sqR2FXurJV6BVZ7n7lyy2W1RLnJyuKK/3VPIDMay2Y1sNiMH6waDkt7hRc1QHEM3UpQMDfZWHu88OxW4Ou8ZQ6a1AaMyR3vXyKEIYTsFdtNO1JQkFVY5FIZuYowTuqqlw7CLhCkRopR9xzQRo5AlcxaqYwriBGpchSqFzcU5YKRyaZSh7YrEcq9mb7mib3fEAZwRtWmvWVDXFa6psU5zcLhH13V0bUfbddw+DYwhM/UB6yRacH7VMoVOBkupsOuvZsdGBSVR+YU4HFQhl5FxnPCusF6v2K8arHR1CT9BRRIJVRT7e3ugFAtXkdLE6fk5bT/QB2j7Tp5JgpJEmwwqUzceqz2rqkbrxOHKUDc1Z6dbxiGgrMI5iVCEnMlhZLmsMBqu1UKWM1pTUqHvAqujQ2prMEUxhoJFFL2QMmOY6HYdzllWC8/BuiblBMmxt1wSk2K33dJ1LcZalqtaQFVG4Y3EWmrrMdrSAuRMU0vP7zAM7PpB3DnLBdpa+jGwa3vaITLFSEwSDUrTIAM/rTFRlpxhEHtpvZAaDF0k45xixLv54N32FJVZVjVTSHST1F70k9REKMzsNlKEPBLy3H8YAyUEoctrh9aGtt2ya3uU1lhrWa+XXJxf0PUTKUtt2otf9LCAcmpF23dMcSSMkThkUoAQEn3bk3NmuVoxTYG+7egGqdPS1uGswVSeoqAdBrquZ13X1K4SpXS2yqqZ4F6SDDWTEqfWNEq/YttP9yqEvHeS2d12hCwKsfeebR+ovedgtZzVzUwigg4SoxgzVbWHLolVY1guHAtdc/P2lhAzi8ZRO0UynqwNTZM5OVxwsNzj7KLn+dNbBGZwWzHU6wOGVLgaJoY0UbLGTJnaBR45uY7VisvtyO2znjFpgsrkLBEFpTWrWpTSxrl7XLp+nObcqlBSfeWwRlOUwcwPqpSy0H+1vacef+L643d93ICbv/7VX1AuLgfaceC8DYRk2A49XR8ZhsBu24uvXwVKSqSo50CxTGKddxLqV4WYNTkWrPXzpla+YFpprBX17q79LKFQRgnwQU1UuqAwtH3C1+C0xVrp1TNWE1IhpExKkRBHUIrKOozR9LtCVTn21kuszXRtT4gJYy2+rgHYXe6oV57l2lNZx7KuyQTacaBtO7xSvOD++zC1Y+p6al+hiiJEwU/bxomdFAgxo61hGEfqWpET5FjwXrO/d8jKGYwquIXHes32vJ+pZdK7d7XtmGJHypp6scQ3lspZllXFptvQ2IplI6pGnAphmOiGiS4I9EPryBAy1UKzqCvOt4Hbpy3XD5bsr8SW1U4JbeDTXvSIbGy7jlIMFEsx0lW1cpZaC9xoWXtCAKUV1VIUtDtnHVMvqmdVCd02lkC9XPKBP3yGro+4eomvHFeXdzBKcbbtGEMmjEBxxJyYJunwAoNShs3VuWyMkbxZ203SE6gFAJMzpATOGx68cUC7285dQpacFX0XsE5RLzxKwzhFxpjwJkrIuwiK2xnJvOhZzRz7zMH1Q1IM5GnAGcPJ9WP29pZkItMwUiKQDFMMNAcHaDIxDABS4hsjhsxiWbNoal5445i68WAgDAlvLN4pTjdnjGNhSpmsQBvL3n170E2M5zumLAfhNEa6vqOqPXXt2V/uYYzhaOkIOWHvHPJJ73kLr/37j7B6WQOx0D0x8gf/2VPc+AvHLL5w4F/8/HfSjiNvuO9LeOWL38jv/tUneeAvHPHgnz9iOAssX1xx9o4dh18hBLTnn7vJ2/+93+ctP/Bvc/q7W05eu2b5YM1j/+vzvOuvf5jfsP+S3375L/EPvufvcf+1+zkO1/nVb3uMp37uDAW8+X9+NUevWOFqzYdu/wHf+re/kavNBV/96H/Jl3zbF3Hz1y45/tQVv/v3n+b0d7a86Qdfibum+L2bv4UCfuGX3sHD3/W5hCnyzf7LiVnxutd9Cj/yQ9/Dqz/1MwE4vnbMk4/9DuvDR1BKJvkpFz7vz3weP/2P38Y7f/K7uP34LQYCX/A1/wW/965/xu+9991sukw/QXt1wVd+3TfzrrM/4Pt+5sep33FO5R2LheObvu3bePqZx/m1X/5Fcj+wWjRUi4opTIwpU3tYLz0vvu+Qdd1g9xfkMbG5vKCbJiq/5HB1yGNPfJibbQva8IqT+0lhYrfL7F3f4+WveBSVMx/44Ado+8id8wsqJ1nebGushqpZMvSJa2tY15mf/IXf5l2/+xybdiDlQorlj9TCWaIpMygIFbFAUoo8q78uVwKV8hFU4eToOq942Qv5nm//Um4+/TyXVzsCib7dcXrrAmMbAROUxNC2TG1GVRC1JiRDmCJX3Ybbd+6gjefGtWOWleMPHvsod863tPZBlo3nkb0NpjJcOzri2nrJojJcu7bGakMYJp65+Twn169xfHSMN5axaykmY72lpMLleU8MjmIThsLBnsN5RTdEltWaWCQL7W3N2VXH7c0VaShMMdOOA6XAuvLsX1uRc2Bz+5TKel71ya/AKKibCoDL7RVd13P71jmjknpxBkMcJk67C7QxVNrM1sXIZTvx3M1bHO7vs1otuew6lst99hcLUhjY9TtCghwy2jkUE9YqjNHY7OiCULCXxnG0t49fL3jqmefR1rPbtVSV4vio4RUveSHtLhBjh7EFkwwhJK6d3Mdm0/LEs7foxohymoP1goWzeCuEzoxM8S9PL6i940WPvpDDwwOmNEFlUUYx9hPttufDH3qcNEX2VxWHh2seePC6xEZSYq+qqbwDpUlFsmJoxTQm2i7w9LNnbNse5w3L/QUpwWOPfZTLy45pGqlqz/7eWpSUGmpriDFy8/SSbduzaQdCDBwdHOCsRSlRJmq34smPPsnmakvtD1gvF8TYYlw912ll4hjJIWKqxKI5wGhPN1xweLQiB880TuQ0gC5o64kls+dqphSZktgxXeU4PztHoVg2e2jrsCpzudnglyus0yiVaYzmYP8QbQpxzi0aZ+mHwKJZoDOoovG2pu96UJF6YTDWsOulz7VEiCGjrDyj9vdrLm5ekpUilMTUD4xxIinFUzdPyRmaqqH2nmZZkYeIUZr9gxVmrs1QiDqfElxeXLHdbmmWC45PjgTwFSbGYUAZT853y9Slf3I3BkqGvh+ZxgnQhBhnKqn4FO4Oqaqqwlkn3ZQxsmjqezGgMHcWVpVl/+CI3XbHruvYbrvZ6aNBMVdqgSkzuMx6qe0x0CwM3luurq6wxrFoGkqB86tW8rbOSra9RIwWCNK27THe0zQSSel3l+RciKGQEqRYsK4mpswYpRLmcL+iqsTimWPBmApjLVpDTAJelEFqRGuDryuss9I3OUnlGzkzxQgUjNYYbWbxwqPn+3sMcw1UVnPXo4glpWTpN86Zy+2WmBK2cigy1lqcluJ6rwzeGdaNw86cgJwzm2mgG9JckSKurdob1o3n/oN9pinxkdstF+2IpWdReZaN43C/Ril47vlzlqs1l9uRXORwN4xBrLbTREwB5+5GFQqVc9RVNbNKFNMUmVIiFTmEhjjJunbX0qy07GdHgQx5q+/9/ykEnLV85Nmzj+tM8onr//31bxRwc+e8R2uFs5aTPSEftaMiTpqQMt00MY4ZZQrbXcvmKjClNC82CmMcuRgJtyZFnvturFHEKUuxqlIEBVMMFMGTUBBEsRAHE42fe4KURgZAQkbTaJRKWOtmabzgrFR9lKRR2qD8BFamNSUXUZK8xVhDXWm0goPlgilHhrEn9gPDdgdWyltTzEzKsN0GfDKcnm3IOZNLRrpcFdZXNFWFVTCMHQf7S7wB5w+IUawHuWj8MLCwHoXi8qqjjyMlZurKzxjuQrWA2GviKNUiRgdqt0+lhCC1rheY4yXeQgyKMSYu+oGbVzu0MtRYKUgeCm2dKCgWlWNdeSwSru67TM5wtpPMRd/27IbEpkvokmk8OOelnHaayWCLIyiRZS3b0t1uonaOIU2sFjWL2mOdo+02dFcducDB4Zr1ytPECmccB6sFKUkm4uBgj0Jk17YSJg8Koz0Xl45+mrDWSfGw0uzajhAS7RAISR4CuRRClEwZIATVIuXretT0s51EFSmzjVqhDKiiUMVI95CxQMG5IsS7KrMNI8YvUEpzdtFzftVTdGHseyyGw9WayMgzH3mcRb1A5UwqmYgijRMGhasMyioeWzzL3v4+i72aOCVMLjhnSEoydNZ79vZXOBO4+YGbXN9fsNcsqY3mRlPjtebpZ0+5GnowigcfOMRoz61bH2UqBXU4cvUtv8r08pq3fugfUKaJz/3tBeMXDbz20z+bF69fzcMvepjaOQ5OOsKDT3PxV3+cw1d8KqtP/yJ+8L/6Vp78J0/z5z7vFfQ/+0o+eqvlm77l27Fa8SfUPyW9OvKmL3gLxhp+5Zd/jl98z//Ez/7cL/CrP/x2fvhHfowf/tG38dKXvpRf+aWf4ZWveQtnZ+ccfdMR26stq1XD3/jWb+SL//xb+G++42/z9gd/iq/+pLfwlm/+Mt75Te9kvbfmf/zB7+N/ec8P8/3f//088sjD/NRP/zTv+Y1309QOoxWH+w3bq5FHX/gATz39jCxKCs7OztBac3J0QLvZSO6Kwu2nb/P0U0/xgk96I9uLt3PfyUvYP7rBww+/iO7iGdp2YAiKlG7wya/9NH75v/5x/sxwTPW5D8mm+HDJIw9fZ7Ga2J09RJUNzbKhOtoHrcntgNYZ7zwnB3vUXhNMoarq+f7LeBy1tlx/aM02ZnJW3PANxinazY6oCioHcoKTa/fhh5FsNC57dLEkFahryZntfITUsrno+OjT56QQKVGUW6v03O1aZnHxY7KkWov1zMhQYYqBoiSDqqMSQELb8uHHn2Owlv0bB6hcGPqRZgmLF66hyMPdWanbOX3uCtNo3LKh2w4Mux770AHTw/cxjIEYoXaO/VdZ0IZ/NP6nXJbC56S/R4jSufbscAdnNW3fsb/wnF92tCGzH0cuN2fcOQ+0u54b1xpe80kvZFE1OHOHalERMahciMNIjJLbIW5ROYHWqGJ59MFjDvcqFEao3AuLRta3qq7JqTDef0xT1yxXzdz9JZbw44MVJ0cH3H98wO3LHdt2oD62HB6sGKb7KXMGzxgNzrLtRy5v36FZLnGu4vxsQ1M5Hrh+gNVW8mdk+n5gDIFumu5ldUYFMQT6dsBqx8m1Q/bWNV5lxpDQN45wTtSpNE3kmEAZppC4ONtSFHTlksvLK9p+wFjN0jtsFiXCZIe3mjFFcowYZ9n2gQ9+5BmWy3N0DdgEGPrLicuLDbtdi0KywdkYlL2kpEy7a6kaqTQ4u9iRKTzw0DEpTXTtyDREum4UO6k38mxWjsYamuv7KG0wylDXDmslxmK0FpspBes0+6saa5ecHB+jgXGQg5hfGF740Al9vyYmK/nabBg76e6ytkatNSFMaKNnp0Jif32ALo5YRlwFbZcY+4CyBa0Um6mbuxNFpfSNpanXOKtZLiupmTCe5brhYL1HTpEwDvRTRwoj7XagHwJThLp2lJRJI0xpmuMFlm3bM04dzmq89VLfNEykkKm8Y29/ydRn2mFAhQ7rPSkrAoqDgyMqb7jv+IhpjLS95DG1hmgh5szF5Y5xCnMOTEn39ZwFXB8eEkLk5vNnmLk7MqWI0lJRkcrcg41hYSGEyKRkj5VnddBqgzOGyoltNDvmjKMlxcwwDFBmp1cB7z3WGYyC3dWWYZzmIbl0EDZNg1aSFSxFhvXWOBZ1LacgCkqlWXUypKzYbHvImb3FkhAjVVUxjoP0uxqxz2oyROh3kRSj1LdpgaDllKEk6fc2llVtMAZqb1gsKxGikX1HjJm2H2j7hC4RqxROz9Ahq6VTMCXJSSpNjhlj1D0rNVriV2K3lM+kdn522RTZoyrFOEpt1dgKk6HMh/c853+LieAlTx91psSJxmiGEElBk4viahjJRc9Zf+lDjX1hmCba8ZKCIqSANwltLMoqstZMkwwXTg5PGHPG14kQC5dtoO8GjBb3hTFehkGpsHBWXj8Lj6RtJ4YhIN9eCY7FkDEaKqtF3dey7wxBDtO1b+5FTry1HxNr+cT1x+36uA+LqYxMEzhrWS1lA12ZSK7FJhpLYRwGHjq5Qc5gvEyuxjhIZ9GyJgb5QhQKuURikP6zOAlq2NaajOL0fEvdNFhrOL9zQYyBy6EnqYpnnrvi9ukFpITVshlyzuK95LxyUaQkh1BjZbrRDhP0ieVSlMx+6EilYIzFWSPAhEksSZVvWC0ci6UlJiF4dmOPUoVFJWHj08sW3fUstWcqgdrXNI2UCK8aj3F2LpRV6Og5vRwIeUvtLapout2EtoVnbm/pugmtI5XVaAT3nIum9o4Hb6y5cXiAs06sSF3PE7dvs6rXvOzBA0JyZOWE1LnZkdDs7625fm2fFAML22C94tbpVt4XFbBNYLk0eFcRUkaVDq8NH3ryFodry0M3rqOVYkyCGL95fk7tl1jjMCaji2JMCVXEZtyNkTYMHB6tUG3hYrPl2dsT+6sle+slL3zB4XwHaVIIuMWaFCOVzmgPxihqD7vdRGM864MlRRe2bcsLXvAIVmnKNEEWEMx6cR3vGwoCzxhjImZFGCectzR1RS6KcSrcvLOjGwJP3bzJ1bZDu4arix3rpsY4S8gwxiQTutPnSSljm32U93z0yaeZYiLbXjJKUbpCizJUiwajEzcUZ555AAAgAElEQVQ3z4KF1MPWjOg5FzveBTlohZ3LkJ9XlpKfxVhP5S1WQY5JQA86oowR1cEUVvUxH9ITZS4M1hp0bYhj4mwzEGPiV5bvxzhDCPK9Synxq+99ks9+87/L57/mC/mVy/fxo3/zJ8hx4qIcQnPCd3/vz/DQ/Sf8W2+ueWVzg9967Hn2X/gabj77OOuV4tWveAlPnvZUeyPkgsoF4xTaKP7Rj/04/TgSu8xP/OP/jc9502fzjl/+NV7zmk/mh3/0bQA89thj/M7vvo83/onX8zP/7Od461d8KV/2pX8R7x3LxYI//PCHpWdu2/PYhx/n7e/4dSiK7qzlMz7jM/grX//N3Lq14bnbv887/uX/ycVlx143UXnNi1/0AI8/9Zw4D5SAr0oWWw9AXSliJUXVRsPjH/4Q3/It3853/Xd/izd96ddzdXaHW88+wfO3L3n26TP2Do+IBl71us/k1lMf4saewus9Uizs+kycu8F0Sixrw8KAVQObWy2bKRJ2kYLBeMvV9QtO1ha9TVwMExdDi8qaha3BKR64cYJNgZQCf+vJz4JS+LoX/SxGFZZ1BmOoG43Wjkod0XjJAn/wqVs8//zA53/WJ5PLwD9/+wd4928/xfufvoQi+ZS7U3o1Hz60mvcrSibeC6dYN6BVZBgKbSx0JZDRTEUgF2oYOe2f46u+9rt5wxtezV/60s/lgYXh8uZtYj+hLWhnMcZilGLpKp69c4vaJo6uNcQ9S9aaHD1VVXF0coirhfDoreUX331MyZkvftVrSDkSk+LpZy/ZbnvGcaB2mte/9lHGqFAlMAyB/UPLI4+eoGJid7GjY0u/2+G0pznaJ+WM8T11CpJdWqwZU+LyakM3jVx89DmGKXH/tTXbyyueemqHMQpXeSpXSaIzFjofuHnzjoAZGi85yiGirWV1tGbReGqtyNZSaku9NOx2A9tBBlA+B5TRPPqS+9HG4azj01/5MGMIfPjJZ4jAer2SonE8h0c1jTcsqgXrxYpCIpTMkKVq6eLygsvdhhdcP8BXmk3XUbDUfsm6MdQnDqfFjjm9JEJODL3iZqW5XNVCnrSOXduTcmQ3jKSYOd11XHU7rHY4Y1iGwtRNWGvZdB3ea06Ol9x/fY+HP+XFOKPZWzcATNNEjJlyvCQryScfHXpyShyt92mqmlRGiZXUK4Yhc35+wRQmXGWp/JJ64clz1tvajDWKYawZknTA7q1XrJN0LYqKJARs1lYseH1hfXBMOSjkFKgXFTntiRXbZqx1VLYmhImcDDnL76/rhrYb52dypJvXylLE/ll5LzZjZaSgfgp0g9htx3GiS4F+7Mkq8+yd2zJcxElnnepRtiJjGOOG3UUHSjFNUpa+WNQcHCx54SMnjENibHtUyTxwtKJuaqHC5sTBumHZNNS1xxhN101cXF6ScyIkx2Y7EoNBm8ILHlhydLDEWccYCtMU2O56xkmGpzFm7pydUUrGVZ6UEolEtoZSDHcuBLzSLEWhMtZSDKhUcN5TeVg0C0opeC+uLWXkHhimHmMMJwdHgMymdruWrRJwk/OexXol61HOeC19qdYeylBjGAWog2QyjdHkIrRlVCarwNj3hGmkWSxQybBc7ZESc6WJHOJ27W6mghqUsvfUrL2l9GlPYUQVWC3WhBCJIVB5T712MgRYNRg7g3yyqIrnl1eMMTGl7b38/fWTa/fAOVophq6XTkGVyMj3XhtDsQYTkOGwswKZUUoOXAWUNpScmKbAOEWurlpSiJKJ1IZQIsYYzDzMXjTi5Kgby2pVoXWhH3qmaaQbRkLITJMmAWHSlCDqbixBKO3KkSkUrTlYLTg5WJNzYNeNKCdAqaupEJIixXbObvbi6jIVy1UttRZGYUDuE+fYDoFQFFdbUVS1Ao1Ba3GaaaNZLuYBnRFKecpppqHqOfpkyTES4oRCE3P8eI8kn7j+f74+7sPicukY+yTdenmSXkF7lwwKldP3wuqUjDMFZaXXJpeETpHKWLS1QCJGwBpiNBSv/+iG0wanLXXtcLZipSYSheWlxviGymiWPrO5GsglS6YLwWmnJFJ3SnIIiSWglSaEJHbXQaGVgHe0tWhViGYmf8022JylZNhoKbMVsUpUR3Qhl0TMGRM1um7w3rNcNVTeE5LgvRXgrUVXFVpbmpSRqLnMYEiJkgR9n0tBZ7FgTCGSi8I4SyqKs8uecYg0jfQj7RvF6fnI+cUVe4tDnFU4A6UoFkuLMjNoA0XjG+qZKrtsZFo2pkDWWpDhwVCKTHeskkyGVY5V7WV6mOXPTEEz5YzyMgXNsRCHHc4bvPFob/F7GmMKCZk41t7Lw3cMpCzDhBSl9LVeiP0BpYk5MIRpPuCLSlNXjpCFNnt11bFe1JDiTG2LrFfLuesoiTVEQVM5GqOpaoOrKkpRNDVYowk5s79WdP0AZsHV1Y79/SUpqxn0UyhxYryqpaNJNeAMe0aoedoprDFU2s/DO412npgCl9tLjHXkIDCCu8rNOPSgBdZjioKciUoIgTkZrDOUGJkoTGMCpSkR0hgoOqNCzxh2jFOmpAwktFfUxnHVCl11t92gDHizIIyBLxzfCnfg9Z/2J3njz/xl/uyN1/Md//RL+IIv+HcYerkvb59eMgwTn7LpeLgfefLZ29y8fcGDmytunW1YuIpnnj7nZa9+KUUpipYuTUVB3EcCT5E83N0xKjMKW80/oJTCZ33mG/krX/Mf8iff9Ge5c/s2b3nLX+Rrvuo/EDtcUex27T2reZlD7jlLrjUgWPRhRrBrDZebnpzhmY8+yyMPv2Bm6BRuXD+mlMLF5SVksfU4axhi4P0f/EP+9598Gy9/qGa3bfnTX/z1XJzehGIooVDVhpe96g08/lu/yFOftqYbal75h5nlENh2QaqA0FhtSZoZYBBp+8AU5UFfFSX3BHrOSEkGWZcZQ6gMbUj4nNDAzemYkgqRLNknbVHW0tg4owtE2aZA3Xji5Y4nnrkNeeSJ5y556tYVMWmMKmCYB2+FMk9oFQVvDI3V6JJZLA1LDwZLpQuV0aR+EmBZvrtcC3jog489jzI1r3/dS3jRC06o/R6VTZBHhnFHGnqsknyKraoZRlWRrWXbdxjvWSwb9pYN1hn6IBvyu5CDxXrJ0HcwBU6OVyyXNRebHSrDjWtHbK46uj5Kl18cCV2LLprNUIQ2mQLp/AqGKCXnLtM4zeKgQRlFmDL9mFF5nnQXgYDkBvyc88xZcb7piDHJukFFDpKfac+uSLlgqoqcEhcXG5bGYbXiYrujv9oy9p0cmJcrsJrtdhAbbQtxlhbCQUs/TTx76xSN5miaqGpHu5Nc/aLxeDdSmw6LQi0sprbkIL1rpQjevq7E5p6yxjmHr8S2N0yi3tUrB1mRQuJgv8bV0hmXskLpBgiEEAkRTO1Y9Q6jpMLAzXqntRZXa+rKcO14hTOG9WKBUaAR1kDtFkI0zIVuDKSSsKaSfJYqWKtYVA3KgLYeqyPTWNENBec1Ri+xFrI1aDTey9+h6wouiTVtaTVaSX7Xe0c3tGKtdgZQqJUipjLDQcTKlrVDGbBW7i+VE5XXVFXNNIpt1urCwdrhq4YpRA6SBy10T426Z6ejwFkXcdoSS0LrMjtVNKuFVPbseihFo7Es6gVWW1zjMSaTY804iooSU6CylsWiYm+9YLWsmKbC1WZHjpH1ckVdV5xtxeJ518KpVGKzE4ry0d4KV2nOL3umYBgN80Ha4JzCG1jVNTlX7C+8AFmiAJrWC8mwJgrjMBKLWNmVMuw2W4FYl4IyCjODnXJM92rQSjYCMtSynseQmEJknOR9uXV7w91qo5wSAi+V9zDMvYIlF8aZuKxNQGmheBqrGfpB1h2kmzMjc4EwiTvC6NnCGDMY4UHcLbCPKaGRjuSqEuCREF4Vy+WCUjLjKGtOUzXEGEjJizPCWUKaKKXQD0IkzRFSyMQsVtjKqfl90ZDF3hlDufecUsxEe40cFrXBKkvRSWpmZsr6H+XGpQd0mmQQkeZ+YqU13mu0NSxmer+5qwxbi9YZrYt0R+dCmCJjHxknsZTLIUzeZ62kruauxdPNKqczQr3txmnOVhZUlJ/HKRCzuPjICqXtfCiUA7Ay8vwqSTKZZZJDd1Ly7zNFUXmLN1riD9KfJfZapDYP5D7LFKlfphCi1FdlZL8e472G4E9cf8yuj/uweNUm8fyXSM5XKK1YLpZMg+CbrdXkaHg+dExjz2pZUfv/i703i7VtTc/ynr8d3WxWu5vTV9VxQZXtIgHb5YbGYBOQkggINo2JsSEB4twAyk2UiFwlJCF0EiRBipQEAlhAbIREE4hNEyQoF07hclXZPqfqNPs0u1vdXHPO0f1tLv65twsIUmwpJBce0rrYa6+5mjHH+Mf/fd/7Pq/Gh0g/TMwuYKxFSoFzMzFE2rYlZw4FRUYpCMHjRo+tq4Jb1hKhwc0TjYQXTzVH7RG7MZMISGmY5sA0eubgEULh4+FmCAGp9OFihpv9TIwZiaCqAiGkA32xoMKlFFS65+mtOkR/ZIyRCGyRDqhIziXvSCOIoUgu3G4i5/4Q21GgCG1VUdcFqHJ02uAmR/ARkSNZZ6YpsG4qlk2JqhBCs3c9bV1zcmIJIXB9NTL5gJkCd44XdAci50Xa8tYHPS/dbVke/Hyrdc2yrbi6GXjjwS1Jaj7ywophDpytGkSMjFPGUTHMieSmskmXASUERwuNSJHLXUFg55jo2ortfqaqAnPQ3Oxg7GeOl4p+jrgUqbThznHLNE9shx2nqwXrRcs4Jja7nr4PBznJQSInioRjuWhBFG9keZ8E63WFNYbZCXLoePj0hjD7UvinTEyCR29dYK0iuEIpPT09ZtFJcDM324iPmZxl2dAeApRfPF8g8oIUwb6y5Ok4cbP1aCSrtqJSNcv6bqH8TTN1XXP78TNEjNhDzpe2huWqozKKnAsNcJ4jCslVv0G2S/oocbMjjzvSAcY0zQXzcnxaSL3z6JBIcizh01e3PbZZoLVhzp55nvG0pDAy7felgiIQUuT68Z7xKOJzxOdA9Il971Fa8en5O1i8VPOF5m/xo9vP8dnxDX7rL/nTfMu//nVoHF//9V/H8XnDPHp+xa/8doZxYB4mbi5vuboZ+Kk33+POquEffOErvPT6p6kO8ltB2RD+9t/23fzFH/yrVFXDd/3m38B/+of+c3b7HT/x+S/yO7/3e/if/+xf4BOf+Dif+vqv47Of/Rzf/Olv4PZ2y+XFBdZafvf3/46ykDzPaARyOFCV4e/+vX/I7/q+384f/q/+OK+++jK/5lf/Cn7kR/8+OWfmOfAzb36ANpbPfPbzNHXNL//Wb+Qff+az/MDv+1388A//NcIhjDkT0SqjlcaYhifXE9/8rZ/ivq6RJOx0zcd/8YvUymJWJ1R1zfXF2/zVX74nZ/j0k1PqRWTpAovW0qhjpnsv0LsB7x3ZaBZJYE2DqixNXXOnspgc6LvEyTzysaahsRZhDJ7EzRgKedlouocLSImXP/YxmH3JmFWStnME56jmPSQBWfD1q2Ne/cg9Hj/acHs5s90GslQgIFGIdOnwMM5SQYKcEsYq7hzV1DpTNRVGgKCQK3NONLuJmAXBRUYXue5L8ZWC4M033uZP/Xd/hRfu3+OlV17m469/hHsnK149PcbIgXncsVgtWZ+foUgoEYkxUY0d5nhBoyw2SUSKaCHQusL5UgDOQbPZZ/opcHR8RH0iCVVV6JumQTWJtqlohaG97em3F9iuplqvkEIQ9jPb21uyn4uKQBtkSlw/uaRbrpgnRxxGtLHcOT1mnAZcBlO3nJ4qpmHgeud4+8ETtv3A0dGCrm3KhNDWvP32A7wPvPDSfdplyxIHJ0fYynC76dmNHtu2nK4bjqxA5Yw9WWFrw3sfPMLaJcI0XF7d4FIkaYm2DU27pLWCJDTTHNgMmeR7wrwlS0ndKJQMhQKJYrXoEEqz6YeyFuaMj1v2YyLGjJ8GtILj+ahsrrOh0ZqmEvgUGSfPojU0VYMSpchq6gXjOLLdjXgfcTEgtaBrKtpGHe7Hcp1k78hKlYljrTlardBG4WfPNI7MMZYIBSFJ88wYHM1BYujjpkj0EJydLrBWs9kMeDdjjUJLizEVQkDbOJgHKp2x6yVaaWxVEVMgBgPyQLS0VZEs74rvcQ4lAknZujSMfYmvmKcBYxRVJbD1s4iViNYlaqOy+ZCfDEhLYzROBUTIZBcJfsJKw/nJqmSihojUklVbCr9ngjkhBQiFUhotDpMyYSCXpm8Bqx0ikgAlTZnCTSt8KM9MKSUnW6jrhoikHyeuNz37/Y5FXXN+coS1htoY7p8VynxIqciYB88uzizqEqY+z4UgaqoKYyxKLokxFXhUU5MRrFYdTaMxcl32c7czQhqGsShV6qZMv4QoGdOzDzy92DC7wkEAwapZkIH3H14jVYlyaWuL0jWdaUFkZlea+AhJyiXWgsMUd7nsIBdehZJF2ZNypq0txpjispZtyd6LCedLxJGIGZ4pFqsae7KmqyuMKc2FHDwp5dJkTwnvSyi9VApyQ0qJZ6yOq5uZwU9MoUC1tC4wwVXbYQ6FTxGsKW63u1IDSYmQGV2VolPEAhZDikONLMjRF/psLkVQygIXDnnQMRGTR6iMkYp20aBUGUCkGNBClwnoQSkyB8c89dwOZdgiD6RTgSAljdICUxeQpBEWZIHZSQqJFlXyFP1M8QCnxCGpBnEAySkFlVYoYctVWltEyhDLNTt7X8CNKVOy5kLxLSpJbQv8SyuNQBxk3IGYIsEXYGJMpRMZUy57bKkZp7FMcw977RBKvM0vHP//PH7exeLbD27J+ELvtEUa51LxLIosiN4xDZ716QpJLlHeUoGMhByIQhLCjA+BfhiQSjJFS10L+tuSm7Y6iWxud3zwYKbuDHVrWaw0Gc9+HxCbvuQHCcGqW9A0DXfvtMQgcHPJfTHWMsyO3X7gdjcgtGUcSzbM5MrNq5GEyRNSYu8ONykSmQK1TiQpULp0kKSEquowWqN0oQkaUSE81G7CmMjsHD56jhYtthEE5xj0zGJ5hB0z3YkohakvXVijFb0LICqqShHEzL4f2fcJKRM+zkgSp+uOxbKghvv9wH6bOTptuffCKW8/2JAFbHee3X5CGU2nDUporkfP482O01WDNMWwbbREpIyIqQCGDvr9FGcaa7l/b1kCbJUp/k5SyehpM8ulRUvNhx9uGPcDr7/2ER5udjy82WGkojJrtAYRHVplqkrhZs+ilmhzREwRlyayyOyHgX4M1JWirlsWyzXDdod3PdEb0AqrDW2bOT9bkkPpQCpVPBxvvvVlumVLVSmaylC3FaiySDrviLFc4s5FUvKMVtJXIzln3Og4WTd85qcesesdy1pzsmjp2qpEYISJSkaOFkc44SBkXIxs93vG5Dg9P6GpNctaUVWW45MlaQ7sc+b0/oIsLMknGs5w00A/OG76Imn7Ra+fo4XE9zussnR2SUiRy92Gk/N7VIuG0c1s9yM7e04rA9OTxxADQST6yfHZz36FfpwO04aMc/De00tS8oiN5PTrF/ylP/KXmV+skFnwQ3/xB7EBLi8f8MXPf46/8IM/xOOHD7l6+oSzO+fcPTtidbTAGMud42PurSqmUOSvMkl00hSxi+Cf/sRP8rf/tx/ixRfu81d+6K/xN/5midH4d7//9/Fn/vQf4w/+/h8ghMD3/e7/gKvLG/723/m7/I7v+W5+5ks/xgcfPOTHP/cTfNM3/FJK4GbZSJWJUyYn+P3/0X/Mn/sf/3u++7t+A2+88WX+9x/5+zwrK7MQxCyIc9mYfd/v/gH+zH/7x6nrmgcP3uP7vv/3liaQgM/+2P/Bd33Xb2VzseHX/ppfyR/4g3+Q1apjuL3gi3/nL9Fow9Fpw2p1hDn9FI/e+jzvfvltdp8q/uh3H4x89JO/jF/x639L6caTeekXfSNf/JE/z+bqfSwRoTTH6wZhNZWpkEEx7meiCsQ0Y0zLYtVim4ZhmIg5IETJXNRKIzWcnL2AzCOjd6QkwBj8TmNywiqNUQrZ1Hz87DVubyc+eOM9fuan3uHi6pYn2w0q5cO9wTPdaSkicwGaIME2Fq1aKpNJ0RGTIGXF2ZFC5ESYPdshctUHggCbM22jefDOh3z5zffQ6sf5xd/0zXz6G38Zn/iaT3NvEbm9eJu2XaFtR3Ij49yTLKzWa+LCIKNETREhIp0CbVukUGXTFQVS17SrBaZakrNHNxOzNWyDo14tMe2SumlZmsx4/ZgoLGa9RknJzaMr2k5zcnqHtrZI4Opyw+f/zy/y2qstC6OhNThKh15pw/tPrksn3nn8OPF063n/4TXb/Z4nN7doa0lSYquW99+7oh9G3rkcOD0/4Zs/sqKfBjwVta2xuuMT3/QpBIEn77xFdJ6v/brXMJVGW8fZ/Y9xdHKP4eE7hAw//eFjbLvk3tEJdQ44UYAQcz8hIuiUGaTHjSP73Z6YBT5CGAPXQ8/Tq2vIAiUgpYnJzzTNitaUzn/KQMwsbcU0TkSZkUpglCCGCWmWZYLoHKYR9DGxHwZizBgtqJShs4ZFZRnnci22TY2xiqaqMVVR18zeoZQqNGKVi/etq8pEI0vmaWbYF7hXJOOip9aG1aKlaRsun94U6rETgCPl24O3ruJ2uAGgtguEMCxkid6oK4PViqNlTVPVuCmgOgutIstMFBJT16SUub2emWaPa4o/dLu7paorTFt8jbe7kX2/JwHjkJjnRG0Nx4sWliWHTkvFoq0RSXJ+fIT3nuvba1yeccnSyZp121IZjbWWIYwgIEyxFKvBFV+bsgfF1R4XHDHDcnmOFIrjo/JUzVFijeF00SAljCFQVxolLMdHLZWWBJfY7XoqbWmqkoW57R07BxHF7ALXu1tiTAzDwDw5VkdrrLHc3lwTgi8RVKpkWO73W5Zdxfnpsrx/dY1UlnF05BxZdEuscFR1TTY1/TizHXowII2gsZZVVyidUkpMZQqAKJfGvNKWlCHErjQLMhhpCMEdCoviYZydI1eFtlpwPJGmlbRNVQA5wRNDQGSBjRohiy/SJV9iWoSkaRsWTVOmmiEw+RkpFMRCl5XqGYwngqDYiLLAKEVzmOK1yxqhBZ3VNNYw9I6QYZrSISs2sWprTGVoFi1CSsZxIoVYCP2IAnXJiZACPiScL9FyxmhIICigQWUMbVMfitbSkHkG95mnzDz78uEDIAkiM4ZECAmrCtjGakHb1FijEQKqqljCVLK4ODB7x66fC8yH0qbIWRZ2ha4KcyLMSFXiypqqpA3M84yPnin4UtT6Yp/Z74dDxrnGGos2mqNFi1ECa8vU0wdwLjDNMy7E4vvNGa0lIkSyLH1PKEq/GJ8piAonA6Ew1v58S5JfOP5fPn7eNNTf8ut/WV62K5ralADpwVO3Nft+ZnIeT6HWrTqFkprdrSOlIhvoh4GTowXrdYs1gu1uh0MSXUGcL1eGlCN9H1kedfTzyDQXg75NZaHZ7m7IShKyJISAItC1FZuNo6o0i7pGq5rMjLbh0GWXaC3RViKyoW3rIlMxghwdIWvefXjLdnC4pIixbGhu+4GL2y0uBHwqRmArilxrzok5CKQw1GIixIyuLbYyBZhCCb5FRpRW6FQCS/0cEVrRth1GWxqr4EDPtG1FJLISgtk5TL2ibmpqWwKmrRWMbmJyDlsvuLNqOa5rvITNUHwP90+7Yr72maU1rJc1P/3gkmkeUMrQ1i0nyxqjM1qUgGRrDfWiYU6CzeUFy8WCrupARBABhSTOufhdphJYXNsalR2LrqOWZRN4eryiWy549OQp/TwzekfTGFLOuGHGKE1T1UglCDkTMzSyZhgntrsd65MjFImmKuGyQpSFeLO74mS5xKgaFxMhRm7HmXlOBFdkvTkGQozcOTui7Rp8mHCuTJRn58tDSpepS4gZKSJ3j5d0bcU0R6bRc3K8JMvIfpjZ7IYiq1bFMzu5AVNbfFY8fnLD9maHRnB6suL1j91HkHjv/SuUymQt2faexxc9x8dL1gvFcacQSbDfRaZpxiwU4zTT7z1KSV57+Yi75yfUxjJMA/up5823n/CJ1z/Gp77mJTa7ng9vJtrlimAhHjLKKiXZDHuqytKlwMl//d3kLNj8gf+V/+w7n3C5HfiGP/SQeTfx6779m1ierZFuIKuKYBRkj54G5sGxFoHVi3eR6xXf8zv/C3rnCjwoZyqtqbVhO3tyjqRD5xFKphZfJUctnz7IuUvC8T+3+ojnX8Mh6/QQyfwsOYODyYNnxeS3pO8ABD+mf5QUi/TpwLeCBCnw3FyPyGS1QBJpG0cWmu/8lb+a7/tt/wbNeMFbH77HO09uuXp6y7/2yVe4tyie27OXX+O//LXXKDJ/8nOvoIRi0TYgEp//yZ/CIzB6QWs0945WtF2DXrbsry6Z5j25qqjqNVfvvUe3WLCNZYMy7jak2bFs1oQcSNnzR97/vSij+cNf84Mcn5xwvb1is91Q65r16R0q0aBtRulEf73hycUlNJbtbstf/5uf4ye+9Jh3NiMigRIQcibkVGSoWlOQy5mTVcvZ0YLXu4ooZ5I2pWvsI+O0RRhDTBW3g+eNRyU3rqsTWlUoXbyg3mV8SCQyv/bf+rf59m//Fn79NxguHrxDp1doYwgpMM8jfT8x+8Cibblz/w6qVtw+eYJUlv/kC78dgP/ml/4w437Pu+9/iKgazk+P6ITkvYc3iFpxfLZioUwJcpcJq8Uh0Fwx+8C0v8U5uLq5QJsK0Mxh5na3RQtDV9UokRjmgb6vefDoKRebW0YfCv69D7RrxdFyWQoQH9nv+tJIHEZCLh3xfb/HOcccdckDlYq6ytS2AMJWR2tsJait5OV7d3n85JpXz1esj1rW6wqtFW8/3PGR4yOO1y2r86bI9MeS1Xfx5DHHJye8+OKLjNuJz/zkl7juA5W2bLc9j2/2PLzaElJkXTcYWTr4FzbHwgQAACAASURBVJtL6rbllRfvsmgq1gvJ2fma43WZKKUpYYXk+OiIp5ePGabAzd7hfaSrNU1t6GpT4gSkPGzwIhGB94l5mpAqcff+UYkksIsCEHMORWLYbmhURV01oDVRJK5vL0qeo12SItxuNiCg7QqQ6nazo+kMJ6cnzJNDlAjHQqI8OSph5bJQInm2HihJiL7I/kKZqrxzccXs3AGSY7m9HnnnwftgFOM00VQVp+dHXG53XDy6pe0arDUEN6GFZLlsyCFxfnZE29Vc3DwFITlbnIISCCt57YW7pDCzWlZYrZCH+2vYzsxuYk4eJTVGVVSy5OE5n5CHZuww7cjSsNuN7Hb750vZwycXaGOQpkh3x3kmR0GlNMMwM4WE0BJUZu4F2QhMlZEpo8n4lNmNocjWhUFKQYyuFATPpMJNfYjh0c9hIkoKckwofTi3qeQEDtOM1AJjFIuuQQLOzejKYE2ZSCMkfpyZ5wKESTkd7AZgak1dFdWXkRKJQCuLcw6lFcaUom8eR9ZHJ8zeMzvPZtvjU+LJ02uEENRVURf1/UzwDlvb51aGkBJSKtw8Pn+KCCloK4sUME8TSIFQmqZty98aHFoqYhAM00xWugwmijKSlCJtXWGURUTKpNxPSK3IBLRWKKMK/MoHZpfphwk3B5QuQ4oQD9NOKYkhEULhFHR1hVSSnMokcZxnyJnFYoEUogDJUuE8PNuCa6XR1hBlUYSk4BEH6KM2pdDTqiqyUJ0Zx31p+CXY3k70o6OPEcV8kIw3SFmT3IyLM92iwWqJVqawPVBAJOUiJ++nCR8DJEFFgQFlVXgAtmowCpQs+bZSwOgzIWXG2RFiYBqfgYtqpCxZi0qCUEUSnlImpmLNcKnIb2UuheMzsBVC8N6jm59XTfILx//z418pDTV7w+1mx2AldWURtSQITxQOVSWWXY21FmIsYapnGmMUQgqGqcI0FTFkUhQcrY+orCjy0ckfbgpFtD15Gri/XOFMJFaBrIvPrdmUv9X5UOiRpiq+DnvLyVHD+dmS66sJpWqyTM+njRCxnSB6wRwSw+RI0bFoKqSKrDpDW0uULZ0tazTBrdj1a/pp5naY6boWnSU3NzOb3rFxjhgSm81c5vlJ4ydKh8mH8kBUApEDdVWhhHiOEhYhIuWMdxUhAiIRp4gkc6sgJckCwxwyvduUm7Eukz2ExUgBMrCLI/u+wCBUhgcTCCOptECdSJTPnCw75tZwcz0z7AKCkaqBzlTkJNmME/525ni1QibNMGa2u8vSCTQV22Fkvayxlea80milQUkubydSDDgfGGfPxX7PclHTaI0gUMuEkRrvI3fu3iHFSL8fiS6xHzw+Bk6WgZQzutaEXGi4KTuMEhhTQEdCLumdwJiSj5lzZtk2NFXCucTsA9tdICvN7a4Y/E9PG1bLlhgj+12RwwyjZxhnMoa6Vuz3kf1+T0lBy1x+OLJoa6xRNKZmngPZCvZTT7doMErSScX5R19gmCaGeUZJRUweWwnu3uvIPnHn9ISqNozzQAyl2JLaIJViGgNCeEKYyUmRoi7xI00x+QcyxrToOXNnfUI/jjy6uCTGjIgj020hDIfo0VJxdLREScn15RZfabpQqMD1YTN4907Db/yNn+SD95/y+Xc+4HR7ycdfucPVzVOePr1kaTLf+rUvEo80p6cvsxMrHvWC3ezIAhpT41MsnokQgHjIKM0/WwM+bzz9bBEoEM8lP/8M6SzDs4rwWQZgLqnG5ds8f438qnGZ4B+Lv4dAQZIoIs/sEc/gn8/IlM9KRpFnZBIYIKXA9vqSD958QMWer7z7hC9/eM17H264vO759CdeBC3YiadFzivgnQ8vWHctmQgi052syAiOFmdlyisDIzMqWHwq61nTrjh+4WUev/ceb3zlAQ+ue9qu4qP3jqlNzRAlTzcz213PMEVsVow5sbCCF154gfPTM/rRE0MkmgkhSlZo1JLcrOnO7/Cl977EF96+4XLvUSLjRfHlHWKPy7lL8fl7M4yOy7jn1cYU4EIskvuUE1maItMSASkiQh5OaCo+4tkXH2TKcL4+JybHm298ESFHvvMbfhOmapFZk1WFC5EpQbI1lZFM0fHk4imrtmO7n9E6Mg09QgjcOCKl4ni5ICaBCZFuUfPKS2fMU0aEzH53gwDqqmEa4OLiCU+3t0yzJ48ZrQz7aUsicbRe0dY162pxKMZTAW5NERU958cN69WCSOZrP5KRWTAl99yLM/tEPOvoh4mcj5jdTPABKU6oqxZdmeLTCR5Bomlrjk7WLFcL2lqjiIic6SrNWSepmopKG0bvSd6zWlRUtUELSyU1c50wTcPRqzVtV5Fay+52T7NYsH/6mCfThovrnt3es5sGpIInw3SQJcsCAEl7Hj0uhcDQuwOQTXH//hlHqwYlobu45s5RTdcYrC1U6MvrLdebyL27Z6AMxytNgyqh4KOjUoZlbUgpUKmaefbcDgPzHAh+pjYKP0Vi5YlaIVViHGc2mxklJMu7iqqzLDpJVVnmmIgCpMmQSzEqRVnbpSyWhM6WZ6JVFi0VKSRcDEze8eTiimkO7Pcz8+SLxzYUu0vdRkKeOL93WvKcfYuxluWixhrBJz76Mm23QCB49923kbIA0GrbIojstlsQNSFFLm623PR79tNITpYPnzzkfNnQtsUK4qbAC/fOsJXhww9uCAmktGx2exBwdNTS1JaushglqWzi/HjJy/fOSqxETLz20ovlXo2lYJCihKg/vrxmnGdubnuGydGPE3OasUHRtQ22slhTVECrxbNogrI2ClUxbF3x2OqS/TiMQ2E0hCIn1ap4D4XIdE1NXRnWq654FAmHgicTQ0BKjTx4ra0UWKNYnZxAiqWIUrIUcwhub4cCECIfpk+GfohMAR4/eoxUkrrSRO9wskyOYohkkVmvOu6fH6MkNMaQMmyHiZjKJFUbc2j0xkKP9zXOheJLlMXDOztPluq5P27sy7pS7CwBLQVZJBa1LdeYKOteigGfn8WARFQjsKY+TOAaYnRMYzg8oxRdW9PV7cEKkkmhSP9diuW5ldLB751K71QW+nNOCVtVpJQw1hJDiWZJB/m2re3hNQmtSlMghIj3pYkqlcHHhNaSeS7TTG0txaRe8kEba5FCs9KKxgq0lriYmFxAIqhFS1d3JAljdGXyOc8ISiJBipG2bsr0NWcabZFKHiaPQC6NASFKkz2l8hGcg5jRQrHuOpRQyJzLhDVHDtQbkigFtfCZnEtudUlfKT+vXHOSn3MF8wvHv7Lj510sSqGZwkyYA1ke4hSQKFPwwUYXDbYLEYGnqYrpVilFzAqpYXYRH4qHxCoJFnIKpUN0+B5GCLqqopaeoAXJZLqlYXZVgSXEQHAR0yxpu4rsR5bLmvWqYRpmqrpGacE8J2Z7mELY4i+bp4IyDkEU0AoCrcVhrF9G9W2rUFnhppbRBeaDFyC4zFXnGOaIJyJF5ktvAUIjsyKGxJwjo58PCwiHySplA6eKTAwXDiCETDpgooOfS8/HyPIi6VAeXBjKdNQl2rpCCcUsPfsRQpqYpggHcMau9yijaCtJJHHdzyx0hc+5TOpSYpwzUQpUCmilcTGxnxONdRhKePscAjYV4/k4R+pFphGCSinapuSzSVkM1jlmfAzcOo9PnpfPTrBakVVGCUnICaM1jrJYh5Se6+CFTCXfsDaEePAz5gKgCIEyfawbUnb4QzQJKVNrgTEGozNyEoyjJsIBwy0OOUeSFCNNbYsHwJVOq7VlculCYp5nooxIo9j2Duczq9ayamqikQiRC1AjN0QfqEVifdxhrMTfzEzzxGafaZykZHyXDZw1BSnufaIfAi5EFAJkxtryYLWqxpoKBLgU2O93pUMtLDHC+ckpUXl2w0g5MZEUPHEMCAJSS7K3SGswFJpvSvl5OPuLvUXUDa+8eIzRmS99+S0eP9lRkZgCkDWmspzdfwlRVazvfYT9U8/N0w1TTNRWlfePRMqZkAv9tnT+D9fxATDzf3v8S58A/9wU8l/491d9+p/5fqKgzZ99aS6T8ec/7ADVERlEiggUGkUgMWz3PH54gdVzyfzbT6QETy63vPlejdSS6mZgu1siyfyTn7xh2TWcH3Uls1GV9zelFikiEofsRwJ74n4kB8/KdsjlQDCK22HCh0RlLetVi8kZ7w26d2gbENNhgwNMZO4vV5iF5PJ2x+bmGi3LpEIoQ1aaeqHYRcODpxMfXA64EAto6zBNzc9PeD5Uz+UkzS6QQmI/O1bKAKHAFgAhNAW2lZ6DCCARU1l/IoAs91JXN7ggePLoIRnHxc2/ybJZMN/2B0mtREVFMhpcySn1PhJDxtglWsEPvPa3UFpSNQ3yIAMLPmCUxtYNzcow3I7M0SNo0FKi0ExuYp4d4+zwIZOzAaERpiYHh9GWRbukawxznHA+kvYTGYVtJEth8LpDikxnBEYorrdbQgyFsOkSIYGPTckBjmVSoISiaVtMpckxEINnnBxN29F1FXVbs2wsMie2uy0CaGuFNBYhNcP2kqYucsScE7vdyD4Lhuiol5lgFIOfuNlOPHz/MZvNwDh5JpcOAClJaxRImGMsUi4pcC7S6BJ472Li5mYipZm6kmRMoU0mDzmzv3/C6apl2VZApp8mxmGm6bqD33pCiUxKiSmk52AyUuTLbz1mdp7RCybnmKYJoxVtJagbU4oHqQnBEUMpBJBbqtqgdKauMtthZOinQixHMA0TxIicCtkyHSYNWmiMKvaJFAJjDOz7gYubG3xIjLMn+FCIsgeflNZQ1RJtDZmIyBWIouipjaFtDKfHHZU19LtVkeBNI01Vs99P+Jg47la46NkP5XcpfnXP6DxXN5F+NIyzY9zPCF2yma+ue1xMSGUZ3ExlS3B9jOU5XJkacqKt66JMQBByxFpL8JF5nBBk6qoDKZjcROMNxiqmyTNMLV0/oqVg3S2wVVWKxehIlGsj5TJZyiIxtMUioLTEe880Ft+o8z9b8MTDNV0bSWU1TWMAcK5kFWedISoWXU1lCuW1bQzWKE7Wi4Ov/OBBFJqQyjXjXAGfxByJyTE5mGaHcxGlSp611CXfWEuF1pLKShZdzel6VfyRuUiW51iKNi0LECzlUogZY6iMIlXl+S2EZD8KnBJU1pTiRshSiGaYfSTlsuGqrMGaQuusjDk8OzJjFAdGRmk8giFFgZsDMWnGuVhWlNYoaQ8N0AOgMCWElMwHwmcBmAmUkAfKv3wOD6qTIYQCyCkxOwJQzyev+UA/zgcljiD9bJNUlHUj5YybHc57bMxFIUVCiMNE+UAsNUqXYUsOKFXI+gtTU6nimZ+yQALPgGtkngN/YhRICdZopFQHP2HJDi0vyQffYSGuxuLZKsqDZ2kCB3ihhCJVl+JAKj/YAUQBsKmD9zKLXP5Pyn/pFuIXjv/vj5+3DPX3/DvfcdifRaZxJIbE8XHxqklgmgLOF8DMnAPBl4JES03wjqq2heKWS7DsFDJ+mFkfG6w2THPRaB+tavqYsCQMCWUtVivGacDYCu9K3t/R8RKjBe89vcAqUzJwsmexWHK00gV7PSTqqsIlXzKnVOmICCQvvtLgp4M8VAiEUIyzRyhP2zRYYTFKsVhWPHpyw64fkJViuVxwd7li1Ur+yU9/gFCa4FMpSKQhkJndzDjOhKx5+60n5MMEbfKB9x7dstlPGCPKIpEK2Uwr2PkCiyAfig+hUMIgyFR1kQSkJFDaImWkthqtFSlGhtEhpC5duXlimifOT05RInF+1lLeJYNRBs3EYlGhlaEwHz3pUEAerdYHGlnJx+zaln4/EmJkterwswedAIU8THOkLJlFp6sTCucxMI2RcZgIueRntW2BFHifi8nflN9da8U8zSWDSZbFxLuSg1lVmixKdzalMtGq6+IHOCBViTEyOY+RhuW6oJmnaeZ203N+dhdlYBi3eOdpmjXOTbgwMbtSlColqKsi3VECuq5GaMv+dkfKCW0rJheIwbHsyvT55vIWFxPHJyclfFmDtZJt79j3JSP07HRVZmPRI3NCWoOta1KMyAy1LhLpLCFliZsjOUWaTrFoljRtzdX1Hh8CU5jxLnL3+JyuU2gDY/AMOfGRo3MeffiU7k/9VoSQPPy9f47NbsDvNrRHHVXXcLvPvPvOYx689R53X/04v/gbv4Xz+3f4tm/7VozpUKbhxz7zGf7hP/gH/Ik/9kdZLCzzULLDErnEMMTShEion10UcumwPjep51z8ys+mj1+11DyXmv4c159vSd+BQPBj6ke+6nsd4iIOvoxCA02UMsyQROJ0VfxMx8enfPITr3HSGaZxIKbI8XrBzXbgnQ+fEn3EasObf/J1Qgzc/fe+QMyZxhavEEqRwkSzXnPnuOW0bZlnz9XmEkmRti/XC1IIfOSTX8PdheGbPvUxqtpyu9/QDz1Ne4oWAasSP/zOLyUkxa+681mkytxbL+mMxY8D/TQz9xO5bshWI6LndG34H/765/hHn3mTz/34zyBlZgriq4r1f8n5PGQuvnDU8vr5kroKzFHgs8bESE4eBOymzBcf7UuMDmCkpK2KN0brck/OznHb91hb8Tu/5zfxm3/Tp1HXb9PUHctlRxaJOWQur/coqWjbGqEEd9d38Mmx2W6oqpbl8REpJ2SKJB9xk2PsS7B6v32KqCtUvUIhCP2GcbxFCU21WCOrmmQsKMH+coMRGasSWiuqusW5keBiIUsbyX5yDFPPU1dUG0shMFmy3e7oTlZUVU3YO/r9njsvnBL8TGUXSKUZUsCFmbkfsMaSEWx7j1aCyyePyDnTNk3xHcnIybpjO2uEaYhZ8ujdL/PC+THaOzLw5LbncrslpEy1OmLqy98fQ2CadogEJ6cnnKwXrLuavneoSjA4T5hmjDLYquaf/vSbnJ6ssVoTY6a1FYIiQzbW4EOmHxzXtzu2fc/JuuH1F+/QVBX9PKBEoqoKtGw7RpQUBDdwcrQqAKLJEXPizXefIqRGZIHznv1+Iic4PW1ROuNcQASBtYpf8qnXubracHm9I8TiL7ZVjXMjjYB123F8Z8XF5pbbyy1KG24GXwLUZaKtC1G3qqoyqUGw3+85WS9YLjruni6pjWaOHq1MsRVIBVmy39+gbYXShnGa2Gx3LLuWR482nJ51LBYVu10k5sz5scEIy9ObnjkEPnbnDJc9fUrIXLyXrVLoZcXN1S3j5OhdUcH0/YDWBq0KhEXkxL27p9TW8vTp5SH3ryZEz03vqGuDih4pJbataVtNcIm6rmhqQ1NpjNUMt0WBIo0sec/KcLUZISeM0WSpCX7CT47KZE7WC44XC9wcGf1IZct9iqDkHbpAjgl/KGac9yXKaY7MPiBkxofMNIdinzGCRVs2/J1t6OoKoSXbwTGMHmJAaxjHiV0/8/CyZ3AeSaCpOpSU5T4JE+cnx2gkp8tVkVAqcVinQ5EoKlnWFCnKPTbObLZ79kOBLi0WK5rGMo4j0+zQWrBcdlghqUzZZDrn8d5hbclkNLbCmIrtbs8wjCipmUNRO2ljeXq9IfiZeycrmrrCaMPOBfb7AWIssJbWUlU128011lbkVIpt5xO7/XAoLMvey1aGRCm0+2HEBY82Eq3A2q5kVE4jWkqWi2fUdw6gF1EgPDkdVFICFxPeB7KUzHOkH/pDfqNEaU1KGTfPpFhAPM9UOCGW6ImcIaeyZ0opl+xLYzCag0exqIGeUVafKVCU1kghS/5lDgihisXkABGUUhCDL7CfEgNQ5hw+HJ7k4qvAlIpEKngSyfMJ8Dw6cgJjDVpLpmk8FNSH15ZRLDFFHj66/TntCX7h+Lkf/0plqJWGqinTuFkIZu+omoJarrRmvSyY4lov2PmZx093CKDrFEYZCC1huiJnT11Jnmx37C72CL1CWBj2nhQc4zzwtJ85qStWTYVqIIfEvZMK5xNfeXjFzXbklRfvFiP4LMk15OBpbMUwOYxO5JTYbvZ8sLtmcbxidolFVbx0XVOzv+mpKk1lNTmVMNa2adn3V1ijOGobSIlhGvE+0dQW3QnQnqebPR88GrBW0BhJbBMuSlpdobTA6harBD5kPnqnpTY1q9OGy9uRH/3Hb/Pu+xtsK9jvC9xi0VSoDKPcIIJk3PkCxEkOwUBVK6ZUJEPkSKUty4U94MxLpyyJ0gGUFM+SFpp9v0NaxdOv7Mgx0doldbVAmL5kSyExUmHryHYbqVrFS3dzmRLLYrqvTMfeRfpp5umuZxxmXnjhnLpSiFQ2PI0V1JWlHwYyBUW+H2b6YSal4hdpmwpr4OpqYnfr2eCRskwYj86WKKNRIhOyJ+pEVTW89c5jumbBca2otMBYRXdyxJPH1zgfaGpbOohaI0yLNpnTsyV+9gzbgeDKpCXEzOgDvRtQZJSOLJYdbva4eWDdrVgtzwgxc7Pr6XdbrrYTXVNx57xkN03jCCLSLTrOTk7RUnN21iIVjEMg5MS7H1wjpWe1tKzXNauuozGW4D1SKcyq48FbHzD0PX0fMdrQri1nJ6c0WnGY5fEz77wPV5KlXSGF5PJ6z8XFjrvHdwrhL5XJkkGR+mt2+1t88xAp4Or6knFKHFnF1M+Hn7Pmkx+9w6tH8O5W8tkvvMELm1u+4Rt/FcqWfKf+9oKLh19BZzAiMzxzEx6meFmUqBeeexEPMy1RkOgFlx5J8C/ULwJxIOLycysYheC787+PAP4RP4I6NCJTziUfMBep7yHRA0m5FxCZKBIZ2PUjDz64oHr1DiTJbjcw9HMpJJuW6kizWja8X1u8k7x+7xylFNpKosxs9gMXTwIfPHnEk0ZDgNu9p8dDgpPjNXdOFtxcXfPT74/85l/3Keoqsp+2vPt0h5CSF/2W3ApEJfjer/k8sVsR3YrbzTXb/YZRSewcsE3HO0+u8AdD4qsv3qVZLtjfPGLub8r5zQKyKbJR0r9wyl5//aP85R/8nwD4o3/iT/Mf/p7v56/++T/LFz7zdw9RIAJE2bBIBUIVYIU4EJAzHCabiiSg3+0JMZBSZHYjX/zST/Ft3/a1fOOrr5LGmRwjUisMiVVrEVLw2qd+FTcXHzA9eZc5eR48eMCyW5L9SL0+4yOf+k6E1Ez7DT/+t/4X5gBPnl6yWC3Yjw8gRl564WV+ya/+XqqmQxvL1cM3uHr0ZerasHrho7z6yW+mWZzylZ/4G7z9pX+Kdz2Nbfnkp38dRy+8TnRlw/zjn/k7vPXm56jqlrZqOTk7pj1doZQlCE9TLXnh/n0evf8e/e0tummoVivqRcc+evr9ntk7hKogetwUQAt6NyEynBwf8cJrr+DfeEL0ESUSr91fo5Mi1xahJafK0tQLzGnNbBrG64FKC5pKc7qwhN1AoMBpmsZQm1OO757ggmPabjBZ0dqOF88rshBcXw+kBC/fX9PUms12QmqNblbkrBgnx9OnDwkhc9R1LLqKLNdIMqt1xzDOBTRUt/T7LV1TE3zky+98wJOrG773u34NU8g8uniKGzxiLNfKtp/wvkcqScTgnGdlNLKrOF+1CKMRStO1HVImnjy+YBgcy0ZzvQn088xLpyteeeke17cjX/ngMTEH2try6kvnxBgxleFqs0OahqbSdG1NbQ1v/8xj6lpjlaXRC6xWnK5a2q4jy4qpcaxWmq4z3L9zws3NhpvrHW9/uGN2E4+PDEfLY9wYmdxYIqdUpqsE2tQYUzNNgeUsOKoEIqhCNV9Y4skK5wOL5QLvPWPfs9+PXPkdDy8ukUJzdmzwOfDGw8d02nJztSFHyapec//cQiV55SMv0ihdojWyIklACTZXe/bDxH4OvPP0iray3D1eIYRkO+5xIXHWFrpr8tCPniAylTxMRV3GasnTbc8wTdRVg5AKnxxaOpRpWXUdtSmNlGHw/xd7bx5sa3ae9f3W9I17OtMde1B3S2pNtiUPkmxjG6gYi2CSgOMBGzOagF04UCZMBcSBDKRCoBIzVoIJ2A6kYsbYgGXH2NiRPMiTJEvdre7bfbv7jmfe0zesMX+s3S1ZmKRKqVD+Q6vqVt29zz217/n22etb7/s+z/NjslfjnWW5WtL1AxcXA6WUIBOvHK84Pu1QSnDtyoKht9jBMSaXU8aFYn/W0taGtpVMZxXTVtOvLSenAyFmZNVm3e1wIoCILPZKrhwdcjBpmDQV86Kg6weOiwGfYLPOSJimKnn8kSNKJfDREyKcLTPbNDpLlSSbrkOXHlM4XnjxDhcXa/b3FxSVoijApEgKGhFzEe29QIjAq6ennJ9v6bqQE3mbHFRXFxG6gUnbEEmstmv6ITJvK9q6yAobpbKVph9Yrnv6YUBIyTAOhLTJ4S4yUBcSWcC0LXn8sWsoKXB2V/QJ9bqFwsaEdY7lpqcfRkLo6UZH9JFCVSiR0JVCqByIRMpxEiF5ti6zwo1sEUSCc5k9rjU+ZoWYjxmPIgWkGEBmqazS+Sbto0eplhBHrHXZkxqzNxPyfUG+ZgsRMJvWOeEVMhpMyB1PklywhkjXDwQHMeQCU2qV8Tc2y1nVDt1mnc0p3p8dLf6aXZ/xZPHPfttvSYu6IPjE0ANCcXTUUNUZmuvHLXUJ607QW5iXBWWRu76mrHjl3m2kkmitaHRJCAqbLHH0CFGBkgjjKU3NbFoy9o5h6zm9WONEYuhWeBE533qcSxxOG6zfEoLG20BwlqouKLTGukTTlMxnFYNztIXBWkFZFSitEErz6r1jht4RokUJWDQtBwcV0ks2LnLpBhCRykhmZUVpKg4XezRFgYsjG9tRJLlLfc1ygKaocXZLbz3okknb0BhN3+fOfVtr9uoKGQ2TxZzLdccwjrSzlnrWcLlNjENP3/XEqOicYDN2PDxd8cGff4kHpytstMgUOD/b0I+RcecFUDJidNrJhRoKVdJqB7IgCpMDA0I26COzrCtL2TQxeppmQhhGNt02g+fLguQ9ZVNTyESpJe2kRWjQqaCtK3rbEfE8euMm0SdOLx4ym7ZMmobezYy2RQAAIABJREFUWkqtePKx66yWK+48PCcKzawquX6wwBvHatVxfr5hf39OXWr8GJBSsX8wpWpLfvqnP0rbtLz1LY/lRLjgOD8dmE0LBudYrztWqw6ZElXbsDzvMLrk8HDKjRsLYtxQGM21oyOapuTe3VNu3b5ksZ9QXqIowEhsdPTb/Pvx1BN7HM4rPvLMA3zMWBgpMqC6nVZ01uFsIPpAt95gveCpJ66yXK+4d3yG0QWP3zzC2YGx7+kdpKJhkAXdxYY3PH6Vo3nNtYMKoxUiFLz08j1GF7h5/Yhr+xO8g3XvqfYavO24ODnBObj+6BFDt+bsfMULt47RAubNVZ584irlYYNzkSZoprXEmoqyLknJ8/Ivf5Du7ILP+z1/CWlm/NiP/Agf+uBP8MEf/zEWe3v85//Vf82PvP/9/PMf+AGef+ETDE6xtVnOo8ldSUuO6lZpV0AicHEnfxRhd51yB/k178W/Uczspl2ffPyr7VC/sgH2V/zfB+A79O/IvhCRi89I9nak5NlVszkdUkSUyCD0nBibKKuWtz91g3mZoe2X/UgMDpMqdJG5ni56TKUojME7MClRqMT52pOEZ+0svnd4GxCl5rFHb3BtMeVw1nI4MyQ8X/yl7+CZX3yVD/z8s9jgqcsGKQt+6fZt9ts5+23DbF4ypMhib5/QX1LXkqas+Y/+1C9hTO50u51fR2ZdLf/kb0/54X/ouXvvjPf+Jsdv+9bxV92jBZL7H/jTPPbYI/zhP/In+WN/ueOPfuMP8dFXvovz1ftJZB6oBD7+EyU/8b0laxtYTQb+yF/qEZ/65nzKfeK7/sSEBy9nf9E3/WeSX//V8I43XSeMlvXG4mNESJjpx2he+noeffrXMyxPuHflD+Od536Xo/zn6Zj3vuHHee74j/PyT3U8ceMPMT24xi8//98ye8cv5mCOmDBa8zk3/2cu+g/wcPN9+A/+Ad791X+Qf/VP/yYvfPzj3HzsTdSN4V1f9lu5ODnmZ37yn/PEkwe4cUMRDPvtHDNtCKLgLV/2TfzYP/0bMG6YzOfo5Om2G7p1l/2TVw544fbzNFXDajlydr7ictOxWOxxeDAH7dEyUkXNsIGjx6Z065FnnnkVKSRf8eWfz2XveOXF59ifT1jMJiTZ4OwISrPuLNv1CiMi2yHy4LLjYL+h1qCVZztaJu2UMCouV2uGMHLt6j6nyxElDNeuXGE2bZk2JX23JvqIkpGQHMerC2QsODqY8fytu5wtl5Rao5LkyTdcyVLklO+F+9MZm/U2K2G8Y7NeEYKnbRrm8xlNNWW79dw/vkPTGsaYp11aRKYTQ1nXPHyw5pnn7xJi4Mmb19jfm1FWmrptadrsE19dnOHHnlv3Tvn4rSUxKKQcGbuOpilopxU6CvDw1JM3aBrD0I984uX7XHSO937+23j0yoJot4SYuOwdy23Pe975dtqmIQTPg3u38b5Hy0NC9Ky3HUlIqrrFDh0HkymbYWA1jNx7eIndWpppyfn6nIvVOUYb9qZXGWygW11mPm8tOV8NRBsxjeTKfIKSitPlhjc+8gg6BURy2WctFTYoYvA0tWbwgcvVyF7VEnSkUhCbkot1z6uvHLPQEy67JevlCqMMi4MZQzeytQkfLHt7NW1d4t3I049eQ0joXUAmzc0r+1gf+cXnXuX44pJhHJi1FUeLCSFku4gpEhC5dnREDI6h7xgGx/F5z2brMSZSVgapCqITDKtLFvMZ9aSkbSdED9EP7B3UtIVBSBi953w1EMZATIqQ4Ma1KVf2Wx4cj2xHx7brKGTkYG+GEIaL1RKh467xBLIoeHD/PPvzpMAlyaobcW5EEdhrJ5iiwipB8JaH988RKk/VUIbtdmS1OkUpTaFz6I71jvVmy8HeHlWlKA1s11u0KZGmyM1vEjF5FouKGGHTecbB0veWSVsTCVRtxleMo83WmKDou5G+G3JSb6VRRZ5+nV9aVuue07PV7sQ0MJ81HCzmKF0ymc4pdE77t3YAAWVZgBRcXi4ZB8c4ZJmqLg1KC0RKlDJhlGCxmOdpHul1eWupNcNoM1cxgLMddV0gKkH0CYLOQ0vlqYtqx3hUOQynUGzWG0pdQ0h0g8MHEFgQkuAifb8lEVGmAJGo6mlG9Pg8ue17x9APOOcoihJjTJYMDwPbboMPESFyCI+UYpdZJyh0SUzkPzHSjT0hBpqmhijoh910cvf/kFry/Cv3/h9rj8+u/+/r3+lk8bkXT6jqBmcTkkBRCm6fZECtkgKpsm7ZjR5pJLM6f3CNKVBKseqWoBVKFTRFgBAxdUA6kQ3fEmLcIJXn3un5674/LRRVqanLGUPwSO0RUXM4n+BFg6HAjm43Ug8UuqBoWowWiOQojKItDKnKdkBlFEVlGA9qnKsyuNlIrh0s8nR0a1lvPXuhzkD2QuWRPIpoezo/YAXYQTLbz9ybkMhSPTyCkBPHNhFcQs9qNr1Fa83Zec956JlOp0x8YvQDwziyGkfafuTB6Yq6VEzbAiUTbQmLRrFXzTiYPM0QEiiJt477Jx1ny46z1YYQYtbth+yfWK0tw+AYQqAfB6zvkDJlP10AO1iEklkbTw72SH4gkhDG7HTrEHVOs9XSEIWmGzMDM/Rr2rYkkjcEF+7ATh6x3A4IcUaKBqMNp5djRgcQMTpyOo5cbNY0swnOjgyDxQ0e1zm244gQmlWf+VGL+ZzCKO4+uCAkgfWB0HWsNw178wmL6ZS9yRQ7WjCCplKECEl67j+8ADIbSLClKh1nF2vqiWaxmOG94/RsQ9dlziRR0K17bt+1xLggjA5VZJN2YTSVUUTr6Zcjw5h9A0hFVRk2m56uc1wuHT55jOiYtwVazRB2wG4HmlYjK8Nms2G7WnF2rCmLgvVmhBRyOItKdH3uem/WFnGeTeZuzImvy4/3SE32UKoCpTV92HL7wT3kKUihOGgn/Ce/8SHWB/7MT94g4ZisLrny5s8l6oaYJE+95a1MJi0lhk/cepG/9z3fy63nn+fBwxPGYHAxgMj4lJgiMWX/i1KCxaTBjp5tN+btRGSu0muWudf8p58sNj6tIvzUh79i+/rVvIvpV3xVSonceUIgZX9KyocTACSIpLLfI3hSEkQkwQUm04ZrC0ldQjVEUgiZhYjEBRit4/zynPV6TVU3oBWrzcDpNlBIxZWDPeZXa+pCk2Tk2rVDCiVoK8PBfEqhJB/8mY9zeTbQzCYsquwNOV+OHM4a9pqa0miO3R7KFLzvXTeoZeT5Z17lS7/9J+htzbse++vM66eJybPsX+Bnbn0Lb7z6O/i93/Q+/uH3fRtRSr7qK7+Jr/6C38QP/vw38LZHfhdP3/g6envK/uStvPDgn/DUF34rUkq+9Evew7/8pa8H4Mbeb+Dzn/ijNOUNXnr4j/nw7T+PlHD9kcf57//L/47FlX32rzk++Nyf4+WTHwbgfe/6HvbaN6NkyVu/6za/55v/COcXS566+R5+89v+G4L+GG37Vm7Fv8qy/xjvuP6XKeURY5EL+YvzNXYvIKVg6TOf7emDp0A4+vhhqupzefDCh3n7u7+Kl16uKU1BoQ3O2t20WmDMHqauOXzsMaTKATmTScPF5Qlnl4mnNh3HZxdcXjqWq0i0sLk850E58oYnrrM3r4FEWzesxjXL5SlTPaFspxRtix8c1noef+xJonXMqsC1q9dYDz3bbs1B2yBLlbFDNpGwdDbRTFuefOIGMQa0sogwcHi04MrREYv5nMvjM2ZtRe8lZVVQ6kTXbWmF5CqKssjdeJk0SSi2Y6QSksV8QqIC65jUWR64XS/ZXp5zTGB/VpKkQZcGKWCqW7z3JO945PoRe3t7xBhQIqtsUhQcL7ecbwZOTzpcSGz7MfPpVO7r9zjW7hLBktVq4GK1RBfZVzUOO99qK5ApYR0czGeUhWHSFhitePa5l2mmDZNpjbOOy8s12pQEDO948iql1AQl6UeLURFTaLTQiJgwMgd7+BR55NohTzcNh9OGrrMsL5coJSibhoP5hOOTB7uYfkc/9ECg397GaEE7bZBKs7y8wPWOWkbOVxtWnaVtFdf2J4iUmNT7XNuf7QJbDNut5c52m7MFRpjWino6wfpIoxu0UWyMw9tt9twZnQPLTA52ikGwP5sQgEKuESGych3WCuog2NeG/SdvIKynD4bIYZb/OUfyUw72jxhdh/eZebzpCo5POyIZAVYVhg997Db9YAkh+zETgm5MPDzvmc8qykITY2Sz7RntQ7QxhJBB81VpmM2mtMaQRGLrLCcXa2KVp6BVNGgl8ETOVz2ByHHK092qLOnHERcSZscH/cTth7x4TyKTohstw5gVGvcvt/R2JCWoi91Bi0hZl6y2W+q6RgnJemsZhsyKVCLSNBl0b22PlvDItasMdsC6DIyvZw2HsxtoJSnLzP29vLyAgwlN22Ctw46WwhRMpxO8tQRvGXYuCKUqrM3oIlMpqknL/uSAi+UlJ6cr/M7OklKi22yoypL53hQhJM4FlstNPh8qxZW9lsN5SwwBN3QUJvtDY3T0wzp7tnf3wBASMY7ZVuNjVnDpnR41egpRoI3Z+XnhpVfuo7RGl9XrTVZNQsuMFBFGYL1hMyTsKqfT1qUmEVldbKhLh5aa0Xm6wbIZM7N1bzHBKJlfJwmCC7kQ1nK3h2RVzugCse8wRlMYSYqBohC0zQwg8xGJtGVB2xS0bUnwASE1KSUG6/DOZS/4Th1VlAUSRdtMEUJQVwUk6Icd33t3FlBG8fwr/+/1x2fXv/v1GReLs6ahrqcMyrHqtrguopJiGHoiCRT4GBjWFnTKMcZaYiqdZZFIosw67LF3GANrZ5nXFQAuJoKHvk9cu6YZhsA4JspCcGU+p/cDq25ks/FIoRFG4kKkKQs0CUViPi/wo+XK4QGJiLUOowzr7ZbZvKJUkUIbymKCC566LtFGkAicXJ5zcqGJ3jJtmrwJrCPeS25e30frhHMDLsC8neEq2G47jFaYMnuoopQ4LanamtIIkCN9DBwc1pydX7LZespyCkXibLNkOiuZlS1+SEjbMy9HjClJztF7T4rQ1lMikaeOSqrKoFVBFI7wOVdQuqLrLW4caaTCjiMITeciq67n+VcuGHxAmYq+77hz55xNn3BKkUQk+oE4DEhlOD1ZYUMi2LzROQlKaUzR0yVH7yRKJJQWOCcZLrc7GULW24PBjmt0aTClQRPYxoEX79wDBJOyoDKSQCJKyay8ABGJBGwssf3ItNVoExidA2kYfU9VSLpNNunH6BEi4B+eUhV1hvlWku04smhnSJnDd2IIdF3HI49cZ70aeO6l53DOUzcFSiqeefGEw6Mp00rh+pGkFPv7DcFrVkvLs/YhyQXCEKibOVubOFt3CJlB402Vu87b3rPpO+zgmU4KFpMpg4vM5zXXDuo8oe1LhmGg7y37+xXz6YyxHxhWI7YbGHAImVlqD07WvPTKimYiGLeOw4OaqixpyopJVXB5aVmuV/jk2TvYp9QtWsFgHTkrTXK23tJbR0yR2888Sxi2/MYv/2KOPverQWTPxbVHjrh2XfP4U+/l5Pgr+fH/88d49pnIg+MlTpWEnEWbExiJIBIiSWJMlEWRQd/OEVzYeRAEkmxiT/8WL53YGThefzbBryajzF/7tO+FnRwmv0ZOUJRcOTpg8D3bbU/X7UIdMuCKsXd5hIbEusjNm1f5+t/yTnBbTs82VMYw39N0m4F+44hJMoyJdTdQ1AaVAt1qy8NNj7OBaS2pjGG5yQ2a6B1aJOzouX3P7vA1NU+86QrL0fPwYsWtV08IKfDOx2/iikRRldw5/POUheSdb/p+Th9seNBWlKXm+uIrGR48wR/709/GaBNVNWVRfSnlV78DNYdu00FK3Pqo4sOPGb7jG6b87m8u+Wv/45fyeV/2Zbz40m0AvvPPtkwmLX/8T30nIPjiH1E493b+0B/+fSymNX/9f/1+nv3+2/z0T76f/+Hv/Q2+7+98D3/pf/oe3vqWp/nXP/qDfOtv+xJOTk/4iwd/jvPzc7RQfOd3/im+4zu+nT/7Z/4CP/aPFH/yW97G3/m+D1HzI5jtAe/+jX+TOx855uFz/4qjqzf4nH/vd3L24BYv/6P/kPPTNd918i0ILfnbv/Vj9G+TNM9/Byb2nLkzAMbLd3DtuS8nOUe36XE+8tLsJd76676Ra+3vRb+75BM/937qquGJNz3FS7dezk26GInSc/XGBOEs+Byg9thb38lbvvgrmMz2ePaD/4KLBw85364geXzrUKPGFIbkFZvNiv1Fi4gB7zyDC9y795D79x7y5BOP0NYNRVFkfmyduFxZ1gmmhaEoao4vAyYYpKi4XA2cL0d+5iMv0neWL3vXG5nUmlJrYtly0Z8xnZSYqsJawTjColXEsCWlSFs3SAEPTh5y9epjNJVCNRqEQipNAay3HcO2o5CKWTNhjB2XFx1SCOqyxRQFZS2YFiVFv2HZdbjOM3iLMhW6UNSVpqmz102ZivXlihAcvQuEkPEdVw9mqGSJHrTeY282RSkJwhJi4Phiy+WLD6nq7KV65ZWHrNcd88WU/SsZUyFsQEtygEttGG3HrC3zwT6mXNAtLcEHmlIg48C2W9FM5wSl2fYDelwxaUpMU6KUIYrE3dOevrfsHUxYb3tOz9coJVE6h5cFNdD1gaGPiG4gNQkRc3Ntf9rSlCV7ey2pKPicL3oa6SL4SPQ9ta54eHaBC4EQ4XBvysXlJWOShGjQXiHj7t6Z4O7JOidF+ghGsLno0FXJfKpRSuK9o5xrKlEw9p5xdDRKYuqS0a1wPrJcdQQf8p603yKlIu0KPmMUUQompqRtK3ofOLvYsDzriaOht1lm2VRTVhcrpBi4fnUfjKYbtxjt0KVi0rbMyeeSTWcxMlHWiqIIFEiODucoqXc+cEsII5XWlMZwutpglASb2K4GjhZTJouGJBpIiUIbVhdrts7zyukZ3gamxlBPMn9vWHe7QKPAfN4SXYO3jkJLpIoMO09sVSZWqy3bfiSkHITUNi1aS6Tc5ntRkhijaKUkJhis53LZs+oCx+dbYvRUjUQZyfnlGikknbdEJEJojFjRjz3brSOFRNj57ew4UpU1D8/XO/lknvThItcO9jncm7E3K4kxsFmN+TOpJUpDVZesl5cZZJ8MQmgu1xuCD7ggEFJR1gYh5M4CEOnGkdW6z5kLvaUoNFINIARaZe2MLgzH647g8zBgHAeEVJkDKcxOJZeQ283OVRiJMZBk5koFn3YhZ46QIk1t0EbksCwnsdaz6TaMLjHaFVIIGi1B5ICiosx5GadnF3gX0Cb7g81u8HB0dPh6OrndnT+2dsT5yGYISALTuqSsK0Znc/BPXQJ5yBBCfD3o6rPr1976jIvFSV1QlpJCFXniEDyVLrGtyaNzmY3AKz3gY0Amk03OpczFYlS5y+8DOgaM0ZxvA7LVxOjyL7koiGGkVA1JBaJ2bLqR1lhO1xv6MeAdKJkZjP0Q2VZZRmrI8NihH0Bc5C6PgKaqOLnY4iS0xqNEQVlICDEHrZSCGByrbcdoIZE43HNIIsPgcaOiqSqKCnywGWUgDDYF7DZgdKL0CWQiRI/FooPCIPB4tqsRJXSGc+fRB/0w0A+WspIII3d8xkBZagSS0UaczfHXIWQvmB1HiJbC5Ej/ulJMW8O8kNgBGi1wTmCKGlO3dM6ShGSwgbpuWa02hN6z7CJM5sQ0klyP364Bje1zcTqWOao7JEEIESn9LrU1IIWiUBqxm7ZmWaBASpUDEUZBoQylLpAiM/maQhNjLhNCzOlhhTaQEj5EXPT0zmaUAjkFDRGQMvvN7KgRUWYsQPA4Ejb6nAbrHKKLRKDQDd5m3lCG8mYjt3ARF1wOIUglfgys1lkSNa9aCpkPGVlCobA+M/IKcvJcN45Ym3bNBUlbCpTK3cJhtKy3W2QhSY2hKjRSRXQpkaViHDzWjvgQ2GwHopHUxmbvmICYInvTisH5T1FtOjarLst49JwkJNZ7lDTMFjOCtIzeIrVE6oQdMy7h4G99E5B45ff/L4SYiyoTHDMlmD/yVnR7mCfH7BJ3Gdm/kji89gZevfUoH/7Fj+WACrmb1CU+6SfYdWpzsp6nKAxXrsw5W/YMo8PZ7BP8ZIH42nTxk/uH4DW/4muPPn2lf8vzrz0rSK/9BCIn4r7hDTc4vTzBR0/X+91nXuTQj7gLvJEQokNpxcHBFBUkwTrmsymTVmArTVjAd73zEqE03/5LT4EURO/wo2Uz9PT9kD+/zjNGT+lKZIjMmgyNvth4tv3Ao48ccW2xxysPz9l2mmllmFSS61f2EK2kntR81GuUhLPTFfcfXiJLRVEYhvAc124+wh/9jj/Bcx/7ZT78C7/A/qxkMm3oYvYTik+5Gq+t/+sDP82LL97+Ny9dYveewN/93n/AxdZCDPzr9/8g73r3u/nwz3+Ap9/2dv7B9/59SPDsM8/x4Y/8Mu99zxfyg//8X/LNv/Pr+cZv+FqKoqBtGz7xiVu7FkDi+Rde5J/8sx/hy7/ocd64J7jx+Jv4oZ/6QdIwIk4fsD57FVMYSplwXZ/lpQHOTi/oBsvxxYZFYRBqF5ZUKnx0JJ/h1iEErjz+Nk5f+jgf/sAP0TvL+775j3Py4Bhvz1FElM6HxqY2lEXEjQPe50nmC7/0QZ792fdzcPMx3vcf/yFefuGXCc7m5MQYiGMg+EgMGusyE60tC2Lw2U+83pIiLLcdPkLlPHWjmBi5m7BFghEED8vNhkpCb/PkbnCB1XbE9p7ROmTKIRMJgZACbaBqK3RtKLxB9CtiEtiY8MEhkRhTQIRgPbrRJCVx5FTJRJ7ysePOoiQRcKNF6YxECVbhpEcbxeHBnMmkxTsHQmNHy6QpmU3r/JlOGWESUqCpIq3xbF3BfDJBtQMxRsqmzFislBicw4XAMHq23cBisY/Wef9XpqJta5pCkpRg6wI2RmoNtVaMll1oR064FiorWJRRKEkGg4+WosoHSx9zmE6KYGRHVdUgFNpo6B11U+Gsx449UoKpCnwMrHuLkhWTiYLg8K7LtgoRsN5ldM9FwleG6dEedSGpCk0YBVpIdKly8F2MFFoz2nzPSSo3qYySBNsTE2zHiB0DzgZUXdBddDQzgalyc2ccR0ytKaVi6Ea8tdSmwifL+XqZRY2DQ5JoygJjJNoohq1jGHqqQmNSbnQrBUUS1IWmN5Jh9ISY8u9oCaCIMWT+oVE71UfMSanWopSmrSokCqJFypxgKpAIURDIDGZrA/3YkYJCKvB+RKAJMfv/BxuolMQUiro0FErhux5hJItJmcN8lMnhh6pg9FmhUhhJWyu8FPQ+4IJHS4XROSQweIePISNCIgx2RKqSIkpCtPjoqYqaQC4SnQ+EXbhTSDmcLqaI1vlaDdshcyd3gPsQE951IGWWTu7uaSCoyionioaMPitMgTQggkQpgfcW52S2WSQPMWJUgSInm662A/0wgjBobRht2KkjMhezLA1Ga0IMbDYD1gdG6wgh7kKaJCkFiPH1/D7rItvBvx6+F4KnKHPyrQ8eYwqqpshn0pTf97IoKOoCO1gKozMzMqWc2KuyNDZ9yv1WJImW4GSEmLC7ICRT6Nd9jCkJEhJrI2kc0RpCUnTDgFE5y0BKmfM6IggR8aMjpB0dwXpEyucdofTOE5nPVyH8qrf8z65fA+szLhbvnCzx9pjSlLRlScKzjT2TyQQlNVoEytJwsKeJARb1JEuHUkAAhZA51ZJEVSpKabh/4Tjaz5O00UZUIYjWkbymmiqUETw4sbSV5M5JRVHULNoaQ+5IhJiIZebrWGdJQRJixA8WJExmFW1lWEx2gF4f0UZSNYaxH/HBsV5DCGB9LmCEEFysRkqjEWiUSjw8PUFpvft3npfiKeNoeeL6DULaopYWQfZqVk0NIfu7yrpgc2m5/8oxk4miqASbfsP5pUekxHazQWtNWZYUhcFoBcnm4BclUDIx+BVFpYhREZKkaloEEmUF43IkBot3jrMYc9eHQKk8INnbM7heUgiLaQKP3iw5CokkCmSSEAz4Cq3gicOWtq3xyaIKQzckXrx9j5Nzh3U9zltGpxj7QJSOkAJaSIKAmAJaecrGIEUkjBYrEkrCzasLpJKMIW/uRhXIlEhKI31EOo+MPVVbsh46hn5ECMGkrVFBMPrEwX4+yK3WmSu2KAyLvSkxJTbLNYu2xbQl3Uay2W4gSepqwupyRIjAzWtzykKjVI23DnfQEkg5SAGopeFs5SHZzJxThkrVFI3i9OKS0TombcO8rgnWcXKyoneey22HQNJIy+llQhiFj5FbrxxzfD4ljD0pWJCa7RC5WJ3x/Pgyi8mctjDEMLIv58ymE5TQ9GHETCWXlyNl2+Bs4HK94c69c/pB8va33GTW5nj7jz9/h735hLHv0Lpkz9kcxNNtdjw9zxOPXeENjz7K9Kn3krTGI1ExoGQHDHhfgop85Gd/lgev3iUJUMnmrvZrQTY7j2Ai5UAZn9g7mvDYG64Aibt3LnnuE/cY7JhloZ9SJL6ejL0rPLP8RPDJYvLTKkp2XsdP8y0myIBuJYkkyrLm+o0r/Nbf9hv4+Q/9LO7nei4u+l0Sq4KU9xmSQEUIRM4u1rxy9wHYJc89e5crR/sIH7OkbjrhBd0RUuR8WeB95PxyYHCJupQUytG2NQnN44urPIrgqgko6XLEejkhJsG9h3fpsaDgzU9e5Uve80Z6NzIrpty8coguFP/0hyPrjeOZT2xompKnn77J8sd/N8/ce8Av6P+Ct73j7XzJF7+Xr/mG38U/+rt/BTUpSJ1gJE9nyyqjEITI13Kz2f7KC7Vb8tMe37kY8KEkIQnJv15I5riC3IlOKTemft2XvJc/+Ad+H1/x69/H2fkFX/e1v53f//t/F1EIQkpsN1t+6F/8KMPl51J9+dtIwPnZhuc+cQuhNG/4/H+fh/fOaGcNVx5ZUA0lIUQ+9rEzPGxCAAAgAElEQVQX+dz3fBV/67vfz1veeIOrVw75DSnxkY/c4UOrFYVWNEZRFYYv/tov4B9/91+k/YJ/yURL7t39GlS7x63nP0rXD1RVxiyZJLh7+wyiwLlEVUlm05LDw2sMw5ZhWHHzyTcxfGzJ0I1EV9BOa2JKrEdHs5hyeDSnUiqnu+7tcXj9Kn50XGwuaeZzqrqlFAkTIwfXJqSUGDcXDH1H3C652PaEpJGqoixLvvRt1znYm3K22XLRO3SCUhum8wMqGVltR+pJxdH+Hi999DY+OZppQzcOBC842L/CsF2yHEfsqWTT95xfXPLGxx/DSMHBbILSgsGNmKLk4Kgi2pK2yVDz1crywr371E1B0cwIouCkv8S6kfF8yWxS46JEGUEKnrowIApM6ZlUimEsCEJTVQZ84Pxixf37x6SYmMxairJkVlc01w9wwdJ3kaP9GTeuFpRGYvuRoizY29+DBNM6N/M6Kxl8oDvtSSFRLSoWbUXwgc4nHAXdauTe3Rcpy5KqKimKit4nfvznPkZd56n53l5LMyvZrFZIBFeu7CGVwnuww8D9BxvqytM2FUf7FZeXHrTGeke/7Ylx5JVXjlmNI1WhmNQlbVXRlhVCCno/UGnFrKrwKdL1juzgtgQtCcbghjwRKTREHThbbUmDY7nq0J3jYtVRKEWpFa8sl+zVNdcOJhSmYIia7apHC0WpNMlkTAJKcuv2faJP5DlcZBwjSggmVUlKA8poTKmZH01YLTtUyqqUi805s8mcGAPnvWUmK44ODgjBslqO3H31nJAi9WRKqRX9NieOhiSwLtENFqlA75J2nc32jNKMNJWis47TVUZjHF9uKIqCpilpqxLvPOvNksW05drBAiEVPkD0EVOlXHgJSbQu49V0LhI3dgQfOZrn71lt1piiIIacXF/VV6mrBlJkHDKSh+SZTGYopRjtwGhzgjsIppOCcQzcP94yjI7LzYZxtOxpvUu8Bze0qJ0vM4acAJqbVgmRIk1R5OYHOWm9qAqsHRn6nsuVReuC4/MLBu+JYYdNs56HqyVxxxkWKXGwmLE3n7KYGSQxp+BWFevNgOtBlZpqd61ihBCzNSKl3CzzIRdvU2OgKJBag5A4N1KqgrLSaKMZO48LKjf3k0JGjYgGkTzeDgjAaJBKE0aPH6AsEot5QawS82YK5ClfSDD6QPCOuirzPV8IDvdnhJgY+owQQoLSgsvVavcZMLvkVEnblmiZZfY+xIyOIxflQiRSyOg9KXORKj7jiuSz6//v9Rm/NautJQ5brLGcX2QZiFSWsj3PEesIBJ6rR4eA5/xsi1SGspCIFFg0E7yIeBHYupS9eaamUnnDkiFSG4UsSu5tOxpTUtclN28c0ZSBopZEdobaCH5wVE1B1ZYgHDENnB57UJIQAonEdFIxaRpqNeRpilBUrWQylyyXNYnIqy8vCT7xyNV92mmB0ZZEYvQQfUJaz95eQySzG33wbOyGYTCgEpWp0CJHmE/nJYtpS5ICpaAUArvIBn0bHNpo6rLIY3vvCS6P8+taM46J6bxlGEYGbwkJ+tESg6MoNNOioTIarQXaJHw/4m1OQDWFoqlmOxBsoOs2RB95w/UpjZmRfC5sZK3ZDANnd9f4HRunridMa8PCWIpZzRAsRkv63rJZamxXcNpZurXjfLshxYAus6TClBKjBCpJrLWEYClUgZSGwQ7ZtC1T7sb1PXb0tNMF276DJGibmulkQsQzOscYBrbjQHSGwswQKaJUoKprrB8IG8vNvSMKrXIQSIxst5LldmShNPPZjLI1dH1PNw70KwHRIeVIVWiuXqlwyXLebzMAuYtUpmAxFxwtWpL3uWPXFKhKMjpHWU6o68RsAnUlsLrm9N7AZdfjRWJSGpJSeCSNKdA6cHZ2yXrVU1ea6zcPaSYNfjtycnaBlxpVKAplsDHwcLll//AQKQSrh6dstz3TKwuasmBYWVbrgRfvX3B8OrC8POXJR4/Y9AO/eOuUt7/1LXzhW/bZOnK0u4TpZIqSa2plufnmp9h/4l0ErSElFBEYiOIM5AYprpL8nJ/90Ee5c/ceEIhRkku7zOKTIiJknjIrIfIBMkU2yyVf+IWfx2Jyyd07J/TDFiElUkqC959e772uLBWvcf0E5Cjvf9uU8Vc+EiIDoEVKOVL/yhE3Hz3i9jMVjc7PSyGIyefXkoIkMlweBOttIMmWyQQqc4/oLTFIcIHLe6esN47Rej7y8bskmbh164Tjhx31XHI0NXzJFzzNwfUF169eI0YYH7zErTsP6W3kySee4vHHrvDCrU8wDApZ1Ozt7fHWtz6GaUBuY0YIxUTTNJQm8NThIYvFhDdc3yOFPPV69d4ltz/+Ic5fep6v+U//AvP9hsGe8463v5ui0KQQ+Zrf/h9webkkpU+/wK/9ReyCatKOIwbf/I1fx//+/f8HUZd8+Ve+j+/+a3+Zfrvm2Y9/jK/7pm/gr373/8Zb3vwmPu9z38HPfujneM8XfRHL1YrTs3NMWfC7f883AuySZ7PPJSG4e7rhzvGGl194lqfe8W4+8Au/zGxxjZtPvJmf+qkP4Lst9+6uWK57Qoz80Ic/wDd+y7dTtEf84/f/HN/6bX+QH/hnP8h3/90fRhnFbFbwm7/inVyrai7OTghmHzE5Yzo94sb8zZycHBPqGcfP3+PwQGG9ZTv0JAwah02O/cNHKdwm/+zlhNneDZ5f/ygJnYMZ0oAyWeEhFbSNZtaWdKsNujC0e3t0MXH37l2euH7I3iM3aKZzxtUFMXqmV59idXHC/VtrCtPy5ieO2I5bTs5HZrOWpip4eO8OQ9+xvdwyxMjeYkLd1Fz2lkIW/NLPPU/ZNBwd7eO3G4KKzPcnxJDooiWUgZmuaF2NSzBtq4ztMSWEiKgqqqagwVFVNcv1RfbNmTlSKSazgZfuPuTh+QX3j19kufXcubgAIdgvKgojqSqBqRVHh3OmSqK1wdQ1ISYevnqH/aMjlClxvUVFmM9mJJ1o2gopFP2qx7mRvSsz6iLbDkolmO8t6F3mFwoPhRTcufMQXcx4+d4pVWVolEb4RBw9CMPZauTWnRNihKmGoR950xNz2rKkKDUbB4vpFKUMyQZWp2vauqEb1yz2Zkxmbf5alGyXkvPlCau7JwyDpalLpLBo0WKKxP7+nKpuOFzMOBQRUmAz2szfrBNRRPphw7ytmdU1RhluXCmzNFRExtHRjyNFW1MWBqOyP+/m1SNSannh3ktY57l27QpGCvrVkqovqSvN1aMDqqbh5GTJunNMZxOcD5wuV/SjY2sD43Zksx1opg0HBzMGl4Phgi8QMdK0iulegTSaK/sFwYachisTj96Y03c9y1VP38PaCIZxxIbEECzeO9xGsNl6kttwtL/HYl6j1cgwBOqy5Mp8nmWlw8DhpGFSFDw4v2BaB/YnEwYb2Q4De7MJWimWK8s4Ria6wY2R5++cIKRkfzKlKTTTomDaFpS6QLjMaFQmT7fOV0uGwSKiQpmW45NXmbQVk6bkkatTFrMKrQSjHdl2BT5oUhxZLBbMpw3ebhmGLQHJEBKdtXSjY+x2MmTn8Ekwryu0SpAcyTQ7hEfeNKURCKXZbDq2245tl9ElfT8w359TYJhPaqaTCh8jq+V6p4ySuJiyP14Erl7ZBwRj3+Od42AxpTBZwQbg+kAYekJw1FoQhCIqUFqhi4quG4AsLXUh0PcjUgm0zEniQhm6fsRHR9sYDhctRaFZrR2X6y2jDTmh1GjKUtBWNd5apCoQQuOdo+s2KGHQssB6wWa7JYY84e+7mFmKKRG8J8UtpviklDqEgFSKEGNWl8WMYeuGgWHwu2JcYERgUpfsL1oKI3EhEVOeviutCCm+zjV1Y9xNdj+7fi2uzzgN9au+7N1p/+CAEHqmjeRgPufK4gCRHCE4lsst2+2YNfwhcrg/xePZ2J6qLKlSBshqKZlM6iwJ2QbGEBGVoCgVe21FowvkbEFYr/HbLZvB0yhFOy843Wwo6zn70znn5w8pmoqZEFgHvRcoYzjcK9FVgbeefjMQUmTSVPgYEGoXkiEF68uAECPTRUNZ5DAeZGKzXlGqCXuLXMSs+g1aFMQQMFpRFAWKiiQUfXeeOYExMTqP2km/pJQ47+itzYfDskTEmDdrGxFJU1aCvssSR1MKmkZz794JVVWwN1tkuO44kFLmDh5cvUJZV7x468UMbW5ryrLK4PoYWV5eMPY9RVEym04hJe7df8j9szW/8Ox9JospR/szbDey2DMk3XLZO+7cPeb/Zu/Ng3ZN6/rOz7Xd67O9+zmnT58+vUBDN2uDLCIqIAiIgHEUCSqaxK3iJMakhhgNJKEmSoxOMsY1agXXcZBIShnDIKAsQfbutulumt5On/Vdn/fZ7vVa5o/r7UYzqZoMVTPlH1xVT53/Tr3Pc1/3fV+/3+/7/X5ypbn+hjHSBVKfIIQiLRSDMiEvNV2jY9qNsuwdTlFpQlV7Pv/Fx9g/PKZaOpRS9N4QTjxuoyIlS6M+XWlNnidRSuEbpITN0YS6qZktjtk5fZblYhXByyKgpGM0iubqxEjmsxV4QZokDIY5V6/t0jQNo8GAtc0Jq65iMW3w9JR5Cl6wnFfcfP4co1EGOOq6o+uh7Tzro5Q80WRFhpeCpokdTecFhMAgUZRFyqpuuHB1n/mi43jW4HBsbW/w1JtuZJglpLJBpAmfv/+Avm8wCSgjMcKiBcxXntmiQ+vA6e0xJsmwrUCInvPnd1jbWGP/2h67hzPa1pKmGpMqVrVkXi0QJuJjzk1SNJ77Hz2k3ByztjakmTd89M4HueeRA26//Va+93M/TqIEV9/8q/zY111jbZzwzvl3kF/3dLxf4OQGylfR1B528aHm6Jrm/rse5Tu/94do2z4WW8pB0OAkAkeMPxBIHciV5Jabz1DmKakxrA00Nz/pBiqneeDBK/zpn332RMZjCMER8EgBSgiM0VHe6aMkybkQ4ctPBNY8vv7vzycdYlEpVfSR/I3XvJbXvPylnNmYcuGRS3zs0w/ym3/wodg5DrHIPYFxIpCEIDl1+jxv/7E389ynnWZx9WHy0RA9SAi9xzcNP/Lsh/He87YPnyEfDRmXKaJdcbQ7pelbHrmwYDabcnn3MtbDs556Cyo1DMuEcZlTO8f+tSXrkxyZRt5d5zWj7Q1KocnQeCT/6O5X4/qOX3zh74OXYDVBSAY338bNt78Y31u893zhLz7B0ZW76NoVR+J2XvziF/HIoxf5whce5PTpLb7tDd/Dm7/7jbzm1d/It33H9zzxW73tn76F4aDkH7/lrQjg/R/4Q/7Lxz/FC1/4PK6/7jQf+MM/4vfe+bMEFxD5Nm97x79msrGJtZafeOvbed/7PoCWit/8zX/Ps571dC5dusKnP3snz3vuHbz0Fa/lJV/7Yt7xU/+CF7/wpTih+K6/+U287Gvv4Ku/9hXoJGX36hW8FXz205/h9//je9g5s8Nb/uUvA/Ced/403/HGNzEcDumt5drVa/zMO36S+eyIaxcP+Z33vIt/8uP/hNV8xtnrbuDv/+iPcO7JGq0S7v/cVe6565MMJ4pn3P4Mzt74VWidxMag6/noH/48VT3n+a/4HobjTayNzNr7Pv0nbJy+ieODqyz3H2RUGmzTYW3g2lEF6YiXffMbSdKMejHlw+/9Da5d28N3gYf2F2SpYlykvPDZz+RZL3k9TmqUlFy+98+xq12Go4xTt34NyeAUXbPC+8D+pft55J6PccPODkZrRjfdQbl5HrzFdyuuPPAxKtdQ9XOGw7MEq1gbabSWHO4fcnjtCtV0wamzN4AWXL58kS/c90VuufVJbG2u0QtJ23vSAPWsYlW3CG0jwLy1zKZzso11kmzAMDFoLMca8jJjgmE2XXIwm6OMpMwT+lkVmXOlprOW41lHmaSUQ0NaplgfONid8vAjF0kHQwblkOWiwXYNXR998ivbkmaaG288iwyB6eExFy5co+kDW5NTyFRRd8cYAtfvbLA+HlM1NY9euAzA9qktTJpwuLJ0vaOvahbLFeMs4abrT6G1pxhkKK2pqpauqxgOJ6SpITMFAkHVzFkuFvgkIysGmDTF2haTJugkRwQ4vnaZaj5DqgFFmbA/nVKYhMJkaGERuaIg5drhgnsv7tH2gaO9i6ytj9iarOOso+saNrc26boWpRIyrZiMMrJBxihNkGnJZ+68j6ZpIxcTiTOB4+mMetVS5jmjwZCDxZTxaEChsyiLRpIVgaoKXNo9pu6WPPX8ecrc4JRnvqpZTJcoQCpFkeUgBE55lIZHL12DEFgbj5BCYRuHkYo2BFbtCuc68jSlyAt6b+i7hslowGiQUxqFCJYr+3N250vy3DAZ57g+cH5jC6Fg93CPxXLF5uYa1lqs8yxqx6rz3PfFC5R5hm87eheonKO3HUaUtNUSbQTnbzgVJ3kh+hXPbm+DMNx/4TLL2TGD4RpCK7wM7E9XaJWyNcnAxtAUdORSd11L01YUWUmRD6hWizgNjJlzpEWCkpraaQSekQhPTLsGkwzXe3AB56KfL0jN/tGctrMo9fgUD65c3cUFT9dH7nOiE1KlmRQlSkvS0oAUtLYl8YFBUaKNZlk9jhsK9C4AirpxVHWNSjRFlsVgNhxlkVG1PfP5HKFTAoLZbIHzDudgNm9P9nvEpCRaYlJzIjkPKOGwXoCI09HgLeuDhMGgRCYpVWtZrGqkkJTlgK6vaNsG2zoyk1AOSvAQXIsxmiyP0m6I4TUCEFLQdQ1ZlqK1eMJJopSirhuWyyUEBUiUjl7OokgROJrVir6zT6StBh5PQxVUdYf38JHPPfJl1SRfWf/96//XNNRHLu2zO23wvkdrQZ4cMR7sU2YGIWKKo7UWYyRKKboQZaIaTapSCiNPfCOCMs9IEkFrelwIyCTG77resuoaEla4rscFQRCCJnhUD1pHqPlsPqeqOxyaIATWQeeh1IG9w5Y01zFh1Udy3cF0hjEqglKDj8D3TNG1J1MUb6maeFhOTYYUMJtFWZpFsGiWJ+B5iZQdSWJpe0/wLWmSYpIIg8d5bN/ifJQRhCDoeo/ta4o8QWlD33e0XUual5g0BeVI04QkSxivWYQI9AESpVlbX0MIaDtHXhRorcjyAmkM5gQW3dQtBE+ZZ4yHA6SU8TAeAuV4xNhJrjtTkaQpRimclEgRC8LSwMaoRPjAow8fkJqMPLVAwE4dihAnmUgiBVAAlmxcsre34uCgom4CxsSJknQSoSO7DeFpuhpvY7KbxMFJIqpOUlCQFob1ZEKeG3yIEtbeWZxzeHqkSFlWHUiF1hJhBHXTkOcZw0FkNw6KFKk924M16q4lySJYvNuckOiAkoHeKjqrQPf4zrKoY4cMHeIz1oProuyn73vaJv7mQgrGg5I8KdjakE9IhW27ZGUlaphD1xNo8L7DO4NSELSiczbuuVFOIGAtJKkgSSRZVtC3LdP9fZTUT0C2PbFUOrU+ZLg+IEkzXF1z+doezjlSnVJXFu8XrBWaZ996PdedO8XR0ZJLl/dRSjGbHpOnEl2uk4izEFK8u4YKlxCMCZQE0SDCksP9wKc+/Sm8t0gRTnyTMYYd4WPhT5z9KQRJYsi0INNRPqWMZDabc/bsWdInneHRRy7y0GO7UXL6JbsjLoDwIv63MsqTE63xPvpjfPgSbSn+K0/mjSfxciczyCgOMJSDgsFIUc1XjCcDtk9PMImK3dUnOJAnfzgBhMP2Fa1bMa/n3HnvAwSdk2kYlSWb64PoZQvQ9jVZm9CFnqaag5aUacnmGUO2ljHaLjF4cgzoBO8082WL9Y4gepaNQnbQdY7Wd9grHZdrx2rZ0bSeNnwDSaLoifiXVEB6xx9y6P4T9/ze3Yx0znw5pWlqNjZ3EHnCj/7wj7NYVoDEewEiIl3e+Ru/yzt/43f/ynP6n7/9HfEXPBntvuTlr4t+EQGFkUwSxdPPlVjveOiLj/KaV30LC0+U8AYFCKxz5G94A18gKoKfJgSNkvyxEPCR9/OOb/gEo8k6JjOcv/8BnvGhD7IM76Bte/wJq/EOIXiuVtz/vW/izgf2eNuv38nPfPRX2X7Zy7nytrcyf//7AcGPnFyqjxcl3/umN5GcsHzvu+cefuhvfx8/+L8+iNaad7/9+bS+RSrY3v4Erv95JknKIE/BBIQU2AAf+vTb6dropRsWCZqWV33LjVgL1/ZWXO5rlquK4AN5nvFtf/NH+PAf/jquOuaZL3o1L/rG7+CzH3kPvndsbC1j8nciueMbvpUrD93JH/+nd3H6+ht43Xf/KB/4vX9DMStITq04fPAjXH7kLlJjSPIMPZowl5KN00+lVTmf+MBvMEkSNoYlSabIOglNCvUR2mj6KqF2UC8bdJqxddMmeTlEm8ApeRZMxnhtg+HQ0C7mhKphuqxYHlc0FoT2KA/CxYh+JaFvV7Q+QSYJq8MZe5cvo3ZOo5TEGGibmt5b8lyBEPQBUJq2r+i7lvWtMyTasJovov9/MGCyPmY4KNgoE7QYIRQ0vaPuHVonTMoBwVqGW4r1YYYH1obrWBxtmyNslEImeUnRl2RFitaKJEnp+o55N6V1jnKUszbKGZcpyTDheLrg4u4B1nnW10cUqWI5nzHzijyP/L9ltUCIQDubMp548kFO37YsDx37Sxt967InEQ7h52TpGsp7RBAgJCpJECogHGilyRONET3D68+ilGBjYwOtBHhHlhq6zrCsaqSWBCmYTyt22ynaCLJCIE3CfNVy5fJuxEElkZnntSJoaCtLlwXy9ARa4wNV7VhWPa7vMQgOp0ccHcK1xZLWObbGA05NRhR5yt58Sd91DLIULVOypCBREiUUbR9Ze9JIUh1QKsW5FCVAejiaHsfk0Kbm4Cihtw7vYtqsSWMq5qqqSVXGY/t7aKnprSQQeZTBB1yALDEMSk1263kkAu8snXXsHS2ZzipO72xR1SXOWeSJHPvUqW2mR3Pa3qJTyebamO1xDr2m6h3LrqVvamQqubZb4XuLNJBmCZvDIUJlBNOTaEGqHdkwyqq9bbDO0yw7tLEIqSAIDlcdVVUzrysG6xuIvkMHhxfQEX2I0lqU1gThca5DCMvGuHzieRgCUZoZHEbFEL0QFFIolBDMq4Z53aN0PCMFC6vGslo5EILOeZq2i/J/cRT3mzRIFgg8fdfjXIMPIQbUIbDW0XVt1PlkihAkSZLGd6GLIXRZkUU5vjEUiUbiMVqQpilSCTKdsDmIwX5BBJwv6PqEtunAx/NREAIvJEIErO3xOJTUT2QrKAlaR9Zkbx1db/EEtFR458izFKV0bFB0HVLGeOEgAtpopNQ4ZwnElHtxIlnNsoy++8pk8a/r+rKLxelxxWzZnci6QAhJlkwZDQdoJen7BvCc2Y66/FVrCS6abpVQJGlKFwISj1YCozWqjMWaVIpAoLKO3nlc2+BPQOAmiR0OLyR5mtH3NiawBgguUJ8cK6UMaAPVwuLpT5AdseM4X9YUWUKRJQQl8HikkgQE1sbN2lQ28vRGKdZZqjoC71UiqJuoVRciasoTAqtlS5oohHIxXEcKQEIPzjuCjxNGzwnsOkQMQxAQRAAh0YmMoQdJ7J4PhkO8tzgLQmvywSC+2NseYzRSQJHlyCxFKoXvLa7rkEBZ5JRlifeeRb3C9R6VpgxGgnOnG4JQNI1AhpOUUx3DavTaiLZtOdyboUrodcDLQGcttmloascwi/HLrVUMS403kqqqcS6AUHjfxQeOs0gZu3td3+OsjQ9V75GxbCAkCm0Smq49kWCkeB8IwdFZ6DsHOOqmjYmjqxajFSEBXMCfJMQVuSFJEoSQJFqxNhxgao1KHGmqEMEwm86wtsc6jQ1AiEEOsRAM6M6hJAg0gRO5RG/p+oDtHGUZWZmUkKU5eV5QVyuOjo6oI8sBgicEGx+CgpNuYvweiVEMBgbrIt/OOYeSce83TcNi0bG2tkmWaiBOzYSCIpWgNIOiZGV7Li9rVm3HzjilrmJYz/ZwyI1nNrghS/jUp+9jtlghhGR3/5hv+ouMU899Lp0oSHqAHukuQ3B4OoSpCPRcu3zE3XffTQjuS1ZBFxDSxz16EswR2dHyBG8gSbWM97XWrJZLxrlmY7TFrbec4WA653jZxJdiEDxeulkXnY9ShIhx0TIa563DcpK5AxE6L2TMTX1i4hj/hp4o+14bl0yGkqNpw2C9YHtrRFGk1F31V3J1RIjy1SACXbdiuVoxXy65srtP3Svy4Dm1tUmRxIQ5IQRKCVSwtKuO+XJFkQ8QUjIYFZhBxmkzIcdxeOmQDo3zMqbTSUGaxWIrOIESCuE7umXDbOXYny6pVz1+w6GkobGCZe1xqictD5F9z+LwkPWtHYwU+FQjSBCJimy3v4Ij+X9eX3oFx2Q9QtyXix6kF+DCE/uSx3+zEOWZ73FLVJ7zrP/w6wxvvw1sT/XFB7nnzW/m9He+ibd88+u48x+8ldEo5/ZXvpzrnvNsHvuhH2Dt276d9de/Hnt4SH7rrcz++P/glte9HhB8zS3P4sE3nwdg8nVfz3U//D+SnDnDwR+8hwtvfzsowfmbzvMT/9NbGE4m1M7xK7/4S3TtvVRVw3f97b/DDTeeIzEJFy9f4a1vextn1nJe8PwX8Ibv/lvcd9993HTzzfzyL/4K9/3Fnfybn/sJ1tfXODrcO5lG9Tx2ccp8ecxssQACL3zh8/HO8sgX72d9c5MrD9/H87/pexgMh0gC49zg+paAZ7C2zcHVB5HBcbh7kWq1IBnvcO3aY5xaNhzNa6bLjo31HO9lfA4fL3nK82/j9//jr3J4tMvOaMy8mLO+McC3Drvs6cQSU2oMCX0LVesIEjbGmyz7jsR7HApVrLFoAdFh2h7he2zfYb3DWodCYXuPDDGdMTGatm7ofARhd1XF7OCQ1WBIUZZIPH1bU/cdo+31eHC2AaXis6h3EaEgVcQbZKlhMCgZDnNGZYbJDS1TniEAACAASURBVJlRCCWo2o7WgcSgpYze5CJjOEwQWjAuB1Rti+8jxqaRhl4IEqFZWxtjkoS2sSybKKc0RjDMM4ZJSqph5VpWjePK1SPqro2YgfGAdrWgagQ6P7GO9DWjYc7qeBEnbi5iRVbzmit7FY3rOb1VYgpDU6+o65xExpTlrncgBUo4hI2e6vEgB68ZjUd0Xcfm+gitFAJHtajRRuGDpXOwajqW05qj1QqtHOubJUJKqqrl8l4sIAen1hAiBaXireZlxFyE2EglQNV0VE2HOGH1tbanqx2Xrh7R4ynzBABlDJ2zNG1HYQwySMaDkkRprLf0PiCVPDmYBzKV4JFgY+CS7Tuct6wqi/c189rR9z1KSTZUQdvF94zIDftHU4RQ5GmBEpK2q2JIEETOpghMhkNs7xDS0fuI71J0jIYFWaZo2pa+6QDIs4I9t+RgXpGmFusEgzTD+wCNxTYO23WkJme1aOIQIpUEETDjMUYapMxIE0lmAlrmVK2nCw3YgLU+qsjwaKGoe8uibtmbLZi5FG1bDA4vJU5pUi1YS2UMSBLgvUMKy3hQoqSI5xwExSDF2gbvYlJ/iMYojBR03tN1HUJBmoD0iq4LNI2FSOYihOiTrNsKIRVJIiMGK5EIJN7G0D/v4tlXnHgdlRKYJKa/miRB+IBvYxZIMSzQSpEkijLVKKL1Qkh5UjhKBlmKkoq6r2OKtDOslML2Pd6L6JmU8QzuXQxQfNzCEJutAa0kbduf5IPYyFGUFoGiLAyJMUgVk2eVit53L6JXUilJ03TRehJCbM6HOHH9Upv4K+uv2/qyi8Vzp0+xquYgwaSRrei6DuFqvA0EGy/7lSvLE7/SIUpLRsOCYl4xGmaIE4/AI3sHBCvY3t6K0wvfo7VkMh6SZRlN18YCQwi21oZxnuUsUimMjtOZdTPCeo/RKUUSKJNAoEBsRC9g8AGdgFIp0k9QRhFULNYEEmQgKzS2l9ie2B0Mlvkq0HUtSIVSCu882ztjvPNPcGoCkspaNjciTLVvV/jgMakBBQIVvYxKorXAmIS+96xWFQTHxvo46sA7G5liztK3S4zUJCZDFQnOugjxNTq+R/oWqSQba2uE4MmKL8FPH+/+AITeIoLC946j4wWjYcn58zfQ9z2ddUglmB/PY2pWYhCUzJcdT7/tDKky1F2PE4I8zTCh54uXasoctIL5oqFtO4L33Prc65itKi5cOuRT91yKIF/RokOG6DSLxRQhBWWWsex7VrZHSEU/86hpjV21KBm9MlILnPcMiiL6X4Xn0ctTNJDkCt+Hk+8pmIxHseCK1jmCF+RlykPpEb7rUNKgk4Qs0cymM6RRrG8U5JlmcWxJlGG8Fg9zR8fRt5lkjjIPUZoXYFgaFqsW2cvo0cMzm885ni+ZLSuOlhW9c3RXpkipOb8+4vSZDWzXslhWTBcdy6ojLwPbmWSYabxIOTycA4LZYoExGq0UWb4iTxMmoxQtFW3b8tCjl6jqjlERuU833Hw90/mSo90jVJJTpCVKamarOewKnnruerbP7WCt47OHMw5+7QKX/jRjeeMlbrz+POfPrzEYbJBVl5H+Knr9HPNukz9+7/v5zF33xkhv+XiMdTwcxio6brAQogRnkOfkSUqSKJIUkiTDu5rpwSHnbzjL977hJTzn9rP80m//GctFzUlAKcH3ONfGaVcnsELSuhjIk2UxEa2O5AMC6uQei9PEH3U/CcC/M/8caHnqU87z9c+7hfNbCj9LGRrN0244wwue8zTe96FPxWIzxMCWGNcbO8fLasWdn71EIQe85PnP4PDqPtZklIOSsjQoEeXSN58+x6qa8djeNbo2IIPloDpisXSMx2M2Tq9jdGC53jwBPt7ZPE2Pw9czUjMkEdG3WfUtXjrWNnZiNewt3zD/KNYG7KKOqAhpGANZarhxa4BiyVrq0TplspOyNz+MRf6JJCje5P91MNB/Y4WTsKDHy8YQkwwrbzmYVwTvaZ3Hhi9l4/1l++jWN76Ce9c3ecNLv5Ei0ezsbFGdP8cbT5/mxUXGdadTxsMhRztbPLS9zW9902t41jOeyhtf9CL+5Vt/jId/+3d48PJV/s7mJoOi5Od/7hcIQvBzZUn3rDv44R/8EbSR/Iff/nXeee/n+cBHPsp73vbPyH/tV9n97d/mF17+Uv6XX/gV3nf/e2nsIf/2J/8th1evkqeGv/v3/j4//P3fxzve8TMMNy9z/bkbeNs/fSt33X0XCsVP/+zP8l8+9nF+7Zd+me3rzvKud//v/NF738+7/vOfkxoXO+ZKcP4pPY9dusJ7Png3RTFgMnqA57zyu3j3H32M8TBnNm+wIe7/G1/0MKE8hyjuZ7J5PZONU5hkzP7BAW3T8sw7XsTTnv48lseHvPcPfpeLjz5Kbz3f+JofIvMjXvfKVyEI/MG7fpe73/1nKCPRSnK4EHQaJmnPRmEY5wlHiyVdrcH3BOGZN569acd81bI2MHz185/EDddvc9OpbbZ2FIeXr5AlaQRsK9BKMsgzbJYxa5bUvuLszhpn1obUnWe1WIG15EbTWMusDfG9Kj1GSM5ujdFK09WRXbw+GTIcTlg/OmK5WuL6HmTCqnIcHB6RZAYXOqyN/kajNNWiZX/V0gmBCYGubRkPJ/S9Y281p7YdQ6EJ3lKYjKoTXD4+5qabz3Bue53UO5azBZ946BKXDw75mmc/k+vOnMZ2DXZlObI9F64u6X3P/tGjeOc4uz3k1OYApwfM9ueIozk7kxHWOm45PUJpRV4UCAVXOs8XH9nnmbef4+DwkP39x+iliTkHeQEETq2XrI8HlHk8B/ggWNU108Wcuz+/Rz5K6Kynalb07SG51qyvTQjApcsR5zEeDXnus56ESRQ37myjteKRk7CgW86fYlYtmU2XcUIV4MpijnOeU1vrrK+PYrPLSjZOrbFarjiaLrj/0avo1LA2GJDqgmVt8WLJTTfsUDWRXbypS0QQHM9XHM0rvAjkqUab+J5b39qMjUDXoYXnrNBUTSCRMcH1eNbR28Aqn0cV0WKBNLA+LFkte2xv0VpGBMrxMcV4g7qpKHNNohSuh95b9u6qmYwziswQrKPrax755ILeRgRF1bbM2hWFKTl//QglAjIE1iYjRqMhZ3fGaCGRIoAMTLIBWntkPojNkrZjXgt62yN1TqYFpVagJdiOUSm45cZtkAofAkdHxyhpsFbQNJZ61TEoU4yxBBH9gsEF8AqRtDGYxwb8CRc0NSky07jU0bR1xHlpOLW2hg8hevyVQKrAdafixFgqSaI1WoBzkmrVAp7hKMVoSZYk9M4jdErTWnYPDhHCkuc5iU7pO8tieTJACZ7hIKfMNdZa6qpHGUHvepZdiw8SnMD3PaDwQrLqHMpZLAoXLNa7qNZKExAB7x19iAMO+oAMCuhpXY8NHvoY0iO8haAYZhlKyZNCT4OPZ0IvoMfjhUSrFNcLmtqC6GNSrXMc7s9jQewFUoP6SsDNX9v1ZV+a4+Wcpm8JgOqjobXvO8zJ1XY+5uqFkxtLKYWQkoNZHTehiHBjJUWUvAHl7hJtdITdKkjNFK01UsaOWKI1p5cxrt/altQYxqMRxiS4VQSBjkcpPggsgYPDY8YTzaCYkGeG0QAElkE+xqQKdIyJlpg4BVPRcC2CRJ2AWIMV1LZjXteEAFoJsiTCohOVRrlM5xgUDryMsHKj43cyAnwdJXtKkShF37ZYRJTkqhiSMzsp1px1COEZDUbkec7h0SFIhzthOCW5iumcQiC0AiVPJnY9m8NtlqsFy9k8Jl4l0R+4qiuOF1WUIgno+o6Ll3fRJqEsMwgWow1tZ1lWFYulpe0kR7Oe7bUNmqblYLqga3vO7oxAGlarHoLH+Y7lquLmc+fIS0m3gq2y5Gu/6laCNyBqisEYZQo+85k7mS1qGhcj060DKRV11YIQJ9HKnsCKrbVBTEdbrRBKxgjr3iITiWxiUehxhGDZP64oy4zhOKakNYtV9AWNSrSArq+RWrE2yjg4WtJ7SfCG7TVNohOGJibG9sHT9D1d51AWVnWPCILNjSEb2xOS6YK+t1RNTPmcLVZUdU/Tesq0ZDzQJIkHlVAagQyO4XhEXmYk+THjPmU+bzjY6xiXmtPXF/Te41tJ5x1ZAeNCY7TmaNnRH9WsFYbJMOfMmU20SmjaBUprEmXQ+ZBiR9LaHiF6VqsVCMGDV44xJqF65gfJUskrX3ALQnnee+8pPvGpe/jYh/+MF99xPU+++SbOFA3llmBkod4/5IGHLlPX9kQ2Gj+IkwjTv/QRQqCkQJ14drWWPD7sSrOMrq9pqilPunmTyfOfwsfvfIh7v3CN+mgZMRxOEAR4JEZotAIjepwPJ95IiThBdgTvnpiKCQTXhRsJBHoCMsCp7Q3WT21QS0cxWSNLh6SbiifdepoPfVhQdTGRVZ4kGyM0IhhC6Nk7mLN7VPHsp2xwsHfMqExoXUvVCu64lCAFXLl0ldZ2VL2h85L1suT0zoDVwxdYdXOaqiQpU+qqIR+WjIYDXNdw6eI19hYrimLGqdGITEfgdegClV/hG4ezlvUQp8vDzSEoxfD6c3Rr78N1DcPhgNDFabXQCq80QmeARMZ80yc8wf9dDdnHEShCxIYAARcCy65HhSgPfjygVgrxl6aRgfldd/OUW5/EO/7nf8En/vzP+ehH/vQkETnGtjd2QTtdsHt4mWV1C/c+cB+3P/t2Hnn0QQbDjNtuvZFr8z2cszRNz+H+HCc9nbW8692/z3S+hzGaD37wT3jpS7+eRx9+gFtvvZXP/NZvIXzgsccu89CDD3Jq8jwuHf1nvvW1r+Vlr3gFxmiKouTiYxcZlTl5orhw4QKf+cxdJKmkKDTPfd5zeMdP/RTzRcXqkUf55Cc/Qe96Zospk9GIYZ6ipGC57GjqjmsHDUK0lEVN8HDPF66SaMX+UYVKIo/up//VO/iev/X9vPqNP8qVy4/xwH2fZ/dwzoMPTzn83d9ifzpFScELXvR1vOkH/hH/+p+9hTM7o4h4uX6bj//Rv2c02eH7/94/5N3/27+jqo7REi5dmFGjo6JAeTYGiu3rd7jr3n3m00PWRgVbRcpoFJOOreu5emmX5XTO7sXdeJAODq0FG9sTsiwjYn0c49GAszvbeGuRQtA2HbKP8fyZGaKU4rip2DuYYyScmgxJVGQVzxZLkjzDhS5K6pOCwagkyNg0LZMMijjREFoyXy1oak2wPUIrkqFkkEbPVLU7Y21txNZWiXMBu9tT1ZLrtiZUfcOwKEnVgM35GtcOjzjWmlTBbNHQ1lDoEcWoYDRcQxOY7h2zbCxJmiB6xyDPYjhQ3fHw5X2ectMWg1SjjSUxASNTtrfW6VvL8WKFD7Belthlw2x2TNd2KKHQueK6zTW2Nje5tLfHvRcuMchLttaH1FXDeDyO5xeVc/stZxAGru4fkShBujkkTwyT4ZDpbIlQIwbDku2tDQ6uXEIqQ14kGB0VLMEKZBklk0lhyNOM48OaadOxWlTkKmE9L7hwbZ8yTzl/ZoRaz1ltjqmansPlilm1ou8CXduTJJAPM1bzFVpopKiwriMQ0zBr62n6wEgk1FXFPQ9ewbme1GjSJCHNUgSWSZkxGRRkeYa3Hm0CW+Oc2WqMMob1YUnfrjg8bqg7S901TMaKyXCL2WxB3/cYYxiPB+R5wqMXLjNMDYnWdL5nvJFTqhFpEi1Cte2ZLVb0HRSZZjLMKDJN1zmEVigZEM6jlEYnCaM0KrdcENQSWukQbUOmU7JhilFgu566axgVGTq00DuMlhgtGG5OUKmhahzLVUVTSiajMcF3WNeRmixmHsx6QhJorKTyEZ11dLQEIcnzHAjRmy8cXdsxr2r6vkfrQJ5Fy08PeGsjR1l6fOgJQaJVjhCS/ePIV83TnN5ZpJZ01jGvKhIVJ/3BNnRtzAQJCIpBRmctTS/jPd16unmL1tEm4wjEtAFF6x1eWLwLBE9U7IhocUIHrI/dXAd0Tcz71UbhvEDrhFIlsXHrAt5ZeqtRKgXpkcKR6oSu8yy7NlqjdBLNSh680nR9RVetkEZRIVgsKlarJWmWUA4yJqPhSX/y8pdXlHxl/X+6vuxisV2uyIsCpWOmonUuet8eh8IEgfUe+oBROnbiXARkRylETObyzkdtOIJlu+LxyGIIUW8vowY8AkwDd4YOJVXUn8MTUqzHD0/GRJ+YlNB7SSIs8w7yNKXMNKu+43lPvpEQLAezHus9SkSQrFKetdGELDeMhoo8T8kLgxGCIhsipcQGz/GiwxjDIDdICVkeWF/fZJIkSCVYdTVta0GkqKJASk+SSZRMeOSRXYoiYW0jQo2rZcNqUZ/o6T04h+tbFn1LkinQhq634GMhO5stWHWWPkgSbSikwfueBxYXIjNKWZJEM0AigiQVhuvWNzAmYS9NQLhoCu8jVzKgQCZo41Eqo8gcSaIphwlV03P69DpPetJZ5oslq0XHmjEYzYkPVWHbFvqOZlExLDTrow1Go5LMSFbd4sRoLbh5/RmM1jZ4+MIVFqsWR4pOU6pmyWy25IELu9EXkuccLpYcHC2wbfRLtp3FWcHmmsFkOW3XAzGFy7mKagm2axHSxwekaPnCpRVrRUoAOuu5ui8wIiBMwn0PX+TzdYPQCV4BQZEnhs31kjQRONczmazhQuCRy7uEu6NHAAuZyTBKkRQKpeMkNElTpBd0846sDOzVc3aPZwySIak2mAQyFLUX+FJj1hKWyxntyvHw7jWMUBSm5KqSlNkhwyIDJZjVOSBpQkPXrFgGgcSzXQRGqSGYlM1kC6kVe7MDDq/OKceK7Z0Bk9uXdMuKxKdcfdEOL/iWr+a5Rxsc7V/k03/wRzzwJx/hfdM9zpQpz33ZV3OoCz53/xeQdYRsf0nxeTKR8rFS1CrKfNNEMhwa8kKS6AQtIqg8N5qzZyasjxXazrnlzA4/+oOv4f0fv49f/50PMTtakElBEyKPydGjgiAISe8VVd2jhKMs0rjHWsuy9SAkXkYEhhLgQ0c52mKjkDz2uY/y0atXkcZwbnyK7Z0JT75+nRd81W188GP3/yXfosR7SfAWpR0f/fMPczC9you+7gfITq8xO16SK03oLN/wGc1x1XLP/FGu29nmBc96MmmZ0JuS1WLO2WWgtx3zespsXkMnmM3mfOGBPR66OMOvWsaqxyrPdS99NqdOjdmaTAhS8PDxCuumKAfWpTx04SLeKEZacfXjd3Pbd10mTTNW08C5MznHR8e0ixXl1oSDWXMSCgFfquD/360giEU7gJQcLi2FMXRIrIgSVR++1B14rR7AxT0GT38h3/yql/CqV76Mn/jxt/Dt/8NryZUk1ZqxHGESyeZ4TKoV41zSrlZMDw65694vMlof8qpXfhN3PPM2Pje/jemLnsrZu74PiSDRKXlZ4FxPU/dUiWV+1ECAxaqBENg7XkKAB+8xLFYv5m+8/vX8g7/7/Vzd2+PlL3s13/qGb+PM1piN0QDb99xw7jps19M0ESUyGg/ZObVJYhISpTmztcZrXv58rl07YrbsqNuOC489wvrGOsNSo03CcDAkEDieH1OvlqRpBk6xqgQfeP9Hef//+RFuOncDIQR++Z2/ysc++Tnueegy7ecvcjg95NTWOlevTXndt38nFw8XfPCTn+P7/vGKn/hXv4i3lqfdcIUbnvMgD+1ZPvOp+9gsBjz17AZ+WVEJQWUE88NjjmcrdrY2eeoNtzIQ0fNdjMd0iUM0nnYa+aaVj7K+u+99lPHaCF1kSBUlo72XTOfHPHa1omobsqzAK8NDD18keMG1a8esVjVPuekcN2wPcFJwQEuSedKhYjDIWS+HaCGxbY+dLaiaJSTRL9+3S3rv2D2csrE25pbtNfCBux+8wt0PXWZUppy9foOdzZTs7IR0uIYrcoyGZ77gdoospZ7VXLt2gO1rhpnmyXoNkdyGdT3L6QzpA1/9tCfTW8d7P/pJqqpCKsmN57cZTxLe8NwXkigotkus0jz8yD6HF3d58pN3mC8WTI8XXHn4EC0V9x5dom4tiegZFob8zBanz03YvzojyQdsXLeFdC0JGdd29yik4aueciu98Hz67i9Q1Z5zp3s2xiMmwwnpeqBtBUoVtE3D8WJG33ec3R5x89kNVssFSMhHgSefuYXWCqbHM64dLjg86tnZTDier1ites6e3WRQZuzuz2gXHaJVXLg45cr+jDObE7RIWFaW4SjnpqecxSjD4d4BfdMghOTawYJH9g+4dHGfnc1Nru1P6W1ge2fCYKDoqobTWUkIgYevXGIwGPMPf/DVHM8WLA5mYB1X92Z8/sI19qdzzpyacMO5TbrVktW8YjAaoqRhb/+Q+XLO1vYmg2FDmhmKwjA7XrE+2gBaetfRdpauC2iRcGbNROuO0Shzir2jGbtHNW4Rf5/MJJxbz9CZ4fjY09UV9aKj7aJdpRcCHxySnlQJwuY2ddtifcW4HHJmfIqL9SHSKEoZEELhg0G5njw1BCR7RyuUEBSZYbXw1LZFG0eaSYpRQu/mpHJM7XsevLCHtZ4ySyhshtSBJHER82BzjqdLjpcNjW3oBQQ0VW1xTWz8nz41ZH1ckgiB0oblssadePzUCasUFK11TGcV1nuW80O8d0zGJUYbhJdYKWirjrwcMBiXCJ3Qth1JakAEVnXMdjic1ifyUHfirQyRy+o8xgjKLGFzMkYkhrpbAYqusRxP45BhY2MLBGRFfOXXnaWzFb6yFEl+IoW2HEyPsUGRmIZCQWoU5VaKE57F/oquVwgB9z94GZ0IRqMBaeIZj1OMKbC9ZWNzyHiS0rWWrnXs7U6/IkP9a7y+7GKxKPM4HfCe3oWo4z6RQAoZI4AlIsaSAx5BEFFSJkQsLpU0BOKmfJx7w0knhBA5blKB0RopYuckkxIhJYhYcEatfNSiCxGDMoQQSKL3L1ewIQRGKoyAOhh0qAEYZOCCRAqHVh6tBSL09K1jHgJV1aCPFeEE9CplNDInRiMhhvfI6AFS6hhjIjIjEbF7p5UiK3L63tL2AW1SjhctywDHdfS1eevp2g4pJWliTs5nAWMUdd2RppJMaXzMoGSyvkHZdrE4lxIpNJDhgkA7AVKj1eOBIN3JLw/OKoJzlIOMMCTq5iEWosEgxYmXUEiyPKEsU/bCgmbZkCUpeWawtiPT+sRN51BCoFKFTDXGDTCZiTLZXhEEpFmBt5GpkygHtuXcqTV65+lsDBAJYYw9PeL67UmUGqqIqFjMG1ZNh5AS5xR1HUgLT+/g8KCmbhqs7ZjNYzpn1TqCd6gmICTMly2uc2gpCCEGK+V5jOXBQxDqCSi9D4HG9UyXS5RSOBto+hVSQW97nPWEKqCFplUn17VPSBKFDzW2dyihEdYxIqWTkq61tPUKncR0y2hYd7Q+cPlgTqEDRhiqZc1oOGQwLFBSEESPMAlKiBieoMDVMSlXJhohBQezlsMAs7YnMyu01ngcLgQSIZGt57A5wjUV62e3+K3rp4j0T3hZeClpYnn6V92G3y1YP85YH05oKbh0aYZtGhLxuODzv/3QliJ+8tyQ5ynaxGaOQlAkikQLRIhmewIs5gu21ta542nn+Pgtp3j4IcHsuMI4hxMxMqcHTIidXojyoLb3SB9wIU4WRYjX6nHGIwS2t3aY7JxhMF5nc74gHeaM1idk60NO545nPPMcH/rYfRGx4UP0C+PiAwqw1jJfLNi9dMBOaWg6cBpWwSJlYDjOyIuELDc8cvmQ2lnqvieRjrWsIEkMMpsgwpDjWYsYSrJ1x3g0wlUdG4Wi8nECvqyWpDrFecf06i6h78iN4QPHL2TJbfxf7L1Z0K17Xt/1+Y/PtNZ6p7332dOZ+nQ33XRDMwaMKWMRA9GUASlilZJATBV4ETGlhigYLGNSFqbKsqxELZMoxKQTDSVBZOpiiF1ACJAOQw/0fPYZ9vCOa36G/+jFf/WhW6uU4ooLnot9s9f71l57rWf4Dd/P52vkz6GB08WsXAeEYAo9Qne0jaWyGucjl09X5PS5DOkhF/15M8D/30Mc/sj5kHsUDIWy/rl+wOFl4gtu3A8fPOD6esWP/uiP8U9/8YP8+od/g9OTEx6/+Rpve+UVlC5C9a/9F/9l+v2uQIyUKnTAcWB56flU+DTved+Xoqzi+OyUb/6mP8LZccu3/6k/iZvOEVrzLd/yTfzw//b3+Zr3Pc+TN1/jHd/xZ3n2997Pl773i3nl7W/n+773nJfe8XY2X7nl9TfPkUrzr33TnyCkyJOLK27d3RBC5Nn5JToLtIAP/eo/4xv+2Dfwd//OD3Lv3h2+/Ku+ik99/CPsdgPLzY5+DCTg1UePqNuGr/jqr+ZjH/0Y3/xv/kl+9qd/pjx0CYGqDDKXTM/Zndtc39zQD3v++Df+Cbx3/PKv/hJtU/Pg+fu4MCKV5D1f8mXEmHj27ByVNB/4qQ/wh/7gv8CP/ej/ye7WggcPXuRjH/44m6styThIE8O+Z9tPCFkyvP0wsNwOnF90VOrwsWuJVJnoAlNfztm60ehKkVXFbh/5xKfOqbQq31MhSSTW+z37wROzxgPb1Yq2qQkhME4Tn3mjEC5DTEwxo6xE2rIZc9zMsFJSaYXSgmHY03Q1TWVJLjLFyJPthtv7wHbvET5webkhB4WiwuQKnRU5Jc6fPGWXJG1VMc5bjJFMU2B/UHBciyIRP33uNtpaTGOKAJmMrC3vevfL9LsNKZTzrWlqni0vIYG7vCQIyWbj2CzX8CnFfKFp25baFqfj/QeWzcaDm7AGhslzPF9w57kK7xLRTcznNboWtL5mGDxvPL0iyszD+7dZXu+pKsV+HFlvn2FrifeZ3RCZJocLE7pSbHcDox6ZpkjvHPuLFSdNh4+CR29ccXG9ZogeZaHTiuAyl1cr1tuKmAR3jrqDe1kjZGEUbPZ7ZNWyc5GL9esYY+l0QqQCG9kOe2qpsY3GaOi04WYcubhZAUbzygAAIABJREFUo+SMOEUGHTFaczQvTsLf/MgjhmGCVHzJ68GRtUAKzbObPdudY7ddY5Sm20ZqaxlHz2YMPP70BbvtnllrefjwtGgZhiuUgZwS3kWkkLRdxWzeUBSSCqU0ZydH+LjCmNOy6pkFi5mlaizzJjCMhnGc2O9K9GQaHZN39NOIlAqpd5AD/dTjsiLKCnSGnFjtPEJJjFJUVrNcTvgU2e/Hkg8ksZ9GXEzkEcQe0sqRQmZeO1CJYefIOdNaiwugYgHGaKkQ0mOq0rQXY0KFiFSGWlt6HQpILWauVz3RBeq2UPlTBCElwmcEGecHQkilge9TcQ4qfdgfkUhdtvYSmZADBBjdwOgcLjpEhpBiiUZFiZAVIU9s9o6MQNsCJjyZz6mVQKZMyJn1ekQbgxCCtmsQCISICCEYhpJDNFpgrEYaS4gJHxy2shwfzRDC4NyERqGlZrPpSRnmixP6IbHb7dgNDhVgygmrSvNPq/4AiFTUVbERqEaTXZla/v7xe/P4XReLVVPjoifGhJZg68Mudy4ofIQ6TAVLODblUgxKKSnKDIE1tuTWnCOnUHJbRqF1gdhMbiqNF1EIVClmjJTkmNFaHvJIJXOojEGJQ3JYQpZlWlFueEXRYZSAlFhtR7Q1CBVRGVIqxZUkk/NI9hQ6lciEaUIoWfIXSlJbQ6VAkEmhEFqVVIz9QK89KR3oU1qhpKDaOZzzbIeRJAVRCy7XgpwEVhsqq8k5FoF5TCAF865lNutYbdfFvagLWMBUmq5NuNFxc7PD+UCBSEu6WYV3ns7WzLqGozlEl9j3E+O0RBm42RS89c2qjP5ns1m5wARPVStSykxjpGkaKl1w7GF0WKtYHNcomdmORcSbMzRVAdZEJLO2ISYYB48iI2Rm1lRveXS0jcQUqOuKuVEFAJNh1rVF3xBLkDzlQEyRcYooY6hsBaI4hYwt6x/7bSamRCLy5uNLnC/WASSs1j2r9Z4YLVOMbLc79vue1W6Lz5YwZHbjQMyRSksqZQpMJSfW61KQ5yxAXKO0oKktZMWwG5nNmgLLESC1RmpNip6maZBCEv1E29akYEh5IquIQJGyOnRDKSsZEYKBulIco6hFYtsMRQwcMjfrPVop5lWD1gO70bPbrmhniyIP3+/IKXE0m0GVCAm2e5g1Fl0ZVjuH+cgDKpE5/hKNsAM59+j8hFmr+OKvfR75uOddi1NM+25++ucv+OjHP0sK4wHS83kn+v8DomIU1FZw/94tTk7maKUwStEYwbw2zLuK7CG4jFQa5wPtDL7ynXf509/0tfzSh17lg7/4SZ48u0DkTELgE4wZjCowokjGx0MTqnSSSpGXShFbPmrB2195G8+/9CK373ZYP1EvOsaqZjk6KlXzr379V/J33/9zDGNidOmwVntYfEilibBabfj4J97kHX/0fTz75OsErRDSsDyFs4Xi5dzhp8Cb55fskmSuBEjP+fWShEZVLUJLklLMz044VvDiUYXIie7kiKAM+7FkOC7Xa9x2y9npEaZpaOqOX/+l9yCs5M/8gWuqBFYp3MkvEKPn9O3PoW3LzeWW7XpP8C0f+cgzfEiH6yKAROb0Oy4XP8fFEeS33Ix9gJQjPoq3XlRq0fTW8PK97/livv+/LPoNaxQ/8Lf+e5ZXz/DDwG/+xof4n97/fi7On/L48eucnJwhZUUiE1Pk8vKS7BUhrbm6ucadOsbJ8aXv+1pu37rD9voNvuc//kvYZsajj/86tXvM137Nu/jMhz7AH/rWf5uH/96f46+fzPngB97PN37DFyNRGCZ+/Kd+guXNNZ/85Cd48eVXEEZTtw2V1bz84inRB8Y+8N/99f+W//Av/if8j3/7B3jj0SN+9Zf+KdtdzxtvPmEKCUWkMRala/7Kf/5X+I+++7up64rzZ8/4q3/5L1MpC0rw/n/wD/nO7/h3ubq45Ju/5Y/zbX/m2wgx8OTxY/6z7/0ejpo5TV3xn37f9zE/WpBTZr/f8ef//e/i+mbNSbvg7//g3+EvfM9f5Nv+1LdChv/5b/4tLl99FUJgvduy3GxQlcGPPSlE6qZi1lUgBc+uN4cIRy5tj9EjRSbpiiwVZp2IKZUCIAWulttS6EZIvix5GKMOMLcJqxSN0IgDsKa2in7c82SZGF0siiIFzkHKiX67J8XMrOuYzWuC88w6jTWKaYJARtSR5rVLTNQlgiIBI5HPVnzktUtsZZAhMgwjgULD1OSyyuwGfFBMU0JKWMwM9x8ek8h0tsFoTRARHz0haWZGc9LW3L11SiUrlptzehdYbid8gCA0Rma2mzWz9oiua3nw0hnOe27fOeLZsy2PrvZ4JGIzsVlf8fydY6LyhOhpbcd2v8PqmiQyy2FkcomXXyoqlrOzOTEkht2Ic4L5vKauJ3yQmOaEpu0Yhp7tmEgBtruBi+sr9kenbHvH44sbUoy8+PCU45OK1cXIndsnhBxIyfPKS8+xvrwkK8Wd28fMO8PT655r7blz1pGzwHuD1Jrgeq6XS1JONE3LbD5HxIlWgV/U9DFytd6iySgpuFrtcS5yudmz3/fcv3VCytCP5bqLkCgEUiWenK8IQbDebKjbCvV4TV1XzFpNjJ5HT3bsd44UIx/+1LNSmCwWLBYNXVWUDrNZg7SJsd8xOcnoElMcOV7MIQamqMih8NUvUkJuJ8bUc3m9p997Xn7xOSoE731wXNZppURKw2q1ZxoiGYMfE+e5Z3dzRVM3XK17fAzcOem4fdRxeb3H5YwLjjoqgiwebmNqdnvH1HtELptwV9MGZXQht+eM6wPO7nBjoLaWtqvQNrM4biBrKmOIoZBOhZCsbF/WUhP0+4ndfqCZNMbWpAjDsCs2AG05OT6iqjMh7NAannt4F4FgtylrqdoIoi+fW0oe7wNGSaQ1B05FplIWYQVdA5AZvWQxs0ipyyRXS9qmpe8HLpc7tNWEIMrv15LKFDd6W2mkKvRkdbi37/c9TduUm4IqjeFFWzEMjpWfuNmMpCTwfiSmjKnL3w2Dw2iFVCClJiS4XjpC6AtGQAgUCmM1tTUYXZ7rf//4vXn8rotF5xNZlgJQ6NK5iCKTfVFRxAQKTUoBJUtXk1zQu0KUzlI6INrJkA+rVVIotFAkkdFK40PAx4B3gRBjuagkULIQK3PKSCHxlShTy0zJO6lM6k2ZwClobUVXK0IYqKs5JiiMcZAlOWqS4RBILl0foyV1rcixAGm0TFirmHcdUpTsYlmtzRhlSdEgdf7tUG+iZLNcxIjMogFHJAiFO1yMjQIjDiSolJjGESEEyVjC6JAiMXjH5BxGa+pcMbkN/eA5v9mVPX48McFJaghjIjaikMpagfeC3ZBZ73qkCWz3kpvlnpvNHm003Swgkbipp55VZDJD7zHViM0BKRsaKzDB48l0lWQzBCaXEQhOWsXkRnYeTlxBQQ9j4OSkwRhJdgafMj6V4k4pxc12jVaKWddijQEVCd4hUzwUKQmfI5NzzGWRqwvh0WpiURdfkphZlFHUleXF2w3DkFF1hbKSy+WGy8sVKTT0LnB5s+RmveaNS8hixjQEkoy45JhrixKKGAt6OgRHzIVW5ryDcCChKUOYApNxRAGJRBpLtg4hmTyQM+PUo3Y9eVBI69FWIRDIKNBWEHyiMhV1ZZlcYoqeVrWs1z17P4ESiARD6Km05ahdFCJe9AQ/Uk+FgJZToDaZeW2wlWaYAsM4IZLAdpLeR77sQ1+HlYLmO38Z6IFM9gaRNTH3IB3y7Bj0Kev9U65Wa6A0e76gWhTirYKxnLfFgbpYdFSVPVCH5aHDqmlri1EKkUvOWBmDdzsaa3jXS/fYbj2vvr7kjacX5VzNgozAp3LdkKYQYBW8lW2UGlIsgmwoUy8QzOcdXaPRMoJRZGUIPrDb7KnripdevMfZacv5Zc/g/G9zYMSh2CLjnedyPeLVnCk6khC02vAP/ohHSc9f/WBFmCYaq6jbGce6Ig5rPv3mOb3P+FwIkGfPnZKMwuRE3qyxUrANCdXNMXVLTI7Nfk/o9zx89zvRdYVUhs/hgme3TqiExLiA3LyPyQ20bUc/CpbbkeubDXV9m+W23JjfqhUPSozf6fZOedtvjQ8pZ5wohXn+f70SEPw3sYef+D/4sg/8LJbE8Uxx/7mWqrZYbfiBv/k3aGeydMddPGyYGH7yx3+EH/qhv8d27dGi5fjomB/5hz/IT9uX0cbyyU8/pWt/nUqcs33yaYb9wHK3I0w9/VBzfnPJ5r/6sxgtefZffxd3Tx0vvXJEVzWE61/n5/7x/852N/DkcsVP/vCGFx/eY7e54If+h7/GV733ZTb9wJNnN+yHge/89n8HoSoaowkxgggYI6i7GSIFRAxMfuJXf/lX+Pqv+3qMkpyeLaiqispUxJj5lm/8FibnEELwgR/7MX7mp36Cp1dP6dqO0/kRVllIiu/+ru/iarei3N0kLhcqaYiJm+sr/ou/9L1YY3AxEUNCEkgilu93yNiZpbbl4bnrDCdHM2xl2YiRGDzGamxl2G9GaqNJWuNTZtjvSD6x2/TYSmFqhcwC7xMIgVGak6OGplFYC4uqpZ8mBl+UVUZKYoooa6nqhBEHKrWTCCF4OvSMIeCdY5oEKYCfAikm+glCSnQKBtez3GViEqiZJenIfjPifNnK0VlTaUNVK2QeMBKEzPT9lt2UGUew0jIMhigE+90Oqyy2rkFnxmnHboCzbsa9OwuOFx1RJTQanQONUSVqHQRNVWFNIAZP349ElYhklquRzW5kOSacMjQhs15donIgKchK0u0dV8sddePR1tC1DVkEbrY9MUdiltRW05xYpgGOT1sEAyEmqnpG07acPw0kHxingUygsYqurfEJ7tzqqKzi5bu36eaSYT3QzSuMqskpcXbS4XYrfBAEHxn3iTB52qamriW1sczqU7JQPH7qDg/+mqqpSVrCtmQY++CRWjCva0KICFM2Xfb9xLObFUM/8aXvfB6fMuP5Fj8GtLJoqcneoariKsxiVvRSoRTzBcZiuH3ScTSb0Q8Tq/UKFxJniyLY0loUFYUS+BDY3ezpJ8Fu8Oz9lt3gCFNi6EuERmtBlOWxdLndc36xZZwiZ3eP6ZTmeFEjVUNbFf3DoxDpRcamjFOaKQuGMaBNKm7rybPeKWpryKp0vlJMRa/mJUpUKGGQsmjeGg21tawnmEJk3hoUMGw9U+qZBhhcYkyReWfRUjHuJ1IWRe1WWmQoWeBwUklko5GiKttjlSWESN9HXHAoNLauUSIz7XdoLVnMWkIsQMOYIpWsMNqUldLya6lshTAQQyzaisqglSSFiRA8VqtSLCrN2I9IFKOL7MbAbpioQ8SYCsgEHyEEjFaQLFlkrJVorQkBxikijWfWdVir2G97Qgzs9gP7wXG16XEhoVIu1xo1IGW5T7dN2XSzbVcI4xmEkExxKmqY0SMHTa8ijQVbC37/+L15/K6Lxc2uL46gGPC+iDWt0eSUCkhCgfdTmSwdXFtljazk0JSQB9oiBWIjBJuhR8q+CFVFRpCQUiHIKErI1mpdTrZxLKuoRoHITGECkTFWl9GNSxirUDKhtSVnwTB6IJKSw0TPzH+OUKrI2RFiTe8UOUfWvUevi7Mw5TJNMUagxHUpas1hzSoLal2IkLWMRSeg7OGCFEEn6toy6xaQclmNOilIfigrmjEppKyo69NywseM95FjOaO+XWNsWbcFA4KS8/riByhdYZWkFgnvR5RWhJhxLrDbT9BFTuYVq+0Z6/XIsYy8894pTWsYJnfovCWWq0gMYLTlueeOcSHi3ZqL6zW9MCgtOb/ZF7diTkyxdACXXcc0RUwD5zcrNhtPRtAtDTlCnCL54JwyQqMlBdaTcvFpCFmoWwgaZcuENQXu37kF2TMOF2hdxLAhDChdQ47s1h4hEraO3Lp1ikChlUUpWabIVcvkt8znlufvP0Spl/DApGDa70nTRGUtLQ27yTMGjw+R86trNts9PsEwCKYp4oNHKVhtevZ9gQUpaWmsLWuvKjOGnpDB+UzeD1S1wG0zxhi0gjgOhewpBFEWN14lJEYrrvQNIXi0Kt+b0Q1oLakrS13dEFNppJwezfBugzGKW2cLQPPPP3vNbtoRQ6KlgFDuhQXXa8F7Jkc9azl++1eT+RlE6BEpgZhjhifks3di2xPGnea3PvQr/Nov/ZPiAU2HLPAXUG0OFwslOTmZcetswWJe0RhB09jyPo2lm1vqWnPndE5Ta9w4FUqeS6h6zgt3FrRf8w4ePH+LT7/+mN1mwPWx5EplwGWJ6z1KQK01RkiszCSVCSIzhnIdiZT18ZPjU7q6oh+3bKeB6mjGw/mCNKuQdcNRc4ev+8Nfxf/1i7/F7rUiqE6hrFsLKTAHKNdHPvopfvNXH3KyOCalTDtrEHqL85Gr5Yg0ku54jjE1n371NaQI9EiGnLlerwnBMa80y82aLCSrKbOdPDWPqdsWsmcYHZPLtLXi4YsbtvslYZrYr95LSonP/tIvkoxEqgryy1xf7nny+IM8/8IxCMPizi30kWITtmTi54FKD1PFzyvq/z+PDCDJIr21zpuzwpPKjZwycQXeKiZf4fCEIg7+t5xIMZCTwsU9603g8tqhpSiEa5EwRrK8GVltBobR0XYjw/oWnbYczcvD8C/8/I/yC7/yc3z5l7+XL33pOaQ5Zr6A4xTw2zWJcs0eheDJteZ08ZDw0vezJLF//Odpbh9TzQ33b89xLnG1KS7cl+6c0hrF6Bzrt90FMuEPfxX74LneOz716ILf+MgjUg6s1p5hGInekYAoBYvjI7QsEJD9uIMkkFZzctLhgiXEyDDtqZLiwXN3kFLggmfd91xer8kZuqMKnSQqCnTOGFtxfrUCWb7BMUSUUQgp6Lc90hp022DiBjVA1mW92K8jq6uJ8/UVKSSUrTGVQhsFvgDadoMnxMjMGKrKst1t6eYdXddgtKKuDc3c4AfH+XWPMOU7QBbsek9OmePZjNmsJUtB2vdopcgZdtsJay33nrvHrdtHOD+RU0AI0FoyryxNXSG0ZXmz4dnFmqZtWcwo92MlS9Sisgy9I7jSNN27DXKqMEry8tsecPfuKdGN7HtfYgouMg2eBFTGsl2NbLYDWGhMi82B61XPs6s1H/vME44WRygj8cExThNkMEIVFsJ+z8n8mMparlZLotJoAqdHM47PjskI+tGhreUf/8ZjxrHoGV56eIscwBqo6tKcFEnSDxW2bfiFf/YqxEBl4Lidk1+LRCDEzNwq7px0zBYzggtcXC/xKVFVDcJanjuz3D7W+ODZ7SYkLdWs4vxyya3jGfOu5umTG07u3KdpKn7tY4/4jU+8zku3bmNs4smzC04XHaezNVMIZCV4+0sv0k+RTz19gp8cD45P+cT5BZtxz61Zx7ufv0NMCZc8d971IifzBTlEtrs1p7dOWG3W7N42AYJpcPQuFxpoioctMc0UJk4WLTmUaXRlDdtVT91VVJVFVYqQI8/OVwQfCGEEJC7AOI1U3QntLHCHzHpfc3G9YeUitTWczOf4GLi4WdNUFTJI7p8dY7Vm3DkuNitefbwsBdA4EaaJ5+8suH//Nq8PI1ZLusbyri96GSUyx7N5gf+sd7zxdMnx7QVGwGaTmMbMNCj63RKhEu2sxhhFPw7sd0uUXbDerDk7qjhazLB1x2deu2YxX7Ddjbz25hXHp8dorRiGog0LoShPhMi8/PwDxmEkuJ6TkznP3VqwXG3xwWGUKJNWo0Eqnjx7So6FPJxz5rc++RjvHMO0petauvkRZIfzI34KCAlSuEOzNYGEiCMYjZYa5xWTyzjnydmz3a2oqwapJE0lqOwc7xK7oS9ajpjoZpbZccerT54ghGE6fPZIik96u8Xqcm272YxkpRlDZBpcKfhTLPeTpJjposvJZOq6RFV83+PSwbVuJG01K+7QTpVtQynYjZ79+PtrqL9Xj991sSjkYQqnNepQ7L012ROCkIunp9YCYUxRIojyIBKCp7KlU6KUoKpqQix0wJxz0VoAwQeMtex3xeMjJdhCAiYrShbpkD37nC/QT6FIgV1i3kZIE0J4coLRB3xyGFUKAF2ijwhB6Uapisqk4iEyJcxbV7koO05nVLas7BhZcn1NZ8q0RSi0EChdo0yF1AKZApri0JNSklPJWvbRoaNAow8PZQXlP4XIbrVFHvJU3k/0TqLlSNeqt/Kc1qrD2mqZrgiVqa1FJH1w6xSsdK0kUpbMkDku1LLNZkkWmW1funV+GnHOobInBogx4GU58ZdXa7Z9oNaGtjIoW5XIyLSntQKpJSI6jmaSB2dnuBjhgS8y7KjxzrMdJ+qqoq0ts1mDAGZtg1TQDwNDX9aMU0p4/zm3n6Cuy8rD0yeXOO+Rbc3xyTHr5UAgMx2ojslLlhvH5eUaFwOIzPVyS8ZwNOvoZg13jz1tbbBtx9b17HZbltdrBJJ3vHCvTLJVoGsNL7/3ZYzWpJQZXGQYHevtAAh+9hc/Tr/a09mGW7fPeOWFe4gU6AfHdu+KckBm2koQdM3F5YpPf/YxPkTms9I5jD4wDX0RvidRVCGVIoRAZQO2dgiR8Q7cGAjziDEKkmTfb7G2YfKJq5sdbW1xOVPLkgvZDCMBxS0H2+32MNXTyNZALGRRSU2iJvkl4sggxMv44LjcjFysexQSkX+bPvoFBYgAJSVt2zJfzCAHyAXaJKVGyIq6tlSVwZpccOaHFXQtS3EQ4kDTSF5+6Yz3fckLfOITz3jjzcvy6yNIkYqfM2eGEBEiAiCjKVPHlPhl8XMoBYhUBNu7Pdduyc3FEmkrrBRoBFpXbNYXvPy2O3zoNx8RCxS1iKIpa+BCSUQWuGFktXE8/7aO5Dyr9a4I5VNms5+4fTbn7LQDXfPu+mVmFiohSVKz3PZMY8+do2NcKB3m/VTWn+/ePcI0NW7vubhY8eTpktms4tYrDzjtbyOmRPN6e8gCt4wxI3URdata03YN6+s1s/kRVacQORBdT87x8AGVYk9mUTZ0fycF42G6+oUvy29NKg8xRn57DPsFLwM4eFDLT7kQ0Ei0bkrk4KAmqRtBSB2RNchrujoy9BOmKoofIRVto5n8hsePPsN0s8VnxdHdF/my97yLF19wbFYXfPRb/3VQkrmN1GZiJJBcYH95gc+JwUn0NFAWlRNBavpmDinSHbcc3TrCDxPDfsS6TN0Jbj/3Dt75zhfYLpe8/tnHjFEwDp7lcsuUAp015TwUmaEfOT9fM4ZMOpsx9RMhxKJc0A27ZV9olqL4NaNLuJAZ4xaRJCpLKitJ/VDcvUaQlSBHOKstEVDZYKhpVMveXbFoDPWsZT9MbJY9KSvIAdtoXIy4MSEmz61W4wZfcvK1IQvB1WbH2PckKdnvRpSUnJ4smM3nrKdrLi6usbbm7p27XF5f0TSWqtKMfmB9uSPFjFUaq1VxmaYMKnK1vD7QZmt88CxXO5CSJ8MGoyX3750hrMRlh4ya0/kxQsButWXyEWWKbF0mwfl6jTEdD84Kifa4mbFbB157fEWtFFWtmS9mHDULfu2jn0bXmrpRKC/ISaKSJuWJqmsRPrG+WZHzihQ9UhqSrPA+4Mcd0iim0bFaPymE1CkiVOZ4XuGnsprpU+C4qrlzOufe0YzLlFn2jvVq5PbJERfLc0QWtLZFqHIdny1OiNNISJ5d9uz7hDKS9XbA+UjdWN683HJ0NONsMefh3YcgEiF4XAYXI1YU9sHT1Zb1NGFEYFF31PW8ZFBr0CSyG3nn8yfcf26GyEUt1TWqbF8kAUozXyw4sobBe6pFA5NHRckYI2+bP6SpNHMlmDUd235PZQQKj6wtDQ21Ecybis+57gygiGSlOTs7YlF37PZ7rlZLNv1E8onL5Y4pRja7PeSEEIqjWcdsXrFZbmjrrmT2c2ZmDadziTFNcVcCZ+6EO3eO8H6iqxuIgk0/EaLGDSMPnz+lskUUf3HlUUISsmQcB9zgyRkudwNcblhUluVm4MnTNV/x7hZbSY6PGubzGmvgep2Zto6202WjKUqMsszvVKAL1C+GxKpPVEaijOfW2QLnA5t+5PgI2q4rtP0KdE6YLJA5Mzs9AiFZrrfs90OR1ceBqlIo2TE6wXQz0O8cwzhgjEZJg/ACYRJayVLI1RalNTfLniwVSlWkINiu9uQccMFR1ZaqqfHOM/Q9R/MFMSWm4Eg50FQV6NKTMqLYrKvmmKo2SEqkKUfBbhzLbrqUSCGxlSEnyawt7yVLTwXMO4uxGj8m+nHAeUfbaFKW9OsRkcAeIl5GKXIS+DQdeBqFB7Hve6aDwxFKRlEoDaqMjlIMBJcPQ6ffLxZ/rx6/62IxBI8+/HQBzoCPAVJESkHTVMxmHX0f8b6sGiqtqK2lMrIg8UMkuMTQT4QcaSqDUZrGKCAThcJUipk9YtgPjM4zBQNC0TbzUni6IkVt5zPms66I2glkAm07w40T2iiaumY+axHAk8dPyClz7+4tYgxst1ua2fyQleTgUCqrliknQhI8OV8TU0BLiTZlx7uyJSc5axqMMWRZOrJd06KkJoZIa23JS+pykmiV6GYVbVNyexLIEZqq5fjIoFQBfcSUmVcWVIHocMhwuuDLqokqMt4yEShd4hD9W1NcBMRUtCXOOXzwzNrZIbRcMeqJWV2jtObp+SUuJEJIgKK1FYPZ884XTslC4rwrk1wJWElIgjyBH3uGXWIcdlRNxbgL7DYjmz7Qzmtms4yfDGOvefT6nmFyzI+OqGtDdAP9buTk6BZHiw4/jmXVtqlROPZbz9lpQ4zgPIgUuHO3omtbzhbHaCkROTM/PmW1vOGTn3zK5XLLoql5+PA2xwg+c7XjsxcrZnXFg1uCqR84m8153/seUmmJmM8ZhOTZk6ekHJnZFlN39OslMGKrRDUGpmHkz337NwAJN/UEP5GDw2jJvJsx6zq00TRtTRKe11695vRsQUqeRGZ26wgv4GYr+bVP3PA682hqAAAgAElEQVT42TWvvvppVBbIAKTMNJV1481+f8iiJM4vNvSDI/uJCHS1xpjC/Q0hkWXFndMZs5lBFhUSn3jkeHp1Ra1kwdmbFiHLGloaHpE3n0I/+g3i/gje9cd49ZO/wuuvfoZpChiliCpi8mHKmwMCQRQaS+C4brl7csrxrKXSCqkV2lqaSlHrzKzt6FqL0AVEEUIie4+wGbJBTBpJokuB/+A7/yg//rMf43/5X3+e9WqLEiATOCQIRc4ZkVLJBmf/VjHzw/pvlx3VmHn96ad55zs63nP/Dtp7jmdzgh/Z7HvWrz/GZ3jXF38R9//5J9EfgegBKcmUh+9ERCDZjJFPLXv+ra95L6/+5iPcLtBUYK3i3W+/y/n5NR/91Q/T1BUuaWaNJoWArRUP7h9x+7RitxtYnNRFbDzsaI9VeVidPFnXWCN44XbLg/t3ufjsI1RIyJyJyYGE22cnjClz0Q+oxQ1f8vIt5vIr+K1f/02SEgTp+Se/8lFefXRJdbg2upxLCPYwAdRCkIQiCYWIh06vgtJZoRSAh1VvIcUBElsaVuJAGeQwRUyIz2PnlGvX/edaXjyds1ntSMkjtEBQ1s0qXXQnhT0m8E4wqw32ZE6jy3dh2BcYVD4u/+TGGBpd0S9X1LePsRh+5If/Ed//117jT3/7d/Cv/EsvMH/HCW6zpb++5OL8nPtv8wglMA00bqQ7OmHYFYn48ewWR7M5zVnFfls2LUQCoSK37t/lbgVdNeNqu2V7b+KkfYW4fwXTWMbB82sfep3XntzwdLPHjRPjMHL7hdt80b/xB1mvPe38Nu/4orvcPu1Yv3GN2+14/z/6IKNKJFkTXWatapqqYtIBGTMqS+azGav1mnhH04+O1x9fsd33rPee43lNZypi9mx3l5wez1lvB954fIWPiawFkcBMdAgl8W4ozUZZsXWJ2lqOcoljKG04vmVRzSn95Bl3EY0iJsPHP/5ZcsrUtqatLG2l6ZqK1a5nHCdSAKUtPk7cubtAq4ppCGx2PddvjEz9yNnZMabSaKuwUhGmouwJMfD4fMO9h/dZbybeeLxks/sM1ihOjmdEqUm+5Je0rkg+MJt3bJxktd/z2uOrAqfrarLVXF5tWH74DSprufvwDsZKhv2ARpOkZBKJummpG02KgTQpghest575TPHed93jqJux365Yra5Y3D4BVR5w97vif3z6+Jy60tx57oxuXlEpGHtPJTJVdczdWycM04BSnne99DyVUqy2AxerPbPOsltdI+oGoQwkjdLQ70cEgq41+BB582Liwx9/HWkkjalJOTEGR91o1uuRFDzzWc3d288xTgEXA/vtnuNZw2LekMg8ev0aRWY2b5G24miWqCvLdvDkJHBjWeFcjyO3ThbMakPXCBrTcP+BRQjDrk9cXEVefXTNdr9hPu+Yt6Wxd3brDtMUidNTqsZwclKk7mI8LP8ozXoQ7L3jpK25d/8ht1RRRKiUUCgUicFNjONEv92TQ6Kram42W954coMQkuN2Ri0ze4raaZg8b7xxwXxmeffbHtD3JdeXc+TFW5YYZ3zy9fMCWDGK06OGpq44PTrjcrliu9nR1jXzo46uEzx7uiJMpUH/0c8+QalC1m6M4PSoRamOcb/HC8k4OKrK0h0ZVqsrVpsRqSxdXVPVFaNXdNLgdebe2SnWGpKCLz89Zj9MbNegTxTrwSF0zWdffQpkqqpCCMV29Lz2xpqYItvtBEnw/Asn5BTo5jOEyMQho3JDzgFkKBNfF7EoulaxmM+x4pTgAiEnVNWwICGEJAa43g4EL1h0khwDySc88NTvIUeMhJOThtpaVst18SObiut9jx8iTVVz57k7OD/hnCNMIz56FvMapQ3+8ooYE77PbK7W3H1wpzS6J49PxUwweocLEWsVprLElA++5AK3lIiyXRQzQlhkLoRYxYFTImCYJuLB3axk2ar7/eP35vG7LhZTlGRZxuY5FIKSkGXkFw+7yzknRKKAZxSluJSxrF7mCKo8uAUfSVkRs4Yo2O39YcqUSSJgrMF2FaYxuDEgDysyQkiyackHUMZmPxxchRkpM/2wKRNPWdZMhnFE60JbRAi2vSflhEuaaTsUWIctRVgMxR0ZUySk0glMMeAzyCjYD0Uir4TkRvcoKQ/kVklTu3LBJTPvKqwpIBshwMjMfkp0bXmwkoe1XKs1TVOVVcrDGN81beH1iFK4KinJShSJt3AHvoUsO/KHQhd5oM/m/BYcSGqDROBjcVT6WEAIn3sevH16jAuhfC6x/P47t0/Q2iCUIkTDorFIVXQcpagEbSRujCy3O8iaplVoLWkWCVNZtBIYoTBScvtE40KgqtoCJzKGmW0Pnj5BPSvi44xHqgZbm8PYN2JSRmaJThliZrlcHsA8mWrtiX5A6cjRQtPQFUZLVXF6FKnrIrtWckJIGKaR7ZDJncWtHDfbgeW2BMtFzFi9ZRoGjo6LJ+h4bsjzGdOwwVYVbpyKC6ku72MIAek8lsy09yAyykhCTIUGLEHFjDaGs5ngi16Yce9U8uWvzMpacsEBFbdRDFyuVgipyVmwWu3ZbPYMm4FhmmjahuQ9q9WGwRcQjkiJoS95pxgCNsFuF6HWCG1I8gAqEQa6s/KdvjuRKw1CcH694ma9RYqEFIKYS4ZWkAv9F43MAlMJqk5ibQm/IzPI4lvUWlJVihQCZI1WCqkyQpQixI2BrAXKhsM6daIxmleeP+P26Zz1cl8qRSmKW/HzV2A/b9yVKUQ8kRQyR4YhFJhEXVM1FqUldnaCqjtQWwSKrqm4M59TSc2AO0zU4lvqiJwj++2Gz3zmVdL4FRhl0VURDMeYyVmirKWedbR1jQojXVujlGbyI28+XSEocK+zcISRkgPnoNygdWJ+0pKJXA1bbpbXhJw5Wiyo2qr8HwFDP+BI1AqO/8BPkrWh//Bf4KRTUDdM0jJOK0IyZHTB/IgICCrbcXTSkvzEfpzYTx4hJCqnIlHmc/VkydSU9/3bN2UhPm/d+JCBfGtI+XkbqUZpvujFu7wqL7nYXJJjesvXyEHinKU6ODfLlNFoRVMZoo4gE1lE3t7/CEpJ0kGNpJXl6vKauut44eEJT58+5UMf+hVeel7wvnsaqWuiUiAUKYNK0CiJbtqSA1xPBRk/lol7NReMfUJpRdtqlMjUmwXdrOZSrlivNgzDyL6bcf/ubWLKSCN58ZW7dKct7ZvnaFNBknSt5cG9M+yLLaauOD5r6eYVM32H7Y3i677+KxFGsjrf0G9HqO+Tkez3G3JI5CiQpmazq5FG4V3ibQ9P8DFTtw21lnjnWG33XG+2+GwYQ2BIB8iTC2SZcWGPmERZDcsghUclSa4rKinIMVHuOIIQJ2IqGUWjNcYKKlEhYsZITZKG5WqFc+GQJ8/knFAk5rMZjbQQYPCJUJBfWFPE3mMM5FFijcZPnkYburpCIri8WLFebvEhoUyN0JJxCoXo7CPT5BE4Uk5MMeF8IHqPcyMJUNVIVclCvBwijRCEiwuEzDTGQJaMzpNSpLIVLpQIShayREKUYnCex8/O2cy2WC2ISuNcRsoCa1IZrGm4f/cuKQdkDiQnSdqQU+R6PWCsQmvDrLPklPGhQNqSSMxmlnu3OtRdaNsZRccjyGFXvovbyOQigx9RSnC8eK4Au/YO7yOVURitsHPFbpyIUbBe7xESVrs1RhmOTlq6psZNE2e3jggx0FWWeVNhbHlu2W8Dxihms4quqzmNLRlZnplScaZuNo5p9KAO1zKRsXWFi5nHF2tCitinO3IC5wYqq6nrQsdUGaw6fAelwhrDUatRShOkQGlJp02JpnhXnqlyhkPm3VQl5+t8IpMYrAMtGEJAm5rKVpydLehqzbxtCaGnbmu0gkpJhLLU9X2ELFO3nAK7/R7nR5rKUB1WQLUR9H3CGkN9XJ4lYtaMIRSwiyqfpVZglKWy7f/N3pv8XJrld16fMz7DHd8xhszIjJwqK12u8lDY2C3bbSOgcW8QarUE8qZX/AOIHUgg0bBEILEAFmxYsEEIgZgN3WAwtru7VF3OyqocKmMe3vlOz3BGFue+UcW2VrWoZ5kZEYr3xn3O+Q3f7+eLx6N1GdzXuqGtBVIqJq3hYF4Tc8RGzcr1QNl4bdcd1WFTosIymMowpWQNHx9MihdXF2JobTVHRzNSymw3HSlGpnUDBGYzW7bBFqySRGEKGfX23hWSfgekiBeyeHZdYBxcsWPdxhqJiDYSnyJCKbIqdXT0Ae9GnCxxcp1KPH9+jbU19SQzjIEYioS1WENzWWRkWzLGRVE6tZMJMaRCcZ3W3Gw6tqOji4nRR3zIhc0hS+0dfGaMkRgp1jMl0VK8mVGGWCCOQpa7RyD2CpVSayAgp0iOkV8+v5jPz90s5lSklTFGYswIkbGVBgQ5ZcYhEVSJLtD7PA0tBchMiB6fIlqXSUwK5QuVcvEjplgaPmNlQQVrSVvXGC3J/mZvvA1IpTF1C0Ky2lMvnfNvsLxjv2HStrDfklhTgkrrqmzwLtZFTy+EZLfdoLVmMgUpBTGWBjhGD1mQUiKmgtsnFLN28WdK8t51rGWNMZqqGsvhZBRD9NSVpamKtMlIgUqeXZ8KpjiPCFGIVI21+0JbFbBJUy5WQSFTtW1D09Sk5CFFlJDoSpNEoq73MjtVokNSLMZnKSRSS5QQDMPI4B0+7Cc5QCJwsJyXjUjOjC6y7R22WuDHgNYasCASbWNRQha5gMjoxrLdBFbrgMqGeiKQc4vQJaoj9AWwoIVgeahLIyXKBSxE2W64EEFkplWLDyO92yJUS9UaQpBAQJmAzAoZiwTt8uaGvvf4CMiOyibmU8FiofFZ41xiaBWLec3b1hJi4Gy9Q2nLOHRc7XZE3dBvMy9fXOGkwlaKvBuRqUh358s7KGWZTSuayvD48VNmsxnr9QZjLNP5EqkkN9dXjDFhrMWHEa00SirWO1ekyTKD2NE0E+pG8fDUkE+XvHv6ftHv6wI5EW1NFPDy4gKlizF9ux5ZXW+5Od+x2W6p6jnb9YavH79gE+DRo6dszlf0uxEXCkQojBCchKkBZUBm/gv/h8h4De0BsmlwZo50AYBXVytuNlskt99l9lM+UBIiGhUjtpHYiUJXomwqZWkElSqXXl1rUnSARSmDUqVZEAjGscjAK6WLV1cmZDA8fOuAuycLHj26IFP8rkJkctpTOPeNYmkwS2TB/fwQnQzP5Bf0fWB0ZSOiKwUqU8+XmBiR2tJIi640d2ZzGq0ZxuJLIyXkHguac2a7XvPVF1/hOoORGmXK5xBCuRS11czsnKZqqHxgMZ1SNS0XV1c8+uKCXedpLQQB88mEymiyz9h94zVftKAzr18GLq8uUcqwWB5iJ1Pema9L6PO2J4lEs9hHqOTEZrtl3mrMYsogprhwRsyKhN5/VuXcsfWUe/fuEN2as6sbdhcjUhQwQ2RPgc0Fw15cVf9/uWqhVad9oyj2QyRRBkq3clQhkErz8Tt32XYjr1dnkCLi9t+MRE7s8x/LzEqojExFhZE0JJWBxGn/52VoN5mipcZoydnrCw5PAh99eI9Xry75J9//R/zat+/xx997jSTwxd/+faRSREDmjJUSbS1X1z1nV46Ls57LqxuE9GibiEGhleLopGYyr1GhYz6ZsckrxtUOtx3R04bD0/u4rkcbyemDEw7uTtFiZHp8SmVa0uiZTmvu3T8hyYHOeVJwTI8XDHnL7/3RrzGrKp780y+5ubxm/vCA7ejZvb5kdJHRZ7YO3FhTN/vIJVtjqxbbTEnO8/rykqcvr/jJswteXXlC7BCVJSXHbudQWtC5HpIoXsX9522SQikJWpfNYg6kJFm5FVbVzJs5xgi0TtRVg/CRHBQ+CXZXl6AtPu5HM7kU9bPplAroB0c3BjwlfsRWBkEijJExgbOW4EYOjpbMZ5Pigb+44nq9pbKW5eGyeDmHLWrfdLjRkVIkCti5SL8ZiDHQR49DEFNPawvszdYVKRvOXl0icuSt0yMkiu2m3PHz+QIzlFgBIRVyTz/fjT1fP3tO29ScHh9ijCZuRjQCubfImHrCyckJfbem79eMu4DQExKRy6sNk4llOq+ZzWcMu8jgPCEGMpG2sZwczzlZTlg2U1JS+CRZXb/AWIl6OXJ5MxRFydRweHhA8IGbsxu8D+SsyT6hl5mzTc/1ZmC7GWinit12zZ3TE05OlrSVYbOWNNM5u9HRyMzJpGFMgpv1FdELmkpzeDilsZaJ1ZzfdHTjiCSjjWW7Gbi5GojSo63E1BVVO2HbDZxdb1htO8bhDK00IY6FIxETMRefWisz235k6yKm1kyq4i9zqWw3D9opMgvW245qrxqbLWqUEhwezplOW2IsjcIQPGhZfP1I6rrizonCykxT12x2I/VEYbWmygJbS95767jETmU4O78i+cDr6xvqespsMiET6MOO9ToyqSSLecVsVhO9Zr0bQEQkGltZhPXUssGahsxQCO0iMaknCFnuq0ljOD2YIBSkXuBXnlsI2upmw3IiCM4jKM2zkeWStKdLJnVDRtKNI5Oq4p0HhwgEu01HDCP9oEFGpi0Qy2a+aQsJvFI1Ukh6F4udx3t6N+KkIiPZjgWqJHJG54AUZdOslcangNGGhCo0eRcYu74MjYIGEXny/JrpdMZh0KQUyRR2hzKibPulQlWScRhBCMYhULctMSW6vKOta168umZwAZcS2zHSDwGpAihFQpBCLsBFUUBXVohCdy+3Bz76vWZFkIhoqUqe875RFLDnCfyyWfxFfX7uZtHojGBEG4GtVfEp7hsO9gWnDxlr9B5kI/a4+rIRjBGySQiZyTkgrKAbBrTQSKFJGYa+FDDrmw05r0pBYwQil01e+Xv0pfAggRK004YUS+Fs24ZA8TJKoYnA4ErIu5CSWkeMNdRVxWIxw1qDtRYpJHnvuVSqNIDBB7x3BB/KBuRWxiVEobRlyD7uB2sFSGONJoyC7ZjYiS2IxGI6IaaB9dBhjKW2pWGusiZkiRRQV1BXlpvQISk0VkkkhbHEeuxN1DKDGh1KGVbjBqNNiQSBN/CgN3EBCGROKIrPNKTMrh/xYeR8PTKGQO8KMS7nzG474n1k0pSNX2UlJydHWCNw3hG8Y/vCFZKrHegjnL+Gbhi5utoyDAPdkFjMNG2r2G63zKYzHj54C6Ugjh0pJpaHB4DnyWpH0yimM0NYFWz8fFYzX7RMmjkxekSUaAknh/dLgZMyxniurzy2nVC3mssXKy5Xax6dr3j35JA7izmozGTe0iqLC4nOlwvxqBJ88MFDsnfo/fQyp8ykqalsyXTajTu6bEquKHB0PMNWhrouE8eqUuQUCeNIpStmdc2mv6Bpl5ycnpJi4PzlBWOXmc0rlAz0/cA/evwKJQvtV6t9DEdKxJypqpoYCiTh/cUU+/ExISyxlacfWy6/+TZJCGYHv4PRFhNA9COpVvz1j5/z6tHXHPzvx9jJgpzOUV4gqwOiaAjUVMtfAzHj2ZNHvH7yOWO3BlGaIy1M2XVnQUqCVjqkTJwenrBcTDFGYESm0hWNsbTWsFw03D1eYjIlMgZZPGSpNKTGGJRUuOARqaQ41jlyp9X83T/+Lh88OOW//G/+HyKCFBzlzS5bkiwyMSbIZajzb8S/DwL+TfUnPPric158uOT8YeL61RntsuPLr54RfcS2BtvUPHz/HvfeP6Cd11wNA1kKcLE0QapcZWN0pN2aFCXb3chmGNm/2lycv8R5X8AWyiIWdYln+fQn5aJtKmqruX/3hPsP7lMpze7ijLHrudC5yO2fnjFtWz786Jus19ew66kF+PWWf/3uf0vSlsvrzGy5BF22RFJmWhMx03doDu/gPfz40/+MbneJUh6REikpsjAczCp+/ZPf5u/8yW/xv/xP/5D/+r/6H3hxcV2yDnNBuWchiCUTiL2Od3+SF7dfVSlCgBDKOjHmojwQMkMs50jXO75xr+XZ65ZPhdqfdwEhypDQmLKtTrGoRyphi/ccRcwZGQxJGkQWyCwQQZAlSCtplwt2Y6QeR/7uv/LP8eOvXsBgOPzB58Ub/J1voHWNKihstiGwefWaz794yvLolN/7F3+X4+Mpq6srvnx2RedKnmplFFLC68+f8P0fveLj3/kO+r7BuDKk+vPvfQ/pIzlkJhPLyWGFNTV5s+avPv8eKMXbd4749Pt/wWdfbPj8Jy8YXOQPfu836MOWdT+ysJbD5Ywk4Przx1ilOJ3N6WNg1Q9879PP+PjhR7x//4TpQcXH3/0GJ3dOeP3FU569uuHDD0758P1D/mZ4m/liQSUtr76+4vxqxeOrFT5ptPTkUKR/2pTv9pc/eVoUOULtibbFE/eWO0Gaiq4b2HU9q5st69UWkBijETKzGx0peiZNVewcSjGmTPfqJXkzEkJgFyMxaxprMFbQNgJhJDomYhhYTGtS1XLeeW7OXxOFZDJtqLSg3168ibuChFKahCQjOJwvsFIUD71UzJoJSmtEhDBGsswlA/XihqPDKVobvDN7gmckJ4n3fj/0FHS9pxsc274jpEjd1Gjl+fLJihgSm+s1dWuoa0NVG0YXUCJzcnxIYxv84GnqGlvZPW9A4neJH335OXfv3qNqLecXN3z11QtCiDx4+4TDwzlPnl6CCFSt4HB2iAuluWqt5Z2TY9Qg+cEPPmNSW37zt97j+GhGbS1f/Og5Rmv0ZMrhoceHxM1qzUfvf4IiE3allrlzdMBqNbDQim50PLnY0kWHUJEP3lqgleL8YsPVZkfvEveWLbUVVE3LbDrhejWQlcDWTWEVbD2oyDgkZs2U5WTC4WLCtG2xE0WKET+MSKk4Pj5gahVdHzi73ODjiBGGoR/pU7EFWKVQAl6vBVopKmPRxuBGx2Y70HeFKC6kYbuOrG4GpJiwvrwkp8DaDftA+08RUjGtLHbveUYK3FD+jUOCnEOJNsNjtSWkyM6NbHcZ29TksGXeTllO5pweGJrG8M79E6KH7WYHPjOZ18gMB5M53g14P/Du+ydFsrrtGEfPZnRkaYlh4Gg5pRaFNfHh24ecbbfMLYy+3NMyZ3IKnB7NIAu6oUAWayN4/eoVlVFM24qkNKIKxKj5yecXTKY1R6dzrs5Hrq9X+JT2TA9FSomsYBhGYgDvI270LEyFj4EsLVprlLRopdnuBq5fXGKNZtq2xCARsiamxBAz2gje+eg9um5HNp66MmgxwfvAetMh9z9fiIlxHLGm1CduuAQBzbRmkiXLaU3OgtEF3OaCEEasnZBJxBzKdWoyMUS0EkXCTGJ0Q+kJZBlWilSi78ZUVH0x34LW9oxzpSn54L98ftGen7tZ1Ea8AagoVahuUhbZZtlMaISUZQuWb6VN5deWTDpZ1tApFUlSzFRNAymR97TN26G2NAKBKl4jKYp5llLA5ByLD0bcYhqK/E9LgUAWImn5mpJSIsRQYjt8QIiC2h98CT42WmO0AcoGMaVEXddFjokoABjnUKrAPpSSRWftIjEGtLVvABJyXzTpVDaPMZYDth+2JWMQ9s32iNEGqyV1XeI0pq2l8qUhVFIh5YjSioWwCBGRErTZxxhYQ2VqUoyleFVlX7g3K5VQ3BDIZKRpQFpSDpgcqSqDEJYQMt5XDKPnmkznHdWiJWeBsQ0hJEY3cnOzJaYiJZRCMmkm6Mpw58BjdcPJ3RNMZdlsdoQQ+P6nX9G5kc6PxFzRO88Xj16gFWhZcPJjhLo1JF/yLp133DltcSNEii6+H4vPpGkbrCwQkxQcwQ1Y26JsSwiwXY8IlagXioemRWTPs4ur4hUVoCZzhLb7zbFnDBvGa4eVCq8EMQVSzAQH89mUMSa2uxvIGVO1uNExusRms0VrzXQ2YzaZYGxpGKMrGzGrKrSWjEPJQzRGIVIix0hlG+ysppmVL/jmZkU7aUsz5R1Saeq2pR+KbGknbfEHJU/2shBBk6OVlnocGNcbQsw0VUUaFe8ezbi3eJejv/eI9uQjEOVdTWmCElMyNT5YpFF89dVTnj49I4YC0hldQopY3uu9BvFkXuO8Z2prJrbBKo0SYKRCCkFlNFapAlWqDFoIjDWQI2NwaClJKSAoESQgyankso67wJ3jKd/48A6zac1669iLD0BI0n7jJQVAgWNleCPZ8cPA5mbHGDx2rhhj5uJyQIrMSVORh8h/9z/+Jc3EoFTxIosE8vb9SKJsxnJCRk8/3LDtB1JU/Mn/VeP7LWerGyZ1y2IyY/SRx5+/oMuBfn3Fg5MTvvXJB1STpvit8sCwcayvN2y3HYNReJHJqud4MWd5uEC5hFAV19ueXd9zcX6GMnOWy5qcIhcXA6djoU1+9sOvmbcz7r07EpTh5aviywmhAK5uIQLWCE4PNe8+MHzw3gEffvAOL8+vyxmUS4mVZeINuSiLNz5H8i1UyjL0Rab+Zuu4txn8z0KjZfGRJj9i96HUMZYzKoVEloXsLGUZTmmlyan8UbbSCKGJDhKZZ9VvoJXmI/X5XvWa9pvfQLfa4uYrvvHgLZ4+f1okh1oxm7a0k4MSy5QDKSQWiwXf/EBw7+F9kIFXzx7jvGM5aVDsIHlEdsQA3/rmfarWErstxkqmIkJKrJWgcxltDcvFnEorHr+8wtoa7/R+gDilmhk++mDKvTsHCKV47+E91ps1q7FHZMNi0jKdtRx8Z07od6wuL9k4jak1f+sP/lnefnCXhx89JLrAxcvXPH30hHHbc325Zbo8YBhGtusNOr+kmTS8/d4nfPzhQ35lHEirGziacnV2xcXFmumk4uRkwW9/8hZRN6j5DGkkcdgyrntsWyOrmqHv2K5WXF1e8fJ6QNuWyfwIKeD1ox/z8sU12z7QNpbpYkZAshlHfvjXXzKuE1YZjKlJwReoUPLkvP+OpEy/6ZFXXVGM5Ii2hkprlIhEbmFHpcQw8qcy8uhGNj7gkATniyS3qtBKkYglvohEpRWHiyldP7Babcr9voedr3QqITAAACAASURBVPqh2FsQxJDRUnEwa9/kDJJhvelIlSItSvamtYb5ZApZ0Pc7fEgMYVuGQbsV0cPbb52w2Rbw2zg60NekWO7+o8WUHAVuN/LV9Qtenq8REupKclk7EIlKC5y17LZbvBuJAvQWtn8x0jYVtbZsdiM5QV1LKqsAQ4iCw5MWUiEubHtHP/ZIodFa8ezJDV8+PuPXvv02H3/8Lucvbhicp20MdT2j6xPNXnI9OMfF5SV17Xn3nTlNXQZtwUn6oedqu+N6LZFZoEQipQHfGXad43q9QwqBD6WW2m5HYkpMZxXzRqNlxmZdYnayRJK5M58ilcaHxLYbyFmx2u5QWlHVFi1B+VLXeTPStgopLMNNYGprKh1BZipRLEsJiRtHpMlM9hJRkWyRZFPkzTMjOcoZv0woI4ixKWq3nNkOMKZIeHGNc4ndbkApuLiOBbimixUip4x7sQaZ6LoB70ukRk6UDWFdoUxRtIgscCFBcrdFKUpLDJmxX5PYD+j3d6KRBmJR+ZhKozIgBYfHFmMVpIAgMZ/VWFOI4kYoiInV2JFrS8oKHyO7waGEZPARH2M5Ywm40WNrzamd05hS+6U2lFxnQAiFVAbnAp1siDGhksSlzBDA9YXBoZRESvmmCRdaImxRBcUQ2Ww6QhzJWeMDDEHgsyKOxVogKLnbIZZm0WpLGFOJTRvjm3SEmGO5a0V5nzLFpyiEKnF1xkDO7LpfNou/iM/P3SxGB9kUr07nB+IbSZLYzwhK0ZH2hLyQIzmnUuDIUkTEWF5Yo1UhHA4JufdB3YZOZyEQqWBLJUVWlWIk5RK/ocsIA7lvwGJK+0bslp6qCuhAFMkZxmBNkWpOmkn5PTEwsUXSY0yRyrVNhTGGvt9hlChyVmAYHSGVyWdIIIUkxIx3iaYBvdfYv/kU/IjzAanAWss4BnIoP0/xc/WktCOlIsdQSkHaIYWkXTYkXyq8kBI5vKKxpkyfZMQYzfF8hlYCaxTGKNq6REhIUT4nrRRNU/7cnHvcELha7Qgx4Xwsk9i62ksFIn4cUSi0LVLYq7PL0nBLidLFd2iMRSB59OyaIXY0TYtRmvjZc6QUPLgz42g243e++wkH7YxZU3O2PuP86prL1ciuGzk7v6YbHDdXG9RK4XykaStms5bh5a74Q0VEK00cPCTPnROFV5m/+usnpAwH8wXvPagYx46h6zHC8NbdE955cMrl1Y4QR+q6RetC4/RuQ06O2rbMmorQQqUtl+s1KRdYjZGKIQx0YYsA5rMJCEX0CTuB7W7AmhplFKvNFT5MqMOkUH0tpOiZTQ7RGvp1j9aa46ODMkXPkfV2Q4ieyWxOodtKUhiom4a6afA5kUXAtoq5nTBpavrNDUZP8eOIcJHnzy/56vmGaet4/3TOYt5wvhvQrmQoue0V07uPMNUMe7jk37J/SUTz9/2/gMoJRyEd/uD7f8XTx49JrsRVZAQiC0L2vPvWXd5/9y4fvDXjn3z/C44PZ0xajVUZU1msytQqc7TPOhTRUUmLrWxpKlLxzuycY1JbEpHgBQID2mAVeL8ljoHjxYS/9Uff4k//7Ic0+oDNZsdq0+/ZKmXIowCxt+kJCoHNp4jPgpw1wQv60TFfztFKMW8rDuaGixEevH3Mhx88YztELi+visyISCiBakifSTHw5VevCUPm9dkK/9zTthInMy2qSGzTyP3TCS/PVnzjmx9zOG958uQlXz19RVCZb3/wgMWkYd11jH3H28dvY0w526xVtPOa43tHuG4sUK1ek4zkZrXlhz/+guOTU95792OqqiEEx3tv3UXkgbE/5+X1hscvzkgxoYTa93vl8tXThoOHUzaf/hXV6oz37iz4P/egmZzKdhZyUUPcNow/0ywaXc6I4D3Cpb2PZK9KyJn/RLbU1rLUkl0/cDxtmLYtFzc3LIowFCjDsdIoKm41xDFFrlZb1ruBbjdwsjzm0f2/gxCC93b/AaqSWCtKfqkqMQef/ehLfv07b/HDxxtilMjKcvf+Ccs7x6ye/qsQEwfHh7jUc++d0wIraRravT+YYaCN+/f38IT2YMnd+3f48LsdTz/7krFzDLkuWy2ticZDhpPjBafv3+Hu3VOksXz3t7+NzIncdfRu5FcfvIOREd93PPrRY8zygO++91GxPehifZDBst313Dk+IcoEMlFXZXtXGcnzV1d8/uljotB893c/4b0PFZv1SFtp5pMaub1hsxtxjLDribst2fV89o+/ZHGw4IP37qOVQSB5fvEKbSpy3xNSBN9xcLxERY/tPXWMWK1IVc3Bh0Xy+8XjM55frPnW24f8/nc/4euvf0JEseug6x33FzWnv/XJXtaeIUQGP+BC5MX5hsEnpmbC4axmPqvYDD3rznG92rLZ7Di/XuNSxIe4t08YUpLIXAZRWWRciAix97wZSdtIxuAKoVvmIlXtHdmX92ZwjuvNuKfsFvid9orgy0BXUJRN02mLFpL1zZ5dIAVCZhbz5s2df31ziTW2bBRdpPNFGttYw8mdKQfLmnFwuEFgDueEGFlvunIuJhiDZ0yB+azh2588pB88m02PsSPRZfDlXHIMjEOg8wNtXXOzGZGi5DJers6Yzw85mE9JIbDa9Wir+OyrJ+QksLrkV/vRMWkWTCaWwY+sup5HL1fcbEdMKsX1Yt7ut74lZ1IpQRaFLO+zoPOJ82vHerVFasHD9++xMIbjI12KcwIkz9t3ThmGkZcXK1brjq4fWR4uMLb4USeNpVKBXFt8tnTDwOXlmhQFi+WUy6srfArMpjMaWzN0O/p+y4O7p8yahnF0rHYdLy6umFYLpm1N22iGXWC78YjGsBlGnBtR2RdPbBZM6pa20ux2W5TUGGXwOVBbs89alHS7jhAtkUDAs9k54gpWKhMF3PSOsXdIUTIfrS7fl4wk5vPiUSwVIymDzIkXV1dMbEOzmBS6ae+wTcOkKkAZFxJN2zKxFkUsYUb75YFSnu0QysIhXBevbFTFotRIZlNBO4PFRECek4LBe89VtyXEiFEaoSRdV4Bgjba0TVXOfKkgZeI4IJXgbL0rKj2rkNagbFOsX1pg9wwAhGQcA50rCwupSmxZ9G7vSEj4WN4XrSw5RpL3kCi1pIaLmw7nE93oWd0U73hIIyILfJaElIk+IRA4H5FSI6WinczLsiQldAoYXRR3UhY6vjKGFMtCAyGKiuiXzy/k83M3i7Y2hX2Qc6E67mViYt8EiSzIqSDFhZRl0pJ/WoikXCbGYu99CjERQkSmTNxvEFAl2Dvv/YPcemtuKX4iI0zZcqWU996mgust4yB1u+grMlEEUkBIJQ5j9KWhjbHk+0kpcSEglSQCxkfGoWw7fAQhJT4EUixNaZHU5hK8KjXBZ3wOhQQly8ERE8QkSGQICec8Spo3nqwybS2Tmd65ErMRi+k360yO5TOLqQSyxlCiRxAZbRIi9yiZywTcKEafMUYhSQQfCpnWpbIJCI6YBJsuElLJcvQxsu37EiUbyyWvUShdwuk3m44YI7apMVkwjB6bxP6ziriYyL1DyUg/xnJ5J89mM7IePSeLgYPplD4MjGMmR4VRFUfLA+Yh0e1BQSE6pFJordhsR7wv0QVSSJJPaCUQZrc/RANSKkJIexz1jrH3aB15eXWDWEvczmGNQOQaJ0dG58k6lLJWFmDB4BKxScQkCuVWa7TVVL7QSXMGaS3aVmgxoLVFSlkkI1Kg+0BTF4CREGC1RFfyzUpcq5J7lDNFHiwNOYSyKbsN09W6kG69R8aI2AcYi+IE3/vABH0/ICtQVjGd1syXnq+fndGtB2Ztw8HJAYuJZXbnhC4PiJQhlELga7kCocHvhzfCkZPgqy8/5+rqipzlfhKZyUJAEty7e4fvfOtbbFZPkdJStxW2lmhbIFCCTGUVTaWwqshO0x5ucNuHCFE2CzHmkgclyuZJkOmH4sclZgyK994+4fhwwvXlSIx575+7/bPE7Qv85pEZPBEXIlrXTOcLVhtH54vvYb31hKy4ezhlOZvx8ftvcbPacHFxXUife9/Xm3MlBVbbQs8zpsTTTKctSmeskqQckVqymM7JSXEwq7GVQhvLbDJHqkCjVDmHpGS5nHN8dIDrex6/fEXIcN175sslrZFcvr5mt+v59578PVLO/Gvy38eNntXqgjp4yBmdEsuTOzx6fcaT55elAEbuP4ayaU0iU9czajMhdDsqU3N877A0/vufEyjS+NuzkJ9pBvf/LezzJ8k/8yv2hUaRqmZSzPT7fLXaVsT800gfcsnLlEKXZpaMkgLnEttNz3pTivJ+8G9kR4lAFqWxlEisFvgxs9n1TA9ntJUGWX7Oy6sbVs5jchlMjWKHC54+SM6vB+ZectgYhMmsbnYkIZHSgNRl+zkEgvfMJhMm1ZTVKFgPHmVHptMJKYMTib7vS+RFSjSVwvvEq/WKzW5kpy+4dzynqYoPMntNHgNUAqU1SoJzA8O44ejuKaRICoGr1ZphGJCvzzm7uGK1WpGypt9sqKbTAlpBUMtEczjDTCcMCXzI7HSNi57JKFkcTNEmEeJAP0AvBJVKVKrEp8RUSImD81S1Ybtx3Kx2XN50xIuRibFYbTg9WdC2U+p6wunRIbqyBN3SjUU+fLffIrTGmAZipNtck6XgyasVuy4wdBERPW/fP6Cd3GUYIxerHbvtFqGKZHIYRkTOuJAYx0DfO4KPSFWykWOK+N4VT/0+KmuMfRn+KglZkhVc3mwZfWT0BQjng3gjgQyxfF+lKl6ptCvy+X4cIBWGgkwSXIEvlWI0E1UqQ9+Y0FJgqorD5ZS2rRhHj/eBmAMqWLrdWABQEkYf6EaHMHufZwooCU1lMY1kSANIg9RyL2M3dL7cubdRM97FN3FR4+CIweN88YD3u5LXN+pCvRZSEPDshgIvM0qw7Tt8cIix3MFnNyuUKRm4E92WnGZZtr8uJZzfcXm9Y7fZoo3iakiIFLBao5WmspLaCoYRYsz0vaMbAtveYUaHdx6/j5HS0qGkwhixjz3LBOcZfYU2FRqDtRpba6xpmc0ss0mFkeBCxjaG2bRka0afuNp2jIOjVmXruxvBuUAKmemsxm23dFIQgmXoCwiwWbZoysDAx0QWBbKS9zVJJS3zUw1R4rqRIDKqsoxVICaPVAKVFTkVCKCsNOPgCKNHily8iMqwWDQYYWiqAjwMIu+hOJLBl+amJZNSKHYLqfB73gY541IZlPk4kGNGR4sYPbnL3OwcvbMYlfBhRwiC4APbruTE1tailMbFklEuGTFqgyqESFJIEAN1U3O1HfExctPt4Y2xDAyMElRWFaUOxTfqY7mfq0pTaVP+354rQGIfXzciMhihySoTckAGsNpgVEYrhTucMrjMbtiSgJAFKYOWhYAqRalfpJD7u7VI/skZs2dypLTvBaQkxsI+GLwvtodfPr+Qz8+fs6j2zVkuxnKQGF2InxJxK4Ik5kSMCWs0ShusLrStHANKFcInUjJER2WbIktxpfCIuRRxOf00QFoJu58KFc9aSIUOlXKRmiqlSDkSKIQuidoTUsvfO6eyBofI6DqEKl/uYRhQShFjOcy16vc+rkDOexP9G/VWKYT0fgvaNBVKSkaXSoi7vC2oy4uSUyKkRMojIQaEGMrPcNvF3pbE0nGbNQiZcXD7n63AR4yxjM4DUFkDIXF2uULtM3q0Vmy64Y20IAYPAuqqLk1JcEipcfuDMiMQWrPd7hBCFqpVEPQkhA9lA6wK3CYGhRIKgcYPZQqkG0sbNUolUsy0tvih+jHjUsePf/LsZ6A8IFHIDFWtOVpOmVQ102lLVWvmjdpPmCIhCfrOkaXAh8RqPeJD4vWqJ+eAVoraGhSZ1+drfNo3lgo2bkUIgqk1GA2jvyEBXT8y7rffku1+1hCxbUVOgRQTk8mGpjWkkOj7VJp4YqE6SshZMZnWSJkZdgPD4JjPeo6WE6QsP3tVW7rtrsRITBqcH9hdbtDWEMNAZVuaakocdsScaeZTMpTA3eSZ2ClN2xJjYrfZ0K9XnB6d0K3X5C5gtObB8ZzTgwlNU/yal1vH89VzBi345vsfkTaJ5X/0XdJsyb3/+AUlxF0T0wboSCFwfTXyZ//gz7i63BLLjqoMN3Lkmx98zO//7t/gb/7BP8O//e/+O+RQBglSGZq2RaJYzC33jxccTKuiCiBCLkHtt8WSQmFMRXADOZZ+FRFRObO5WZOFQItykXz8/j3++T/8Nv/pf/6/kVBkkRGk4m2jFFpZ/PQiSRGCgpcX1zx9cc39A831euBsfQFZEL1m4z132prVesfv/433aSbw/b/+GhcTIt8qEAo52afAi8uOg0lHP/T8378jmU8Tf/x9xeBDydJMCbsJqJhxry6wlUGZirfuHTNta6atIceI0YbpbMKu71hdb/nhF8/oXWQ2uaRuLCdHM7qrNSGUYjoLuHN6B2My/fiamEZklrx4+py7n3zMj/7iC/70H3yJzAYly3kq9/Eiyhg+ePCAtw5atrsKM5lx74EGIcmiZDP+VKaR9+dN+hkaahkardY7YoDMLeimZLFIoXg/BZSPPNnCdR85XsxYzKb41wXJHhgxxiBQSNR+WBIQquDU15uR7dZx786Crh8IKZXGKjjykPFRI8iQIrtdz9W6o9sGqvkR1hYw1vMnT5FmxtFsynQ5JWjQxhJdZjZb0N1s8KsN1pYNk2rmJFnI3OubNWkbcGGknk2QMSKDozKKRTulWc7Iei8zGyU3NxsOD2oe/eQpL89XXF6uOJm1/OPv/SnvP3zIvbeKbNp5x0034LY9tmuZTmqmtcAYuOlXNKpBZ8vL8y1jirjdhuAdd+8sEUh+9OlPCMowryvCsqX1LWl5yPl65Hr0ZFGsFy5pvvMbv8rBrGXYXLHerPFpy9HdJVWlOGgacoSbG0MlJ2zaHtE21LZibi2qNvzo08dspOOtd485Wc6gC1xeXoFq8DEzikA0hap4MpvRmIrZbEqtMzrMUbZmM0T6buDl69dcrzdUtuJgPgMEwzBjYjVN2zC4kcqWgvDsbIXzAY9EK01jDIvFhO044JRg7D3ryw3BB/S0QeWS8aelJqP5p18+o+sHZC607pvO0Y2RzWbAh1gyayny2PVuV7aYSiKUwOWAFaYQIDtPyqLkE48DOe6oreF0OWPaVDRVzTA6drseKYtVZrPpcN5jm2IDSCIxaSyZxNCNdJsN1hiapiVnQ5CJSVshRWYYI7pWtH6GcwGrSwSWAO4c3SemuCdcCtqqxQXHndMiRxbCUNU1uhI0lWG37vAhMZm2pCBwKTFufOEDdGtCITMwrVpqa0AKRj+SoigbXpFIKTKOka8eX1PVe/RVjMzqhtm0xeWyXa+URklBkgnz/BznixQ1xch0pmnbCUZamkpjtcSNgf58RVM1pJToxwuEliznLcvZlMCWTEIpaK3l/t0jXB/wITM7nDEjcqdusbXi7s6xWtd0PnFyekB/dUWWmojkZgUxehaLGg2MzhFTpm4Lo2E2rfAu4VxgMi2b/LWSaKV4d9qgEFAVFUWtDd4HxnEgkEkRbq63hJCYTidMGst1t2S3LeA4KL7t2WS2J+NPyAJkirjoEEJhtWVwgV0f8Pt/V0WmbWvq2hJdZBwzq81AigPXUJrmFAo9NBXAo5aawXkQYc/OkKz7ns22IyMZXSS4gPMJYyyR4i2P0e+VeuJN/ShkgTVWSlM1FUokcg57qI3FCFEgZUKgbLlzfSx5lrO2JQE3m67kylaFlkoGqRWtBu9NqZFzJgtojCbnQipOOZEye0Cjoqo0BV2ikEoWifdeuWK0QlSG3MU9XfuXzy/i83M3i9ttT90U6Ifbb5SyLZs7KW5X8oIUUwHfRF98CY0th3GUZeKQwGpB3ShOTxYMvSOGTMqCrhsZxiLfiPumsakaAAKCmBObbYf3AbGfUCupICeUkAUXLfMeCSzKOx8iTVUVAI7OGFPklYpYXr5wS3ctX9ohZFCiyGFhL3WNpFim7xmJGweUkrigSUlQ1QZtFCEGxqEv28WYSyipBBcdVt82k+WFiiGhpOL2XRFSYBL4lArVlIj0fZGzmqpg2H1k6AeM0uRciKpdV4pDay2QUUqThcUKMCLhwogLP8U0K1n07dZojJRoWw7HnAp+36i6+PjGiJGaSmvGoS9TNCmIAbRRROcRueDqrTUYq5jYQjqUQNeN5FTksc5BTiNWW2xTYwdJLedUlaJpDXdOjnGjw9TFO/H02Q2rzYbtLhKCY0w7EAKfC2WrDA8yKsNxu+SgbfBKMowbpAwopWik4erZisFFvC/03uWywUVPPwxsdgNKa6bTmjj2JDSJhBt25H6EyZShj5ycHCIl3Fxd021HDpYt7zxYFr+etGijCUNPOipNSEzF+6KUxg0j8+kSs1jgoyMKqERGCKjaCikEta2p24aUMt32mmFzg7h7j3becPPqjJQFdVXTCMkf/fYnbLrM64sV//3/8Zd8+qNznny9ozaOjx9VyIliHFeINpVNUVwj8Lih4vrlK9bXW/zY74d+CilL4/zrv/IJD999wGQx5eXrc+4uFmxXG2qrUHNNDpGD+ZTD5YS61pTmY//e327K995hYB8FU8Lms4QUIzKBzxlywXhPqjm/+sm7GC0ZQ9n6kW/VAuXJ7Kc1+0kmaLa7ni8ePePqXHO12jLgioRcWdZbwbMvH/FgteIP//Zvcnw6pzIGl4eyach7JUSOiFwkxvgd24srPnv/EGsdv/m/epLQrLeOvt/R54HKWiamYTa1VFWJCphNj7hrlswaTZtKI/Ty2WuEsMzmcyYp0yiFzJluu2Uy0UUi/Vox+lBiWuYWM2/xn/0x41Vgu9vSxR2vL294/vwaRYkMKX7FMlXXSnHv7oTFLHBztSvN2DgU6bSUEMtkufSK+93izzTdQMliHX+WQlc+Z5mAnPkP4w4i/MtCM4SEMUWuX1QTRQooZRkYFs+6QKq8p0ULhBIEMm1T4Yb+TZEQhogjoasCdEjJ48PArvdcnm146+ODfeGemLYVx0enNJ/8v5i2Qb3+l6iaXArWLvDcdzx9coatJL/yrW9T1TVjTNzcjHgn6ZKjtoaQM5vVDWM3MlssONbTEi8iJUeHS6xa4K+2NIuGl49f8eWzKyZC8daDJT/49Ps8faHZupGH907pdxvufvSA8aZj2ATIiYPplPm04tMnX3JycMp8tmQ2P6CqNKtuge167i0sk2nDP/zzz7k+36EWECc1RlncLvPk63N+/PwVUibmtUDKzAeLX0VNKg7mp1hhUMlzHXfYqNDOEUMAP4JosRaU77l/eoB9a0bop8QhcLNec3Vxxuo8MakqprOaMdWcX6x59OwFKWdODo/44OEdKi3ZnL+mqjLHB0ekPmGrltZK3rk74523D3n29JJXZ5e4GAiD5/RgzqvXrxEic//uEUJIxrHHKMPB4ZR7dxZMtGBaWzah5fiDd/G7npvnr0gRjt95B+M8IsZCSZeaDz+4x2bTgSsyuBerjuvdyOOfXLLpevrBMfiBmCM70SFFoUAnIikNVMoQo8KLhI8lOuD/Y+9NYm3L0vyu3+p2e5rbvHtfG/GiyciIbKK6TLsKquySjWyE8MAWAw8YICR7gjxhDEOQmSKYGiTGSIaRMbJc2NVAdZlVWZkZkZEZGfHitbc993S7WS2Dte8LUwwoJQLVILcUiveuns5755y911rf9/3/v7+1I1Vl0NMEL6bE9XWHiwNJJkpVokxNP2yoZzmeZbQjWsLBfEG3t/SuI8ncwEoxMAyOlPIZJIWAGx1IT6kNwXnK6ZkRIrFcLNn3HftdACERCsZdz/LBIYUyRAzSKHy0JF/g3S57+uqKYCV4z2Kh0YWkiwOjhdEl9sNASBFd6JyfNzrKouDu6XFWTtxsiV5PedMO58ccxRA9g8+T9633KCFBTLyHpNl3I92w58DOaAfB2N1gtGbezhBCsdleQpI461lvOnwKnBwtmc9q3nzjDnVbcjyvKV1AzgyjHTGV5uu/9BZGC7jyWXFyaLFuwRgjy8WMcGdOlAoX4cnzl/RDjy4iIgpEUiAli7bhZhTcO56x2w/c3DjOzvcMfiBqxWFb0agcP9bM8/T4cFFmQN+24OJqz/JozmFdMwyesiqpG4VLFtfn81tKiRQSbZ2zL8tZg9aK1eUlu95Tty2lUsxCpK4Lum5gu+4BOJq1LJcLdtsdvhLU0lBUguOjhsJoCpUj6KzPed5VVXPTbbOnrxtJMbFtDJWpGK1l1A5XwPVmoB+zf1JIldf2JClNVnV4n+9BgcC0mcWhRSRnOCdsiATnGIccD9Isa4qyICSLFjnD2kdY7RzD6CmKDL4JPtLUGmME/cahlcSL7EXuJjZH2zbElAgJkpAopSi9wHuXz7VFgSDk4U7KVi4pBJUupunpz6+/jNfPPlkUgr4bbn+DFDloExJGSWbtgrZt2O875vOCfT/QWc/l0xU+TIdJlQ9rwcdMyGOVIzimTEI7FQJSmpzhlSLITcYe61xVaTmF3ZOLSURGiUuls1neh+mwJJAiIVXE2i1CSLpekfaJEAN1VeNcRiXnA2kkpkRRFxj1ZbdDSsk4vc/srxfYGBApUBaGFAXRWVzI07e2aacDVJYYJgSShnldoaSAlA8ZSt0yIKEoKrQs8MJOkJAJChQcLkSMzpLCFCLzwxlaZBpZShGlcjZiphf2DF3PbrvLry8S293Adu/wgSzDjFmWkCZ0vlQqTxmnPC+mTBxt9OQ1HdAmT6FKVaJlgdAVSQYkEUVA6pBlCYncJXYJUQqUEowWghDgEyY5Um/xzvGDH7/g9iCbPaw1ZZEwWjCrKkpjKE1JUSxRIhfi+8Gx2m1IPhB9xPrIH958inMjujQs5jPuLpdU2lDXBZTZ4zGrK4wQNHXNdrPl8aO7+NHhbZcnE/P7kIpcHM8y0VEFR11nSeo4WKRWHBwdsN3sCDbLmkk5aqTfb6jqmnbeIKRkp2AQrAAAIABJREFUsJZhGDg+PaEwJc6O6DKHd7t9n704OvuQorMMQy66757cxzx4yHq/RwrFG+++ibfZfyF0wXA9MteRw5OGr/2Dv4MvNS9/+AWfnO9Y/fYlZ/YF/+0/+m948Y8fcXI34TYbqvaAs3PBb/3WH7L1gigVMklikhQaHs3v84//83/Ex08/5l//7/+cx6cHfOUrd+gvA3bbEw5H7p0ccVAbNJ6qrDO2ngRRYApFSC77Hib6mR0HpIGqqNAiF+HCeBhGmrrCRfCh4+3HJ3z7r3yVTz874/nZdZbrTq8D+V5ME+FGlAppR+4sKkZv+c7HX3B9Y/n1b/0ix4c1Key5dzCnu5voh0h3NnCsCr75zgm//8MviAKEyFO9KCUiai72K+4/POGDd+7wr5ZrQkqYMiIE3L1zTKXvMnrH7KCh3/bE4Di5c8DBwZyXz8559eoLnvlAKQ0JQbuYo4zm19//kKoyeDdgx5HF8gRcYhgtxY2mmTW89Qv32G16Pv/pmu2mJKXIbHaH3/6f/hnf+f2P+PTJUwoSVqRMOJQFCUU5a3n/qw/wl1eURZ1x7iZBcIigEHI6OKe8Hr2mhmWd3JSNO/0nvlRwMJGTQwyv/Y0xwsurDXdPl6AEwTmiSyhd431CyoAyMhd4ymBdpmAuZgu6PQx+pJ3PuCIX+xeba7abPUoWbLZr2rrgnbffpaoEx+8Gnr644qNQcHzQ8tZ77yNGj3v4CU4J+s9+lS9+9AIDHL/3BseP36S6e5/oepqqRgmJaEoOZhHGDrUJbPYdF+cvCbpA1yWj6zgvDHOnKDxshytcvKLrO8anA97teOuNCukF/9v3fkR58g7Hj+6zWLQMw4bFcsbV9Yr91YpxtebZGPhdr3n46C6l2vFsHFEXl5wsD3hwUPMHlwOXK83L1Ybj5Yrf+M1fICL49NNP2bnAx19couQLFocV3z59k2RHZjKxWJ6yftnx/T/9HVx0nBwd8/DefUJ3gy8NvS6RpuTogaaSgYvzyOgHnl5f423kZj9yeFByerCk4A5KKVShwCR2HTw6mfGNt1oiiXZ+j3p5hB/2tDpwcnKH5+fn9H1PjImqKrl/9CZVWXH33mNCmqZmqmK32/Pq8hrbd1QaZos573zwHpurS3SSSKMJCq59ZNwO9H/8MaqpOTg9pdAae35GagtiiOy7EZ8Cs1mirQuMK+gGhzlseLMu+M1ffkQ/eIYhYH1EG0GIlqaq2W9HYoj0LvHs5TVVoTlc5qD7edsghOHZi3Mu1xueXa7Zd5anX5zTDz1NpRnGkV5ASIq+29LWDZWQDKPleX+GlzD0PTpIChOwdWC/GxAE9kJlb12Z43w2m2uEyVNSF/Lz9fzlK1yK+JCnY73NjcWX52uGwYLUSK2wzrNbf04UmrIoWd88z1CuyrDpNogEpdRoJFKXKC1I0dPv+uwzNG3Ob7zc51iwqiHEERd6RpsbfE5ous0O4QNVVYBSjD5kyGDGQbBYtJycLpESNpsNy8WCGBMX12dY51i2Le28RWhFNStxfY82ms2u509++ITgIjJJ6qahmEf6dfaA3v/uM4qq4Ec//gmnd5b86jfe5nDeZHvLeMGnz87QUy5lWVa44An9DXVbE7WkG3r8xnG0rBjtbXdLUpeW48Mlx8uGzo386OUl+53n+sYideTBaZsZDiHSD57lfA8qse06zi87pDK40KOFoKnK7DsvS/6P7z1jPis4XuhJUSHwNpJcYBTZXqKEpK3mJBIiZZbFy5crfvLFM5JMmKLEO9h9EkgSmlIjYrYMWZeVYPO6pCoUd46XVFVBVRrunihiUNRmjjYaFzJ4ZxgGrAtcrzuQknffvEciNzfs6LLk00eGcWS5aKmqKkfSBYdK+fyspM7Wq+n+NFqByzYZLzK0BgpiyiDHfhwIUXC93jMMEaMlZaGpK4PWmT/hAzgfOb9c0feWOGYSt7MR23foiQ/SLuYMfc96u8mfmfr5ZPEv6/UzF4tSZB+euD1wTIcMqbJJeN8POVMxRmKwlLWhagpmswrnHH035JtDCMwsm3LHfiCmiNIZbJNsDqcvJ0lnSuD8QJwSo7XSlEa9lpMhM2QhpUhKmYwlpkIqg3dy4Hdw7suiKIWcTxfD67gJxNTtkCJnIwkzQXViXmRiBvNkGqEghmxItylk/+MkcUuAnV43pmkuOXXtLtUwTV6yx0rJLCU1SmJUlrKqIn89Wk2ZjCSkUkihWR7PKE1B21TZK6dSDk8uc86f83mi630gTlIDlxwxwtlNx2g945jR5j64ySKWu0kxiteQklzQMuVOQkx1lhoLkT14JETyKLLkQU7Ze8Tc3Qp2ROBgzLIINzoKo9Dkaa13OV4hT4ane0garB2JXmKloN93r6NIjBLUlaEoCw7qisPlHG/t9B3C0bLD+/w+y8KgpMCHQNc7rtYrdKGpy5KUyJmc0XFvP04Apfx+zdYxb+coKUmbfD+JkDg4nNE2Y55+x8iuGxmHgRQCWivquqRKBh+m9+4DSkNb1czblrKpM5Y/JmTyuBQwVY00gtE53OhRxqCNQaRI8A6pC1L0eD/i+0wSTTETBb0LrPsx++S2A6qQVCrw9TePiPcOcAHe+fo3+d39c3ja89n3n/L212rGbs+rVy+JyaFSxCAJCE7v3OGv/dV/i+JuYvvRBS8++jRnVMWC0/slgx3p9h1XUvLNd06pG3l7OyMRCJkD2qUQJJkzu3IBAqTswYwpy25kUSKjgM4iUkJKGLcdf/VbX6EfHC9erfNTLTJ9N041zH+t/jOiBBGz9Oryasf11Z7CFMzniVV3xbYXdJuOuqro+hFTSr7/wycYpTg4mOUonOAJYgJhBQhIrq9WbG4KqjEyjPlZ+OzFBiUF931isSjoh8jldk8lE9pIdqMjbAZchKqqkVpiUNjBkqTh1fNLnjy9pqo1BzNNoQXbH1xnhH+wXF/egBD8yXc+RfiIHfKE/uTomNMH9/nuD66Jk0dHSI0kx36EGBBa0NQF945attfPmB0usOc7Lp5eA4kgIirdbr63Xtrwpffz1ht+K0m9lcVPE2L/b/woL4uBH3yx5fgNSZAKESxEQXKeWKTJ3igRUqONQaVAsBlEJkqNlIZu1+NcmAjYQNTsup7SwHxeZ1hWDDjrMNLz33/4V3j/K/f4pasrXv70nMe/2FM3FUWSHN85RMbETz49Yz88z7K0StEvZjmWhkA5qzhsa/b7NWcvz7AhkEzgYrVndJ4PP1hQGElTGkRV43xE+EjdtKSiJqQw5cadsrm64vhOzXwxo9ItRan5YrVnUANXacBGz9lqw8ubka+8e0zs19jdGf/q1YqvvHPK0xcDV5drkug4OqqIV9c8evCAFz/9AoKgVjWLRaTVke1NwA4Wc1yzmBsYDfflIQcnCxbLQ2azQ8oayuWcMYAdR8TQ4XYdxyctpjzGdZ6hH5m7SGg9KUrGVc/gRuZFSfSWcbPFBZvjpFAoscbu91k6uGxZ7zr67UgKgRera5KA6+UNTVvSWY8RkqbQmKpgZwXvvv8mL5694vLyhtA7ytoypog0Bq0iKY207ZJaFRgUvQt8+ukZw+g4qA1ynbMXu7HHxSwrXO9HXj7rGfoRVXhmc83XHj+iKDVNrUi7ANHz/GJNitekIbFcLnjn3QcczguKOtEYjZGKvnfY5DHacjjXHC0fYFRJ+PAxu8Hy/MWes9UNV5sp0kAaBpt9aaN3+JS97sMQSN6h5UixzXLPqi6pKwNC4O3A6DwxRYoo6MeRGLMyRsksExciIUWkNpqymtRORqPULcEY2maR2c06YhS4MZPjm6pFpIQI2c4ipUQkBylL1EnZAiOVJgiRicUhAwRtmrzFiuwlLyRHRwdIrRj9gHMuB7JPa67REhlhu9uilZnODIGqKGnqhqIo6G0gdCNawHKxYLsbMWUBfkCRqIymagRNW6EC9L3j/OwMXQruHB/TD47f+v1Psr3H5Cie1fqGqiipypLKFIQYCXakrmt0XWC9xQ+Os9WOstwjRFYM1bWhKHpKc4mUimGEfnBsdj2mVLy82mcpplHYLnCzvmIMI5FEiir7EEOeyu12t1aghI8KMUb0PoN1ilIjC4nQ2daTeQMSIwUH5QEA+22PHztOjheYomA2nxFjZLfNnmslC3abPSOJ0mRIViJig2e13SO2OWKsKAq8t69VIdvdSPCRwpQYo4mTwuOnT14wieiy2i9T4nDe01uFVg5PwAVHchYpc9bivM5F5LxpsNLRVAUgsONIcJ7ejll15yM+JMqqwgiDKvOgQxuBVjk+KatO4nT+qwitmWBKYlL5QV3XpJSoyordTlFKRz1rkVLyk6ern7Us+fn1/+H1MxeLSeaiIJdgihglQphbhRFdD30/cpuZIictlMj/y67GlFHqAkHb1phCopWkLEtAsO86uq7HaKaYDChMjRRZ8hCIBJEjOxSZEqh0DiJH5q6IJPv3tNLZzziRFcuqxBQ6+wm9J1gPSBaLOUYpnHXEkEl2PnqGSdqljSHGKRajKDGmfC2Xq6dMwpT85IMLHB8fok3OJIs+0M7ajJxX6vWmkQvVMBEkYzYYG4PUDjkdun3M5vgQ8uc1OoF1nt6PCC0YxgEpNc55nA0Mtwv+BK0JMeFCBikYnfMcRQoTDOhWYJuDUUOyGJPBLGLK/3Pe5TxAoQkxR3sXOksDx3H/+jsWUk6LfV6smrqkLLJ3z2hDWZQ5ey7GnO+TPGVZ0t7GNaSIUImqzAb8f/OeSQT2nUWaOWPXcXF1iZSglKIoS5TUVGWBN5pCZCps05boQiBE4O79hhgSQ++w1pFqgTYtPoUMy+lyATaMHmefA9n07a3jvHMoo4jBoxEsigJjJFoGjo/mtHXFvKloSsFs2bCcN2z3ueDRRYlWkuAmk72UlKIhKseyOSQQSNGhhaRsekIcUcowny8hWaLzVKVhHAfsBD8xRkPKsSuklAvLZJDFjNhvUQTKuuE/+of/If/64H9gv9qhrlZc/87v8f1+4He/87sZsiNgSIHalPydf+c3+fv/4O9T6Bfsrp9x8cUlJ8slRVFRL1oWeoEMI8k5dpstdw5PkSLlqT9QVQbnLCllMJEQGRSkTAlkuJMgg69MUYPMh5lSS8aUOHt+zt/8t7/Gdtvz0ccv6fbDNAi7BecIXvA5IoGIkigT6/2G81cV81KBVnz0yef4MfuMTa3YDxHpLb2Fti1QGhaLhm7nsH2PEIkgJCKNvHq14dlBxU+7NTf//hFNXXC93Wa5rBZ413K1tcTomVWS4AP/8vd+wtn1lmpxwNHxgsNZxVvHNcu25NmTz4gpYlqNwNCFmj4pvv/0CbUpMcoQyfTHlCKFaiiP4M6v/BFt9Qx1fsj/+L98j+cvbpAUBBGIwEwIQoS333zMNz54F7u54vOfXvLg3Zo//pOn/K//8o8gmWktirnJJFJ+zP88PyD9+R/c/ixlwNjk18k/Tnz8+QUffltyeHqPw6MneDaUSqJkBoQlnxjGgfXKs9lv8DbQj5raKHbdhn5kgp4Jjg8a5nXD6Dvs2COlYN9d5fW6PEKqkZerFYf9EfWDR6itB10w+sQXZ9e4oSMA3cqx3W4xZULNKjZScnB8gg6WwuUYJrFsOZRvEOoFg+1J3RUIgVE13bBjvb7gYn1N10fu3b3Hduh54+03QQrOXl0wm0vuLGuMFkhlsdHgXOTB3ZbHD08pqztErRnDBrFzlFUNbUUqDcPqkgrY+YLrVc/Tl3sCgkfHhjszxS/NTlFa0tSG1bMXFPOWN+saax37ruPq0nK5s8RoGJ9v+eiHL/jsxQUHjaRu5/zwp1d03cDX3z5gdI7lsuHdd97k4ekJN/s93/3+J2zEwLJeMKPE2hF56VksK56fX1FowS9+86vcv38fGy1OFTg78unHP+GPPvqUkzunbDZbGilZzBtmdUtpGly3Y9+P3PgelyzN4pg/+oPvERLotuWyd3z8vc+RdUVTRU4aTaUUcbPF9gPvPDpl6Dv+9Ls/4PMXlwwekswN0/feOuCdx8e8d3IC9wqeVBecXa85u9kQg+HqfEs0AVMICmnYrXrePLnLMHZcXqzouj3b3ZaTewcUQtC0NSjB5dMzbBdwUdDUBrxnsAP7COVC8/d+8eskl+nK29AzDls228B+dHQ7SyE0Jw+XnF1s+MkXF9gxMxY23ZbeO6QqEFHhxkglMuxNkejGkYAiipbBOkK0WBcQSVKoyLAbKGszyfc8QuQ4kMIojChJocB5z+g7QpAgFdG77CWXipA8Moy5CC1mCJUnQdLavGcImfMUjSbaDIVLk0ex0BobR3BZPUCSxJT9bUprhBGElJUhRhs6OyJIzJsKrQzX+x0pZV5E2zSUhWG7s3ibC5hCFwhZsNoOXK46UsqglLapcMNIv19nX2dRY4xh6B1919HOaowuiFHw5PkZiJwZ6bfXeOfQAsrSoLUkrTc5f9QYPn++w9mAtTnSp56VNHWNiJ7xeqTUuRAKIRImuODhvKUwmoRHSMG+G6befh46BO9zZudGcFXoHC8y5ibz4eEyE3ynYUMMkUjMEVTTOWjWzqiEwq/GnKcccmG6H/ZoLTldHnJy55AYAuMw5ixFPQ0zQkCJEucsAU+ICcGe4CMIjXcjZakwumC/m5rbkmn9T7TzllpV9ENP13uU0BhdTXJlS9c5Li/3OJeb9olIIgOgRpfPpSkxnaNzkR1ChjuVZUH0ETeRiVPKha0pTCbZh1xgkno8TE0jSXe2pusGhiFw66+s6jGfR39+/aW8fnaBcCTr9VMikQ+Gt/CXL/2B05QwpSmcl9e01NcTB/LUqu8GhBQEwPl8OkkpYSZqk58KqUyKJBeEMZEpf9m4HmLIBKYw0Z3cRHhMkRizF4IELiSwPkNeUvbWhJCNwsPocCrirCXGiI1xyirMKHAXyJJNwEXHYEOWcwG9y+9fihw/HGNA724pVZ7kY94Qp89BSvVafnpLiZXk/MQgBFWZqZshBRQJXcrJBySRIktTldYIBbNyTpo+y5QSgUzsCiHiXPaAZuprwtoBSKSQF6LgEj5lT5PQgM8hrQDiFmUsJD5GbiNIBJB8zrd8vSimBCHLeuNr8JBldODciNGZzhrDJNlQmhgDzo+THC7DN6QSE3UtTYTdNE2lAtZFRD9CyjQ5IfKE2UWBUgFEJlJGI5FREIeI8pm9WBXTdEWA1gqBRCo9+WsjsQAnPFJkyI+UirLIkSlHk1RjsDY3IGTuQArhaRY1dZm7ytoktIFILvDz1+szmNR7QrAkJVBNmSefpcaNAaM1bVkiS539g8pgVIESglLLXCClgEIglaFQGi/tJH+FGBJxDMgq5wfG4BExsFi2/G3zmHW85uir90i7wPZPv8du53I+aoo4IlVb8o0PP+DkbsO4+hjb70kCmsZgVFYPKCVZtDNKmYhuYHOz4fTuQaZNTveJkjkbKsVELvHyY3pLLIb8Z6XPpv5b9oogExBV8hwf1czmJfvd8KUP8rV68pbzmSfpOZcLOuszuZc8er2VSEanid7iYgTpqcuCg0VFsAk75FiJKLIy4Wa94/Kyot+tGe2Cwijc6Nl7z6tXK4ZywJscXn2zzhCDbgxYF7D7HVpCspZaOMa+ZPCRstD58BUlo02EkA9aw+CIBmSVDyOrqw1tEfH7xKP7n1KUhuHpr/HyfM2us/m9ZzRufgaBN+6f8vWvvMHQ7+gHy9X1iourNefXuXlDSrkwu+3gfWn//Atc4rVa9cvtOzEMI+fnV2gBAcWuizjt6PotWuu8ZqVMi9bGoKQhKcUYyOHNKfDgh/8Jdw4q1ARHKmVBulU/OE+MBWXhCMFxfvaKk9MFpEdUBvQ0yUYmrB0JMTEvK2SskDpipKYfe7hZoVNinwKXNzukUWRFrWawe/w4UhrFbr8m7DpCGIlCI3V8Tao8e/4KUxqStSTnsWUuLoSP7MYRLwSnKsfo2OGaIYGXjjpkbzzCkaJGYSEoht2a/bbj5uqG4EHdfUgsJMeHc4QISONxx0sUAtt3Wb4+Whgj6BxTBQYvC4IuGWNid73DDiNKRPaDxdrEwwcNw2hZb7eE5DlcltSppClawpD3g/V2zX7Yg6iQStHbxM2mox87RimQKaEQ1FpyMK9oKwXOUxXZoxyjpSxA6RrrC3b7HTomun5AKQNjjxsc2/UO1QdcOaB6SSnAdjnSabXbEJLj/v0DZKHZj4G6mSERnC4NbVVR1AXaVMxnNfthZN1bVKEZE4ydowwa3Uq6wXEkEscHM3CB1bbjs6dnbHc9/c5RzRt0pfB+yqitCrRMJB1QAvrLDh8sNo64cWDsEnVbMFvUlFoxs5ahydLEO4sGEfNEzg4e1zt8mLEfHFqXSCTj6ECCRjIOjo0dSFNjM9hIP4z0g4OJedB1e0xREmJiGB0xZMVNjJFE3k99zI3MW0+wDwHvc14fMuf7CiTeBUSwjN4jCdiQCe/KaFLKUx8X8jqshUGG/L2JiYSdt/G8ZijApUDyWdmUBFNcCYw24pXFWZun0ojsqxttblCTsnc65UapdZbkcyEaY6QMuRDMmdYeJZgggoGqztaRW7vRYl6jCoPzkTBOgEGlaes2kzT3e4yAQisqrbN6RxfZ/hMCYRzRWqKEwHmXj44JpNITnE4QYlbCaSVxYeqQifz7qqqwg5v2tBwVsd3nyVxgN62vU8M95Oa/lAqpsyokxI7trp/yZ3PzPTfhI9ooOh8ZQm7uK7IaTt2K0YREkO8HqfMeKIUELSe2hkaIfO4TOscl5c84F3ldlwcJ+X5JkBzWQdw7Yor4kAvrGAW3rAAffF73hULIDESTUyxcIp/FjdLEELIaDZmJ+pGsNBFyysu0BB9IIeIRYP2X30GcdnOZm4fWxomo/vPrL+P1/6pYjClMqPjbbK1cnAmZaaURj9Zm6m4Dk7QzR0EEQEzFQ2Sz6WjaCucyavvWE6i0zrOl2wdJZlx8kPkG1EGghWCMgsE6pFaIKBFRZQQ/AWWmY2vKnaIYIsMYUDp3ztPkzUvA3u6AlPXbArxzFMpkaQeQkkWoPD1zLof3qjw2xYXspzQ6Zx4i4OVNPy3CWbpKFJNXMx9QC60odPYDtnWBUYq60Eghqc0dErcPUKJUEl0ZlJSTll5TFjkkfTFf4p1HGShqQ1kZujEbkr1XkCRSRgbr+ezpy9cTSDt6tv3IGAIBSUgJ2YG1uWsWyZAcrRTjMLz2aQrA2xGkoKlnJATOe7yP2BAyfhzJvu8hddPiKdA6E7PUlNuolcC7/vX3jUgU02SyKIsM/WG6X1KcFr00/RvkdP9AGnM3LftDE6HJ1cW6cxmdiaAqFVoq6rKcCiUJIdLOatACLU0udFPAu4GiMBwfHdM0FffvHqO1ZrAeG2HVOTZ9R/AjrU7UWtFqhRARLca8EWmT8/VKQXCBoEtS0mgZOb7X0lQzkmxgs6FRioP5HC8F2lS5cIqCQiQUkWHYo1SaJuQ1ShtGOU6d0UAKgb5ztIXCmAJLmKbb8B+792G5Qf7KW3T7lv47zwm9RmuNCJ4QI8285Ru/8HXqWWT/0StcvycVIm/YMk+iEYnFsuV00bC7uuTy4oJ794/QWuFTNuMrrXHBZchNzNIcP9GQQ8oEvhQjUt7S0hKEiFTZhG+7HcfLgvmi4tWrzYTfnjq2KfEfxH+IAP5n9U/QIVJIBVqzcwNPL244OJjRGEnXd/TB06SWKBNFnZDRk6LhzmFJv3fcbGVueJEbUNerG87PJGO/wdp7DFrRdZ790HO9W3EuDcdvHmO95fLyhrZuWTQzTk80636DGHbYZDmXls21ZHF6jNKaYXBYkXDa43xPIRS7MRCi4Fvzf8Gu7/n8sxccLub0LvJeN2CUIPSBzc7ivCeJhCTmQozMt33j4Qkfvv8m59c/wAfLxeUlq82Wzmb5r5gUDwnxeo37i12TsD/G/1uBOXjHZ0+eUpcVo4Wuj1R6pN9aCmM4XNY0jUGXBYeHc6TSbHee1WbIz2FybNYdtVb4unqtsjAmA7liEgSrCdWAG0f+q+9+j/mPv8f+330f6fdoke+pptYMm4T0nsN5SVVkFUAScLXbc7PaUCrNODhW+47lwQJdQFWvGKyHMJJKjS/3DJtEUVScPLwLKaBioreOn/zwx/nZf3CCDJGd9dQkVIjsdo4hwEw4vNjx6uWa1bbHa8O8Mhy2M6LxBBUYg6fRcz777AVn12s+/fwCvOTrj/86pjnhSFd4v2Mc1zQHJ6T1wOfPXtDZAV2WhGLO/KRhb6Gq5hwvlvjFAX6z5+z5C+6dzJi1Jd5HUpC8985Drq63XNxkH+iHHzwCn/PiPn92wWYzcL66Rt5Yvvr4K1R1wflFz9n5hmG7ZgwDs1nL6WLO49MDHp4umDUtr64uGfqRYdhjXUdTFiznh8QoKVIGWCzrApkku+2W2I/QW+zaEgqBrBIiBsYBjk9OubpZMZ9XfOPDt3g/SLyH0+M5ySeuL29wweNFjljJgd8KKRTaCEYh6bq8F8VW01lPN3acnpwik2TbOX7w6ee8KK959uIGOaupZhW//O4pR/crJFXOtKsVZYKr8x67tby6WnHxas31+Y6vvf0GJ3daZDIUUiOaRMRidx0ijjx465A0Btxm5KA5pLeeeb1AkhjsBqkETXHI5VXH2lt0JWgbiYma9Wpk23uigFfXV5ydXxLRpCDZbR39MDKMlt2QCeA+WrxPdL1HSCiKvB66kAsvYypUM4OUcF1HSB27bszZ1AKklqAkQZppMiTwXhKURMTEiJ32QyDleBORciMujg7rLNJEZFBoaXJwfZ+bztF7EBGbEm4cGQcLMis9ZGGwCYLLRFWlCoIb8c7T9zuULCY4ViZxhpCVTfPZLMNvprX/9PSQpm14+eqcrMstKYuGw8WClAKh6yhkLhTloqVynqJpSM7Rr9f44NCmpmhqtvt9bhgbTVPPJuV9Ivgs/4zo3FycGpuUJcsg956RAAAgAElEQVS6RQtLJHMxnM+Fl9KS9abLcs6imAimTA1CkBOgsB92WDfSNA0pJrqux3mPMiURMU0mM5xwMWsQgEl5ulxVNYlEWRqapkSIHFkihEIbUDo3rr1zJClJRLwLeBcBwXrTQRKUVQbPOG/xsWO93mKUQmn9+jwpFYgk0CHDZoqmhBjwPp/ZhBCE4IhaUpcl3rt8jpYqS3Wnwt8Ynenq3k1T1EASEm89LkbqqsrTXqFyoztGBjuS/vyG8/PrL831MxeLLjma+hAk+DAQwpSbFhX5eQqIKmKHW69dPvTZKV9FqS+DUYVS6FJn03WMpBBIU7mZUtZH34JuhjFR+8i7dw9oZxWr3Y71ds/dg2Ok1gzBQnQQPc5JvM/j/uxIE6QUcc5OvgD52pt3K3WE3CAyWmcJaIwonbPvIEvG9/s9RifKRYOSOezYeYuILmf1SU0i4VOid47RBlwgdx2FRJFlmCGCC5796LHWcb7KRVAmWyV+70fPCT7gJ3lDQrzOi0LkwNWUMhRmEmtipM5OyOj+L4+dENBWYgqNzQAhpRRaZbBJjpISSKlJKufjSJkPACmN+fMXOU/s1gjtTAExMU5NgxRzsGtjNMHIaRPgdaZRXmx4HVdyO+0ojYRk8vRKSHSp6Lse20WkDJPpOcuThVAMLr4elDg75jBs9dqIhRACayH4kZjyhKMqG16+2qK1QMgN3nu8T9mIbV2OWdEapRVSqvz6UgHnOGupdEnbFNy7e4wuDaopKeqChycHfPXunNYI/DBwebPGxwNSDOzWHatJAlxVBq0UXe/pR8cnn2+YG81uHFClIlmPt4HDR4948OCQUiWidOi6ZFk/YDd6jpYGazv2NxdUpuXwzh32+xt8ilTzBXoBafSossD8wscoU8P2fczym+zjJ5Rij1BbbFGRhMPEhE+Cv/Ebf4u/9te/zQfvn8PFH/IHv/8JL15cEnVgcSARSVPIwLwuWTYFhYJvfv2rXK/WfPLRJxweHvDg0b1JkmzRWiOTYrBDPsDKbMgnCaTIQdx2klbHeNsIEMzqhidPL7hzWPHGgwWfP7lCOM8YUu44C8Gvpb+JQPJP+e8yrrs94N/7u3+bD755h/XFGQ2Kfh/4/MklQkSeXwqur695tbqgEIY2Rrabc3TyIH2W18Ts+d1uLqibR/zqt79GcJ7d0y2kiHeJtYv0yrF9fo4pFDZCf7Ph7NVl9khrw04Z+mFL7yzLWUn/0TNmpaZsACknj3Pg13/llykrh3eWdvxjFqdHXIsjqoVBSRBKsessP/nsnKFf53DkBMWUatEFuPfglPv3Wpy95PmrHd/69of803/+L7i8ugThpicBUpych5mjDtPE4v/pyrPb219/uYjEkPizP/ssr91JsTQVqg0cnsDBcsGD+6eUxhCToveOiAN6UhywfWTWllzvPClCWRmCsJhUUKh8YIxJ4uiw/ZyX1xbnEyGAj2BU9oa5zvLT7/yUrbUUTUV7nBUVe9ej6ppvfvAtnE/UyjM3nhDg0y+uWLYFyyoRkqDrwbvAs/Nrnp+tuL7ZcPh8wXtffUypLevdyBevHD4MfPJqiy4lR2ZBoT1FIXFS8/mzM9a7E1Ic2a5vSClxdHjCtXVc2yeUGLQqEeUBO7fma9/+Jb5uan49zRERwvmP+PTPnvBn11eQFAKDX2yQrkPFxLwqUcHw5IsVJ/aSo1nDR9/9IVeDoG+POD0q0bWh23nCCEfLQ0SR+Ge/91P6vqcbe6qi4O7BnN/4+lt0Y0c80BwfP2T5oKVUmsXJkv1uYLzpaYqSdz94FyEixITtRzbO8+qFw4cz3v/qA9qmwjrH2I+MznJ58ZIhWNr2kMd3HgEWFwdmTYlRisFOYd9S4VzCuYjSJXWp6cc9L8/W/NEfP2M/WBYLw+9drFjMGr7xwTu08wNeXZ6hpOTewwUP3z7iejuw3jiOj2ZsVzu22w3b/TX3HrbEUPDi1TUHs5YP33/Mt37xPexgefPhnfw8hEi33WGl4/DgDsNuYNPtCSJx8mszhn3P5fmK06MDHj045d7hkmcvX/Hk6hkSwZuHR5zMTzgbN/QJbj66QBWaoq14+WxLaxLGVBSlplosmFctOkJVKe5Gi7WWfj/gBksc9oSxAyF4tKx59+Q9pFZ0Y09i8gNWDSJodvuOs5s1Y4zYqOmGHUM35qiHSjOfFcyqgqKoGVzPdr9DmYbrVwNKRtplwxg8F+cbgstrSFELpMpTp/XNjs4bUgr44LLfLIWczZwcUQRQsN5Pyqa0ngYAcqLW5gJKCp8n/5D9k9Hh9uvpvGeQSqOLRCE1oBAy/xu8ixwslxidrS9Kt5AS2802n5ekxPae68stow0kStDQ+54nrzbURUnVLBh8YLvKWczOOeI+Kw+UKKjqgrIUOeJiPyKQ1EXDdrvCRYd3EYlGTKAXpHjNSYg+cH29ot9vEVpOZ1dFu5gx2pFGV6SUGMeBEHKWoxT5LIFQQMRogRIaPw4IBMumQRmDDzbnCyaNkQ0Z/Jgnhz7CECPr1ZrgXSa11gWk3MzyLoLMURRK5zNnGses6JgyDoWApm1AJDa7G8rKcPrwDqUp2WzWE9MikVLOOt1vx/w6wROHgbi5QSCYtW3OlxYSZ8G7BFEiZY1zAz50rzeKqqpyjZ/g9PgUbXKOprMD0Y95yFGWCAT7ricPjSRSLpFC8OLVzV9oj/r59f/v9TMXi8eHc5bLhpQS1ytHtwMlzetQzZRyMKqu9SRhExOgxn05sk9ZfpnNxJNEVZILSyb99CRdu/UsIgTeKM76gdIHht4y9IHRrzO0RuaOeAw5Jw8EKeWsPJU1sVgfETHliUy69UOmadSeb1yfxx65SLKgjJokcUBSWT7h4jTlzHKGos7FoHMW7/MmmZJAa0VRGrQyGQyD48s8sykzMlVomT16kD1p87bOMi4ypGXfDVibpSu3huG+7yZZQi7I26ZCSYkbLD6EqZDSGaIjEs57Nl2X31/itaczhIBPIFJExvw5vCbQ5i+UkBJhGKfv5rYAVHRdn4m25CDWmHKnKNx6n0iv9fxKyNfadzF5AqSUiOhe/1k55FiVGLIEQ6kcHOucJSJQSpNVqylDaqZgWTH5VyEfDGLIstn8V3f4MIAsMLJEyAJdRkqZMGZ2y/TIE+YYaZsmg1W8R8qWw9kSnzy6FCACcb9n3O94sb1h9Rwqo1i2LSJFmplBCTKgSClUzJ+v1orD4xnzCNv9mqoqGLaSwVmIEm0Mu77j5VkGBrnBgZS8865gGBzXVx6RIoURWLWDJLLpnUgoIkkE3NijiyXqb/0Bsqzxwx0+O9jixYb3xIwYd9jLz9DRoatI8oZ3336bD999THrxlIjk1XqP84JZ1SIFRCFom4ZF2zCvCsoCBmdp2pqFW+D8yBdPnvDwjccAjC68fl611viUwU6vjfcpT5hj8nAbCjzdYy6Bloo3Hh7z+K0dr56tCP2QpbbpVrqeiDIDGEShODmc887JAWe7K7SN6DsN95b36Ls9j98SeH+Aje+hEcjR0nUDv/MnP+bit/+UfZebDUkmNBGZIpcX59z9L69wL254WTXEEDApokTOj00+EfosaU8yEaKn1DrLzgrFYdFmX+TlmhBgu8my0ELXGC353kefE0afCXiA0hdUamTe5GiHXxoGSm348U9ekpBoshxMCBBagMv5azebLT/+3HF92XF5ueXzJ9dcr/pJ556LrwxAnW7u+LP5Qf5T1Uy/um1U5SU8kTAqUFWRemaQZcT6PAlBFDkWKCYKJZk3JZeXG5LwbH/hv+B5U/BB+icYKZFRkVuKmcCHFNx0G3Zjl9d+IrvNjmgt42qBdYFUKA7aJUIJVqsN+MRqNxI3lso8o60LOmd55QZUoYimZLvvWNYzmrJk1hhiiIxuQEvP8UGFLlpkUoSgWB4e8GbIAK66VhSVxNtsFCgKxf07LffvVLhRMDrN3aMZUhVEYUjCc30pKLWiLQraQ836OjLenCOkYXAlKUqUG+lv9mxtIERHSD1L1WEDFEIxMnn4a4mRktEljg7nHOma6ugead6wO1hS2T2FUjjToLVnMXtMDI6b1S4Dk2JkpGdrd1yd73BRUQhJVJ633zzl/kGLflOiyHTEVxcbXAxEmZBG0seO7dDx5NkZTVUQgGH0NLXm+PAYbfJ+lGLPMOR9bZCBHs/Q23wQROCsJyY4OjBENxC6Ad8PNJWmnhUsDjSVdDRFQbQjm6sRnxLXN2vOr7fZEy80SMOw64g+vI7TSiLh+4B1jiRr0JHR9/Ru5IuzM5ZNRaE0W9sThSKsbjAisWhMloaaFu8aFlWNMgZTGupKYozg6PQEgaCRFRrJy+2ah3fmfPXth0SpiEpx9fyKFC3bzZ5+dHmaXBZoU7LZ7XNzNstxqIqCIAWmLDNxMkC0gUrlLLq2LZjPK5rSUCjDbAY+OHa9Y/5/svcmsbqt6X3X721W97W7Oefsc87t761bnasqMsYuC6ezicKEKEFIUcQEiSkjpggxhBFISBkxAoRHSMSWSESIQYYkBMdN7HJV+Vbduv1pd/t1q3tbBs/a+5SjRNiFCB7Uurq65+y797fX3t9a73qf5/n/f/+Tmrpac/7iGpTm6GjGrDJUxvLs8sDNLnB+3qH0wNe++i6bzYbrm5Z+8PSjKHzmzZx5MxdQHbBazghObAOjd2JXiRE9SQtDErp5TJMFRcmfU5DnfDc4sXegcD7ivcc5hy0KQN/lXRulGDsnsRBkyrJAKZFsRjVSFqJAM0q8CUVVc9tT7kcna4/RAvnTWmw0WlQW+07WWMUERUVNNiUQPEYiYyQH25SypwoZpQrKwkoUEdN+yAqkLU1SXIkGUpRNjQ8RcR5JLFbwkX4UuqeelHBFYYUXEdJkGwJbFqAthZE8aSnS5DylsMskpqzEqIBEnkj/1awmBEsGQpwgfMZIPFxIpGmal7NEtGkjy3+erED5IPR+5yDGxLVqKXVP2x6ISX7umGWPqXUx5ZRqdNZC1wcishcCAScllRl8B0hhnknCmMiZ3aGlKAoKa4mdRCXpyfuZVZrk+a0o7hB7SghR7Ek/paH+mT1+4mKxbSNtdwOZafQ8baTgbtMuUzs37VMmDrNKiI5AViqFmqIS7PT5k1du2kAqqwhh2pgpyCrghkgcoDVRpBGFYbkoKYqC9epkMo+P7NuW6F95o0T6KtQ9rRTJp9sTxlgj06YYp5JFumu34dY+ZMlDjFFupghdDNwSV1GKHKYJCAC3C06aqFTiCTCT5r8oBeIDUz6d4g4SURYlZBi1Q+npe6BYNiVm0VBYy/HxEVVVcGhb2iGw22/xbiR6R1KamGB0AVNkmsoIvStL5p1WEvw6jhJNktRUsJLR2mCtIsbAnRQNKeoUIjGWqaj4oUKO6Jyl6FNAjqicsVoRneO2vTWrKmxREJ28BzKplGspxwR3WviMIYM2U2EhH9PTtaI1pOTl+5MxRuODLIZ2IsXmnBlcJkbxH+qgGMaewY0oBhRSlBfWEFIQf4BR09eLnzGE/auF3lj2rSPiyRq0MhTZoFJEGzCFniZHFqPh3vE1MUScc5SF4Xg5J3iP0pb79445Xs9597V7lEbzta+/K8qf3qNTIpeanKP4BhykMdFkWFQNu3Ekq0zVgDFC2JN7CfzBkb0n6ZGhPzD6RO6u8V/8Pv/pW1tUdcV/N3yDoe25/uADyjxSlVA1K775/gk/937B7uU5l3vNB08vCKnkaDVDZ0NRzHj39YesmoJZnUQ1YDIKw9nZPdq+Zbfb8v3vfZ/j43usj4/uvB2S7WemB6O5KxhvPY22LMkJvJeGjioMo4dvvP8QrRW/0Q70bS/dzkmkogCTJGdzdJGqKHl0eoo9bBh9pKpmlNbSh4D3HmMstR4xOQEFWSne/9bbfHq+4Xd/94MpR1WTSJw8fptf+ctfRfst49BRmZIheFrnCTHznR+85Pxyjx/PqeuKgRn7tsfvRiJ7xhjwLvHixtKNkbP1nGxGclJ0o2ys9j4yNxqb4WLxS2hdcLT5TdqdIXoJXVYofu97n4jPU0kTLOkAMTObzzg9mvPDDz7htzdbvv61t/md733IH354TTcMQqSe5GS3U8EMokT4E63uU87ldHzEtKhNza2UbzNaNU3jKUooqxWomjHoqVZNaCVgn9JYmGVWiwpbzXAPvixw1m2kKkvpHWbJZzRJaJI/+vRHhFBgbYHVhsNmy8svLmh/8B71rGKxkBiHruvJgyYMjiJoTGEY3Y7762MGH/nhkytuhsCyXjCvpNHk/Q6L4mS94uy9Nznd7xj6jpubnnZ/YAyRZj5ydFIydHB13WFHS9e28pwzmq51WKPogmNWlzw8OWI+q0nacn55wztf+hLzWUlRKJ598jHBFBTBkpInDBt8godnx/zs8bviOyoUqpLJrbKWqoj0h57PPnrOLHuMntP2jrop8N5z/eRzuhRYred8+StvsagLPvniOS8vNtx88Zw3H55yTyfGMtAs52AN83LB6zPofeJi7OlS5Lvf+YjoFIduwKURYzRVYVmuZ7zx+B7r9ZrNbsPpsmC2rBkGx4efPmVzGGnKipvDgdFHVvMl83nmeL7Cu0DbDpAy906WnN5fMWskwzV4ee64MXDvwT0ePXrIo4cPZCNsMuPDM6wymCwWkAeLJTc3G15eXJN8oiktVW242h7wLpBDxOeMT6BzT4xw6K5wIbDbO0xl+dHnz7h3dMR6uaCoFToq2v1IjI6H99ccrxcYpbBFQqtMNziGPnD/eMXRYsaLz14SssJpw34cuH5yw3xd8fajMxZNRdNY/ODRyktUVFNytjqiKkqSsZyerii1QqWId45iVkvhoAtSDLy4OKcbBl5/6x7BjaTRiaJhHFDGY63hnTeP0NpwtFpgtSK/dYytChIGNwS0yjx8sGaMiX3rcIeek+MFUZ/w7GKLc4llU1DPLVVlWc1WKAzd2In/8dCRssAKM5kcPEVZ0o2eJ88u6IeR05NjxtExjlGKolL8h7vOk7OAXXyQZk9poSoq2naQCJCxpx8GnJMmvTWWopqx34+yp1LQtQOD87RuIKfE6DI+RtmTJVH69H0nU78kzAV/2wBTSSZYRUlhpMKsrKEphZyekQaHGz15WsM6P5BintRUMlSIU0Z3XTcE59FWT8VxpB16YsxYU4rSLCUUCquhtJaqLEgp4tzIvJYGW4wSPeSn/dToRaVltJUVNkFOQoYXX6kRxU0MWKDUBlMY/JQXThKlRIxi40BJ412UXQmrAqTpZ0zin3RhRGkj9P2Y2FxvCTGRk7obOtySVuczKxFtLsnzQ08xdm6UIvT2gaLA3alUpudCDFNmeJBpqVLUZXGXw5umvbUx+s5vW9hC1EUxU1cFRfHTYvHP6vETF4v96KRzksEaJRk/TEPDu8GhgGNkYpdQ02hfjszUQJomjPJvuu0GTYcUnSATQgnBJUvwrTGWshBITNNUMmnxQbxRSTbcMo2QQkR8lkkKCK3RVt01y/VURBktnysyThnxa2UJUeITJB7g1Unnu6lnJnoJoS7M5KWbClFAbs5pIRL4yy0YR02SLpnmGZ0IAdTgGQbpynkv07PFfEYxUbJ8EhKY8x4XoOs93geZaKLxITMO4ikcY8Qa6RyDJoYsHrq7aIxpEz69gbfvkVJCOLsF2ij9x29ko/SraezUIFAZIhGmaJHbt1gbTWEMGNmwai1CN6U0MU0kxaxg8jWmlO4aBEyiZKMV2ihyiHdvQVkVMuVMcTJgm9v/hZokILLP1ZR2ItdyO/GEPEGMbDToUqGsvEbwAjiyxuCV5AspnadeqxS3WLn2qtKitCZOksphFBIcgCoU1jKBmjQqBaIf0EgBaZSlLI1MRH1AV1a8C4CqZToPHqUKjCohB1QpESqjlmsq5yQLNQata3ICffmQHHvc4x05JpTVwIwQEtebnpQjJmuSSlxdveDTT0s25zdcXXv6MQmox4JWBYuy5nhZM6s0Wss00KAm4JSmqksWec711Y627SiaGUUp9GGRLE9HyhMQSUmkTvqxEnDy1lWlJfjMejnj8dma5aKa3v/8Y3pIROqeFcE5hm7AD4EYMz5ltPcUymBNRXKgYyT5Qa5VI3Los3tH3DteURuLz4mUNQnoAzw6O+NscUTubyjKij5kNqNjjIFczLi66RhujikLS5dh3zrGTcvgWlo3crN3hJRZp8TDZUPSM5xLbK4czkV8HwiVSJ4uHv8tkYhf/aYE3Huh3emYuN4LOCGpKZsUkXQv5hWl1fTdQNuOjH7k/OWOfpQmirq9Tafp+5+wQvznjn/ZF/3Yx5W8tmxU1PRfmRwbboeb0j3OKFaLmrI2XBgtdNZ8u6ZEVJa1M+WMGyO2nPPuo9cotlvprlcGbUqM8hSF0PZIAvxZLef0WhOGgDKarvNc3xzIMRGR3LjSZerC8PnzC9w4UhtLe3CsjGLc7ghugJgoS0MIiRQiRVHQK3hxcwBrmFlNXRVUZcHgFWHMHHzE58DR6KgrS12W1AZKncmIvzlHjQuBJy83kAJBeVShGUfHcr2gsQW60Jja8OTiQGXkdzqGQCLRlLJhLJVFBzi0PZc3O2aNwQbLZtfSdYZ+u2d307LfbFlU4n0PKWMqS6RGRcV6MWOBJvuKZBSbFzd0XeBm10kgtzWs6oJZXeBCJOsECZqqlIIviye/Li3OB15ctLSj5+xEgylYFImulwkbCWZVST0fSAl8GHFupLKeYfA065UUW4Uhhcg4hrtmrChUAjonysIwmxWQhEJdVeLx2ueOPgW8C/S9NEXbzjMGmdAMTprS2oq1ImqFRRGTkNM7nzg4TxMiOoGJiaYR3c9IZkiJIcQpPkvjUqYdHMoobFFwsz1w2LcUVlPPF5SlprI1trDcv78SyWMI2MJiUiYMnrbXqLJCW01tLSTN4GYUZaYpLUkrhhglv1Rr5k2JNlbiZzRYrSAnTKGnJntBaQQC11QFCVjPZwyLmuRGbFPz2sMVKkuAvS3AFopF06CUpe004zgyZpkAm6ImxUx76FFlQcyZRSOB7pU1zKo50YtKpmxKsoLVGDBa9jX9IB7rB/cXNEXF9fWO0Tuu9obrnSEGhVKinCrLhlXjpmc3jEPN6AKbfiD4gI8wjp7RBbyXxn1wDrTCWFlbTJSgeaVFwSXqsXjXtM85i38vJdkr+TDtUwUQJOabNI0jExoNJuNGOa/CCOHfTbRPrWTab7SeINEZa8RiInso2dfaUqwsCVnXss93q+ct5DFN+8c0bXRyygI4RPZLWhZXog8Sb8akxpvAMOQsgwWl8DmgtaR4S76WNO+zkn2UVvpWuCVDGfHZkKc9qMB4JvVOTHevkVMi36r1kjgZtNXT1BVQaoqAk69NOaO07BlzyneWpZwSt3A6sky09bRnurXSy8zlp8Xin9XjJy4WlY2UhVyAKt1GYyAXI4kYmQolmTfmrJiSsOXrVZ600pkUPCm7V1O626niHysu1d3rmMLg8jhFd5SkoLi67uj6Ea220t1IQi8tyhIf+jvPXYhT/IRCTvq2wp2+gzFi4pWPT39WETWRLUtjxYeVJ1kJ3Elqi2XFLV2VqdBKOZOwlFUx+ezk5wgxopUQvRRCKrXGTAuemrx2Ei8SUyCnTNv109fB5eU1RmuOj5csZgUniwKjaxbLOaUVD6Ugi6VoU1pM2VHBF08vBEKSBBij1UTBkqSh6efLdx2h22mCRgnZKsuCV9xu4NN0jlqKptEnvAvTRSGvG7qeQQs22hiDtXLpaaPF6+hEJpdzmmBG4Y6iKXLeRAwZpX5MDgx4LxmWIQExktPkDTNq8pzKptQoRVXXsthpgQa5MVCXpYBZtJLGwzQhvc16ur3+5k1BaTWFFuJXwEwktUxhBPDU1JKJWFY1ep5ZzSoxpc9LmqoQqWKK+Bi5ON+SdWZsFbrMWCMo8LKs0FaKaG0KjNGU5RzFSNWAwpCyyLON8sScyCkQlYNSoSkwhWL+P/4NKRTe/+8Jh47i+DHEn2dof8THF5e4IWJtST9u+NW/8/f5X/7XI04XUJWK0wdLSltQasVy3vCl14+4t1aTsqcWQp0f8GR616KAqql46733OH95zRefP6WZNzx6fEZVVbS9QxtDnO6NlBRZiW83BAnPkY52YlXWbPYDx8sl777+gK+//4iPPn6J84FXj5FMQGiNYejZXO64uRg4XO4wOqCaBmszlW2IoaXUmUBBTKBGj/eO+sExP/P+u7x8csV3fvgJRhmi8nz3u3/E9eW3+C9++QW+2/Nf/tYZ87KmqhMuRd77N96msJIXKzRhyNrSx8zQ9fRj5NorxhzBdVTBMXrLrg9cvrwm9AMfPbsEI6TjF3pGSIk333sL37dcnm+5fFqjdObyesQoaaqknIhT4XX/aCn5pSrTLGu+98NP+ez5DSk4VJZiTTo/U5Nskv3Ln/80i7ysVf9hEun53zY10nyR6XcMmaHXlBU4P1DmRCoMSSWMKol6+r5KE5PlaKExFiFJagVYUY2QJWxcwU3X8fzZnr/+7/1N1rOG8nt/QIye1jma9YJ790oWqwUA7WYvMJ1GszQNzgbGELm43PDki3OBaVULbFVTz+Revbi+pLYFdbVgs91x+HTPZjvgRs/ZyYqHZydklRn6kd12oB+EGOgDPD4+4eRoQdMUbLuOQx9InSWMmS440gHSxlHXBV98+APcFDF0slzz4sUVn37xnFkz4+H9E+rK8txd8+Tza9o4oLLwJHebG46Pj9i1He3oaeqGh0dzhq5nMVswmxUkW3OiLD/7/iO8j3zw2XMO3cjZ6Yq333kTpROm0gyjIgyey82BpvAoA8tZTVMXPNYzclY0OuDuw+v5njTCxsSzl1dcbTs+u/gR+3GkUZaqKpnNLjFasZ6VPD6dM8SEMnNCTDy8X3J2VBG8wZzOSQ/vMQZP61rGriO6SDsOtP3ArJTr6fzqhiuVKAGjDGPOxOGALrSacOcAACAASURBVArQJWPf0iz3RBe52XbUZcm8tDifhdqiNLa0HDWWeakISrH1UCzWnDSWoyoTlOKXv/0es1mNNpZu22KtweY8gaMUWRs2PkEMLKuSGnBKcbKsMEPPN7/yJXShuekGLjct1VuJt958zIuu53LTc7kZ+QtfOqGxiqubAR88Y3egC5BNlritMbPfD1xc77G14ezeguZoRs6wqGpU0rx4ek1MEVsYqsLQ1A1NU1MYhVbSFPTJ0Q0j3U0AOvk4CVOUNFYKiT5Iozp5B9uRZtZgS0PXHej2LUVRcmFF+lhaS86Km+2BsrA0TUQpjU+KsXW4cWS5mrM+XrHddcxXRyQv8J3OO8q64ex4ysFOsNWRze5AbUrW85pZsWL0gaapWTaJlMepcS8N4AfHFX50aKNZLk5RRtO3XvYq2bDdt7RtPzXBFN7JpLWpyilIPktWs7L4GCRexMmQYn+QSI9xTPR9T04aq0u6cZiIrlkmeSGSnRd5Z12gtGbXThEfLsj6GyN102CVpiylQBsmZU9MEefFk6q1omlqQnQSyZYS1hZSxAHzWqxb3jtyjOjCSjMjJaILxAxFabDKTNJUUd34kCdKvNi67NQgL0tDDFGskcYQkp72TpJ7qHU5LeWasfdkMra0lJXG+0hKMg1ECUV1HJM0Q6pCYDaTMo6cyTETk0KlqQmpgPSKVB7D1NS3dtqrJ8aJWSJWNDPF14l0WKlbWKE0DffdgB39n+IB9dPjX+XxExeLsjcPd3IkqbcmCt9Ua6WUhZyV4W6SM01C4FbZpESDzqvYB61v/U0JZQQyo6YOSApSXBqjGUZP2wscI4eAUpH3v/Q6PiSGcWDoHYvVkt1uL6HyWYncSRjqjC6KDODu3PIUMq5kbK8UYXCYwkwdD4WO0n3KSaacZpqyhBTJbpLLTjdfioqQE3H6ebV6VTwJ5RSRNU3d9RgDVhfMF0tR6U5T1VlTTFM/eZ26tjRlSV0WvPHGI6oS4ii+z6P1mvmsoiwzRsv5WWNRU6HsEvz271uenl/y7GIHSjpkLghNtKmKO1qX+rGiTCvxFhmjp2mFLKC307ycEyF6REEpC1vMtx4EPUGP9OTZFJKpUuoOUBMnIlZOkeQCTP5Khaas7eRdSEzuxOk6gu22nSa00yRUTZ5KK7IV+RluibJKps9RTX5LQfFDuvM93npYjS0mKYdseEubqcuS1XI5xbKMsngXCl1EjDY0fQUxs54rrFVsD3uMUqzmCyBRlpaj9YKqMBzVivm85mQ2EeeU+FmbpmDeSC7jOI6MvicOhqwj0UpsSvSSczQOB7yT7l1UGYxmRoOpS2yeCv9xhx9bSt4g9gPj5iVjt8emUjp5WfPk+QUXV1u+/ZVHrBZzqrLGojBRsZgXnB6XoAI5ie9ERdmcZB0Yhyj+1wDGrlgfHXN+cUnbHjg5OeLk3lKKRaXJRoCYKUWykoaENGykc5kV2Cyd89F5VFY8PjvBFIY8/f127Qlazi9GiKogVQsoK47nC8pmSa4KjI0kH7CLGVVRSuBy3+Ndz6wpeefLb/LOs3O+++HHFDoRMng3EBEAQ0iZm72jqRU6BMZhIO4CMQTS41MKrSmVQWvLoilZ1aBXM947XpIqoQir1uOLOYkCfCDHkRejQ6eS5ALf/3v3yDnz137p59ltB77/vY/4Z7/2mKurHZrPUVmjszQtghG5UqMtwTla3xPI3Fw6uj5jM0Q0OWsy4ZVmaJrO343c/5THX83yAP/bubqb7koJO3li8tREmqTiZNko3Mb8KKVEWo7ituIXRUXGqkRIioREfCSXiE7zb//1X+Af/Nr/hhsGjNF854MnFMrxb/1Hv8usqtn8/b9JrxRhCHz02RPWZc3j9TGrRY2tEm1n6Q6ey33LAJgx40rDt77xLiZnVNA09YxHr61oD47Ll1uePH3Jzc2eQ9C8fLElDQOzuuS1kyNUUVLg2VxfcBkC133P4CJLbViuVhgi11c3fPp5y/rBI7LqwGVyyNzsOi4vO776+gn3Hzygma0Zx4GiLPnk85d88Owl5JJSzfmlrx2xPFrjUs9hiFzddLSjI7vA8f3Ae19+nbquWKuaebVgUC2PjirGheX+a/d44/XX0EXFdnvD+cWevhgZrWepNLauCApuDh276xuCS+yHDZECrWoMCh8Snz4753q75+mLPf2ohKY5PSfmteUb797j8cMj7h2tqIqMiyPDmFC5YfQbjo6PWVYP6PqB4arDKsvJas49u2LbHThsW+qyYV4vCGEkZ48pLMfNHOUqxhA5uEhZrVmtG6KLPL/sp83tgRAdL296alMyKw1n92ccvXbEh0+f8drRI3J9D4PjVG05bma4HCiUUD9rbVjN19x0L1mva4Yuc77r+OCqxyjFfWuEpOwd3/r629QuoFLJ3geudwfS2NM0DftDy8lrZ9T3DUedoq43lKOnjJl+TLzYben2Aw8fP8BYh0HTOcfVbkvuMvfur1guj+j6ke35Nde7nk0/MI6O+WJOUzekbc9hf8lqbvjW1x/x4N59xj5w+OKcolKMg+MPvvcZz59vKJcNf/7n3uZ01TBvSlJW3Azi9R6GTGh7hsMBkxK9iWz7kZwT7775kIcPTvGxJ4weGxPrVcPbP/MYS2bfDew7R0Zzc7MnJPjsacvu0BJM5shk5iendLs9wXuWs4qjo9cIww2Vzrz7zjuElLjZbMke+rBjdIlxjBx6L8qWDcSocAF0DOQQqLRldAFcj0kj7739BqXVlDrLZM8WKGWxhWHoW+qmYRhGumHAlHO6wfHiXBrqyUsxfLUfcDFyc9gxOlFhGTPDDYGoB6xVVE0jfsysGZ2bADnS1E8u4bQA87TWjM7jU6LvO0KUPYa1hmzsBBITm0yOmRiEKRFSFj5ElIlkaYScn1IiTOyLGKV5VlWl5DkGRy7zFCknU/dxossegsMWlrqoQKmJYj8pdpSZoIIG0KCFUG4MVJXGKE0/ekKYsj2B2WI20UmFP5KiZxxH6mkarpBp4621ME6TRybLlVjSZC93u2+3VlNYKz3BGETJpzLeB5maGyPUe5X46Vzxz+7xExeLjS5vB+i3VpYfe0n5izFMkIP0Cj6SJc/mTjKoBHmutIwnjVakINICY0tyTOSYUUY2GtqoKXNO+uazUgLZV+s1o3M8e36NylnkBBo2V9spH1CkskVViPnaFhQ2342+RQJ7e556IoFGZnXzY9JFAC3eST3hjkMQDX3OuFuoh5EMxJCd3AQ5E/2tlE6TMjSzuWTURAlGz8hU0QHnNzsUMC8buqHHhUmDjhSxCXBhklH83qdkH2QamaJINa0BrSeoTKasSorC0ncdxhQsmhlWK0zWqOwxRrL8YpacSZVhMVtwl5R3u89UMtkKQXyUpq4wk+SgKisaLcVV17ZEFWlsPZ11pqkbbh1UaTovYzTz4wUxiG+wLCvqukJpTXCesipJKbPd7RinAFwf0+QPlM2qyou7yWmIga7rpilyIR22lEV3P8kmIVOZ6Z3UWt47/So76PZtvp14xygZiEOfGIfA+aWQ4G6tEuLplHPxSZGUpSpKYniVlXUnM1a38ipRl86bBqsUegp5X84q3nvnESfHC6G2IoCiyigKqzk9mVOVJbNZRWEt230vngijMVVFagdu3IApNI98RGnNIReMPlPcfMYP/vdf5w8/PGd3gOWiIMUdzgUerVY8PFlz/+Ep88qiiCyXC05WC372m/d4uJhDkAxUNeVeub5D54w1NXrK/TsMW1CZ97/yNoddyw8++JD58pyv/cy38KEn+0G6lqq6y2V0zkvkipeuZ+8TsTAMB0elNX/ua2csZxXj6FAp8zR+ItKZDDWRMDi2B4XLM5ZHJyg8MUQ0HkfNg7ffR/lI252TSwEitG2gLEvK1THVcknCQCxROF57/Q3unRxh9EuauuG1114j54gxClNWWKUkNqeLFKXBTOtErQsyEH2mf3lDjImZKSBDiFconSmrGlMUvOUzWY3oQrGeC2zhb/zKt+i3G+y/8wsMasl//d/8Q37ru/8tzcziQ8aHxPFswaN33uE/+c//A4ara37wu3+Ejwld1PxP/+Cf8Aff+0jk98RJGfHPk0//lNVi/hd87iStmqQZtFlTD5H1WvL3fJh80DoyL1dAJqmBypTkWu4VdacRk0VF5N41Qz9SljP+8l/8Fv/01/4uH373Iz5dn7KYNYzdyLPdjptdx1b1/Pr//Nscth5FQdmMLJoZP6gvRDWS4fh4SQye+w+OePzmGZvrA2VZ8Du//Qmjl5yxEBNvvXWMyQmdDW0PuydPef3RguNVzVXWbF2mvzpgGXjz/a/idhtsafjFd95gu9/AYs3N5oZtmzhaHfOL337AzfWWEBacvX7M8XLBx09f0KiML6748MULPvn0U9p9QtcDLgbePFmxXJcs1xa1nKGKkuX8IauFYjnT+Dhw/eKKouh5+sELnr244oOPP+Q7j+/RDZFdNxBTZjErOV4UqLJidIF76xknywWz2QK9rljhmVlF0VhO33+HspzRba6Y1Q2Zgnb0lIsZ3/AjMQWZHA+KF89eUsw0J80ctObjZ8/4+NMnfPT8kq+88Qbv3D/C6pGPX1xBWND3Ay/UByIRPr6Pma/xRGaq48SWYFYcLZZcPPsQVVpOHh5TzipK25COao604mE2RA9PnjxF6cQv/5U/R9s5Dlcdx8sl5ljR2DlpzLx49hIXHQ9PTun7lusvfh+XFMPxKX3SfO/jz6lquT9fXuxZLld89b0HbLYd264FpfnS6Skvd1dYGzidF5ho+PQHn5OUZnNoGcfAdtcSU+Qv/eLX+P3f/oAv/t7/JZJDpfnyW/eZzyrun82olw0Le8xVeMnmpuPx2Yz3vvwmbZ+YHd3j7/7mP+XZ5iO+sY2cHc9YVp5yqXn98THbmy1uVIToON+ds90lQl/y0ceWp+d7Li+uubra8bWvvonLgWa14nG5ZrmeMQbNi03HcqE4WZ3w+F6DrTSmgBASL65KbnY7zq/3/Mb/8UPOr1pef+2Us3tz3jidc3S05nJzzcXNjv3hH5GiZj4ThsHoM48ezTmarwlZ8/bDh3z5tTXOjXz8haxvdVOwWDbkZPg7v/E52+4j1rPvUzcWpRJ+TDx+veR6IxEv99cF+33PLjhm2hKC+Bzferiibfdc7HvO7p2ynM94dvmSualZrBvy6ChNQGvDF+cv6bqRs5P71I2hLBWVDjSV4vHX7tGOjpu9443XzrCqxMXEEHp0hgLDMLb4ZNk58dDprGiaitPFkqop6d3AZr/n+mrPi4s97diTIlgKGmMIMVGdaebzOctmTgyOm92G821PBgorEV0uHgh+gLogogla4VViHBL91YacI2VdYYxELSkyZpio9IjqKSWIXmxU83kt+44ps1rgNpKtbSdlldUWneUZW9hKmuOFxlixhwTnsBmaeUU1W+BcwGYI2dMO/RSL1rCcLRnHTnKhjWWKTKftO7D6jj1RVJaiKvBOJPzaiFRaq0yYsi1HH0lRnoUZ4XWkJBTe7AO6LP/kz6efHv9Kj5+4WIwxTRsTOW6b2LcSRT1NdZQSb52xVjbuSlEUlvbQvvp6pYhJOjAT6wXIqCASAGtADIcAog3XTIHaQIiB7XbD6D0hTlAVrVBJJJZ1ZTBaJlMxJQpboJQhK8+tcTKlJBAXGbcIyVMxdZa4ozuipJBFSeEmkRxyvjlOsJrpY0ppkQmCBKiipsIYgnd327Y8VR5GyzQRI113bMZiCEMk5UShp4B6pSRQON1SRUtmTXkHZLnNVospolGs10tmsxmbzbVIMzN47xl7gZTkqCaf56Qvn2Sxt8XibR/g9ubO0yYyhkCcwmK9fzUJvQXVeD/9flXGde1UMKk7uI1WSnKNskhfjOrE5K3FZK+VvisEk7W40ck1NcWOCJJbpCh5gg0VtpykEBqbpTN3K2f2zt+du1yHhYB4YpwgOrfSVZGxQDmNv7XQTSeJsjYGa4vpep8mZJNUN5EptYTl6um+UFqIYUoZmUrnDGUhhUaU33FdCU66WC1x2jJO5vnSWFxUmKToLrwgyZWQxHaHA1KoyijfqohVCdAs9h1oze/9wTN2/1oBdHx28ZTLfstAojYRE60Qf0uLWZSYQhNCYFUf8/aDNe8+XnNWLSjLAkq558PkM7wFN93KqmOK5Jgnz3HAFgXro2OcDzz54hPmiyV100y/2yDXqtZYU0DWuMHjXEIXJYaRrh8J2nKyKDm9t6QbA92+578q/mOSdFdQRuGTY9tesjlcUnd7vBU/jQ2Z0A/oIFTciEEFSGPEmpI4Rn70h3/EB9/5PpWBuU5EbSFW+KAmmFUgtgPKKmKRSMGRVYGaCHFEeW+NToSU0HZqCkQjcAsfKazGlPW0loDrBumIG3nfpGhWdOdbxvaAibCLIxfnWwiZSBAAlYL18T2+/Yt/nreXJVk3nP3clwjUfPfpAaXsq6bdv6we/BcVf/+vjjytqZkYpOkkCgz9ysvIlJerISotI38m5YgSSnBO4GKPCw6UJbmO3/rDp4yD47urFXVh+bnWUZuSnBWDC3z8/JyMoi5KTm1DqxxdjCilWM0aDr2Qo9unl2x2PbvDgb6PbLYH8R1ZhVKJz57kO3DNrFnRVAafNGPXc3F+wFjN6WnNMAQ+++JT+nYkxcT5dodzI48fwTiOOAzRdXivUYVlGEYudh27PnB18MzWNVW1Zr1IrJeJ6DPDbqD1jkWj8DGwubnk+nJPZSzzZcVyWdMP8Oz5hnvLGTNbsJiV1M09Th5UrO4f44Ki7SXg2irAj9LQMMAUrN1nT/fS8TIETCE01y5c0O0ji3nJ8XrO8bFk5aY2UNZrlI0E1WOy5mytSTqRUo9zUGTFa0drkjKsZyVFodG2Ybjs+fzFp8SQsFaznNW8FRvswbGoNKvTObPZgqZMeDXgYqLwcHGxpw8bDruXhOxIITMvLetZwenJitmq4tmTLdfXO1LwKK24fH6g0JocMm13IE0E67puKOczDvuRD764ZgyRZRk5Wi2pyoKPn15z/fycq+sbnI+oQp5uz88/IZvMN790SnaRMATKpTRDF3XD2XHF8VfeBBRVYfjZr7/Jo/N7sucgouLAbFFx/+RUPLVzqFaWpq3p3Zbn51foomZxNOMbX3kPNas4XdUslAJToWzg8nKLwmJsidGKNx4/ZDUfCH5kGAREU2BYVgsW8xl9O7Csepa1YnnSQPS0h4Hrq0ueVzsUHqvFwxZzxpYFVd3w6N6av/pLX6cfPUyB66XWaJ1Zz2dYbZnPZsQIpkjMS4tRhqQjIYha4NB1bFp5ng0RbKmoC009sQh+5effZRgzV21i3/Uc9jvmi5JHyxMWRjKeyZFl0fDo/hGL+UJkl1H2fEW1ZHVyNEkxHY+O11RFgbURHy2fvziw2Q+88XDOl994HaMsnRvp/MCsbtgfduTCUuqKk9oSp71faTKVsZKlnBzEkqNZw4maMgJDwiqFMR2WwLIyGCpUDtRVQ+tarFLMigZtZdrunfgZu76n7R22Ujw+babsxQatC66utOSSI8OSwXmB61UV66aa9pVi0SimPZQpZD/lpmah94kYxSMYs0PpKb8wB3J2siIrTbYZQmTIIwppYFZFpCg0VVFiS0sMDl1airIUKW52KBNJXiIyq6Yi5kBKIznFqSCU564bp+zf24a5Ep5EVdZiV8qesqyw1uJGUYb4OJF0teyLxtERgqcx1SvrmRJJ9E+PP5vHT1ws2sJCli+/3TDfxSwgnkS4nabd7mASZVmwWMyJUaYJMUZsoamqguOjY9w4CuUsg3OSP5hylBDUmIhIuHtZCGUJH6cCJFNVFfNmMaG8Fc5ndruOXT8BXgrIOYgJOmZ0ISOmnKTwAiXyOJigLWbagIn8a6oJ7jTcUgxr8QmRqYpbr6WeiknuwA9xkk1aa8UEPRVhavLWxRDIUZGTgF6MNqhKU6QCR0Yp0ddL2LYUjredeWMVWkWZ2lhBLA+DE2mogt3hQNf3EDJ6Yqa4FHA5iEwg68l0LLj9rBThbir2SqILTHEYcngnAcEpTzCafCstNuQEYxhlijA9sFCKsrRTgTGFhcc4SUIzLgeyy3eS0jjJXIuyRKFw3k0Pv8mgndKr4m+aEt7+vchmOn95j7TS+AkydAtWijFNPrwoeU7GiO8xi1SkKAq01bjRUyrp7CktUklFnPx36e77FIWVT0iBshTqnLFKIDzyLaXQ1JpVVVFUDS4MFFXByemSui5ZVpaq0litBFygNbaucRm8logBgfiK9E+8EDKx1ypPOk9F808WoODNb36J2fqKZj6nC5FPPjsnxoxzgRQDpjKcvXbM2dkxFXCyWvJv/vwbHK9nrJcN9dEpWAvjlhSiYOFDkAI8S+iuD5HRB0wh0so4gW9OTo9RStF2HecvLjg6PmM2qzGFYd/v78KYY4iMQfxmpfIYH+l7z2ACc2/41vuP0cnw/f1TkaooAI22CZ8G2sNT2puPSH6DqUooSopGUzSGNI6oMqKDhxipyznNa1/le5+d84//4e/w6Q8+5aSxYCNHBQzbZ4TQCUgpRfo4SnvKS7TLolmwbzvCtI4sF3NBpY+Ret4IVVklTIZsFMoYkWmTcdEJga6ymOnaz0nWgtQ5VPRYlrj3/jP+0r//lP/hVw0q15AcZWn5hW//In/tr/zrzC9/iAuONx7dJxdzfv3//JCbzW4CSuW7qa0sQJmfSHv6JzxSTPQjksuVBHqSs6bUdpI2KbAVuVCUocMHx8n2H5EzdH2gnJWkMLLZdELENY4Pn/yIIVuKmAQ0EROfPHnBdr/nL+47AWt1CV0aNu0N5y+v0MbgImhteO/t+zw8OeLiasNHT55TFiUnR0fsNy03+xtSViwWC5bLmqO2xw2O0XmiuqJuSr541uCcY791zOcNujijaUoOVwNVIVKsTz67pB0iL14mFJ6qFglaaWY0TcOz6+cYU4AytAfPcmk4WdSkyU9W2Mz95ZrHD9esljMOh5arzRZTzCnrivuPjyUu4fqGd96+zztvvUHfHzh/doPzCWUXXLVws9kJ9GhWsz6ZUdk5i9LQ7g8cNTWzuiJ6xQ+eXeLJVFmTcsmqXrLbvuS69zy9fIaJicW8IRkYXUblRG0EmFEVhsXxgqaEOA4UOE5XDQ8enHK5aXlyfs18VvPg3hGF1tSLGffvnWC1om23uOQIUXN1bdnpjLGezWHLbL6irhrGnNA68PZ7a3ojIefjdk8zqzg6qRmCIQXNqq5JTtPuB4ZNj24KqtKybGYc9h1p8JS15c3Xzuh9Qj29YHuz58H9OcdHM1bLJf/uOw9oR08fC+LYk11gGALfUZ9ilaE2My7jgavRYYDdZcfZWc0yRQ7DSIqO7W5kfbxk3B8YgyekRN9rLjYXfO+H5yRlWJ0uOFoUMEAwCv+iI4VAzh7X7dhdwlVTs2wK1k2FphJQii15/vSGtutYHlXcXx7zZO/40csb1k3HTBvqasazZxfEkKiKgrKsGLsDxkDdFJPyRFHqGs1EwVQQiLgQWc9K3vj6MT5lnm+u6cdAXdaUBhaVQVtFQJNSjbYR1TuGXeCHL88pbKTrAy/OWwiJ1XLBfhjpt4mUW/qHnncfnvDag2MOo+dLswajEm03Mjp4enUt0/Iy89GzluubjnfMipubkbKRidg4BDCRN84eEsKAUp7j4zn9mElBU1nL/ZOCwh54eb3l5WbED5qb7Zab7Yb5Ykl36KESQn7yHu8G/sK33+fByYLN1UjMmaJu8KGlG1tmtpbp20xyFsfxQIwjla0orZ3ur4rs5picKArDvClBZQ69Z2g9u7bAp8g76gSMpW17NtsDysDR6hRjLW4YKAoj8SlRuA7r1QJroGt7ht6Rs8DPzq839L0jZY1S0sgvq1qK9X2H90GAQdOQI+VM78QmQc7U088/jh4fRsboGOKA7i1kg8kGHUWB5oNQWsMYKYoSUsRkKEwJAUjgXSKQhH0TwRg72RAyGY2PgZj8BEX0hBDw0dH1QUjxVk8wJhkmhVDIszNGfAhQWKKP/0+PnJ8e/z8dP3GxqO/8YEyyvARJv5KjTheRmcTNKcUJmpLxzmG0+mPh71oJVTVO/5KhsAowZGUoUjHF5mR5LaTw0tpMe6FMVZaCdDaKQiu0TYS6wE9ToJT9JDmdAC6TlzbljMp6QsJz20Ah69tp4lRdyA823RxMOYxMEscMWeIlbgtJonxemnIfRcRp77IjUaAioBIxCDBIT4TBbAwmRMktuiVKhjRNFuV7M4Wam6xwk0G7qqQ4dz5M8HvRhpOh1iUqaHKBwG/ujtuCXn6W2+mafEDd/ed2agi3RaEUWDn4V9PV6b3n1WdBYvIqTt5FNdFnp2LBTDAOrQCd775eZSnkY5BNZ45C23p1qCm3E3RhUFlId+pW6nZ7LjLeE1P9ROcVJ6PkPcm5KEKOd3CilGRmqr0hBE9Q6ZWcVPuJahvvpopay5RXvKqRsnBCOTVQlFN2EZOHVGt6U1A1AzFHisISYqCuC8amoK4MdWkoJ51/LntCzqSqIilNQEhrRGkqgMhzY0joLETK+0GmzMMY+Fv/eM6js9f5wfPv8OzpDVYpVIyMIbKYN8yrmsoajpcz3nnjHo8fn1AUBmU0lEix7zXoiSSMIakgZGK4uyLylLV15+8tRCKO0ji/53DYE2OgaipsUb5qGKUozRij8L6fcuPTVBAHXj875vmLlttABwXiX8zShErO4bsW5Q4ctqCU/A5zbTgqAzE5UnIYlZgvMgVzvnj6nKubHVP+Mj5l6tLghi2Hvp0gMQofo3xOlo668x6fIj4FVIwUozQCUsq43mJKSzaZ0QutWaax4ltOId3dSHf/TH+WQtITjSVlyRSNuUAjr1/agnffeszJLNJtDvQ+EGYJ7wM//PBjDoeWyc37x++9/w8LRYVIuUN8RYS+K02z4OYFUDX5Fo0h5sCj6CrKuAAAIABJREFUF7/K6DL7ec2i1BAybetQSrMoCm52e0y5wLmBX9pvKYzhO+WKy61MkIpCkPgoyxBHQugxsmATY2Bzs6e2Jdt9z2bbY63H2oIQPclHua58YOxGvDWMY8T5hMuR0QU6LXL+MXj8LvP50xuW6wodNFUlvvPL6x4XIiEoVPbMozSL/BjJcc9Nv6WsKsqiIsfIxaVjv68IQRQ0dQnWiIewd2JnyBTYaS3yAXbtyGbX8eBkya7teXmx5bBt6cfItg/83+y9yY+taX7n9XnGdzrnxInpTnlzqqxMT+UsD7i7XbZlWm7AMi1aSDRSL0AMYskCiQ0LFvwBbJoV7FixYmPEYHAvUGPLott2eShTQ8735p1iOtM7PhOL5424WVI3i7KNvKhXyryhuHEjzjnxvuf9Dd/v5ztGwaubDcgsYR8ONVWpeXT/iH07YoWgUBqJJpIwNlNMrVas6pLp7IguZbBLd7PnEBKxkIzDhEQwiaweOT89xpqC0lhSCFSVwShLVVqq0jNMHqUldVNipKJelJwc1zmqxbfEMW8n+mGgTxNDGAm+52Rd0fYDzmeoh7ECZTQEw9UO9oeBm63AOc1qeUTVlFBKBpc4PWqwVmCMYhhCpuHGwOEwsZhhGicrixUF62WdqZYxZ0FrDau6RNagvWB0kRDuUxUlU1DISiMbi0AiBslqVUCC60MPIhCkZNd7hMi5sP0YCFGy7yYO7YCymmpp6Tvww8jqdI2Igv0w0XZ7VByJXjBIg9CaugIZE8po2m7gxeWGtu8JYsGj41OaqmDhKuraoLzAFIr9ocV5T2EaAophGjk5qqjLkqLSaCVxfUCgZqVRousdu7ZHUBCTxIVcB5gZNpfVYhItJI3VgJ6HfSPOe5TII95+ytmd3eDQaqJrR643E904Mo4jw5QHsv3keeMscNQYmkrnYPqNRM81jLEGqQWHaWK/H4ibiDIaKRQhOby7QqmIVolD62n7QKELBFCVBetFxeXmkjF4StlQFprlogIRaGoL2oCUDD5rvaRSTDHRjZl0r40mxEg/5KG6kgFVakIKEHOtoWVuXgqZN3pRinzvETnqY/KOKQRSgNIaKllQWIUXoFVCppDvo1KREEwqYY1GaU0Y83BPzfaqorQIoXBTwJaWeyJLQ8VtbSQSZZnBPoeDxU8B7+dhscgby23bMo0TUgiKokQphQvQ9R3Xuw23TFJSrl/clInLYfYyJgQhJdw0x46Z7DZPMc61EaDyayFEtl/lu3W8q2tDyvaqfA9IWaprcvwT8z0iexlvQThprqMyIf3Hx9/M40duFn8IRDOTToWa5Znc5uNFbGkz/GBurLxzHLyffYFyxucGhilwfb3LeTQp0ymVlJSlpCwLjClyeGoKdMOQiVtVTVE2+JBmiSIE53AhEV3EGM358YJ6kT1Pl9c7YpAMU8CnnGeWYi6vrLF3UQBqbgq8zxRSRH6uzG+6c42aGw8AZA5anWtBMUsvv0qMknOx5HyWhHify944G56FkDRVDodVUlIqRRg7xnGiHzNIyCWNlJnsmUIg+BxcX1QF0U8ZPiJlJqrK3HflOI4sB1ZzixRCRMaIQqKEvoMOJTLgR4vXg4AMomCuO8XrJhKQWqOMmSWbr+Wqt1+j9dwYzyTVlCJT71FKYkR+A7wNvA0hzD/r1tcXsm7vTtJMLj5CQmg1b5MlyU/zT/1Kly9fNxIpiXnrmbdwt/JVIcQdjVcbg0hibiTzOZySZJppYUopkr79+QERI44f9riKmObGIqOutcpI7tu4l3T7WuaLJ5NelULZijj/O60l60WVc5usojRFptu5gbygKbK0OQhSgOiyxyMKuGkPjH2iLsCFwH/y7O8jpOB3f+ePWNUlzxZf8Cf/z8d88tkLKi1RIuAc1HaBFgo/ed59fI8Pv/EGzfoEIRwIT3QDGEBkWqlEoqUixDFLTpRCkwElw+Dys1TybpAQU6KuFxhr+fijJ2xuNiyXa9587yF93zKOQ45asDnjchjyYEXi50Y48Najcz59ckNKkf/a/Q8I4D9X/34eTsWIHwP9bqTtdnz/iw3DlM+FIUS+8d4xV1cdttTUleFkfWC1vObJZ5/Qde0Md0q4KbFYVwy+58VmyxgiUiqG3iFSpLYGIRXb/YGyapAYYvSMbsrhySIy7FtsYbC1ZhgHalMTpGCcG2KJQBlD6j3a5tzZ/+aXf5vRTWwOEzIMFGcOF7OiwkdFqSdEDFij+fpb99Ds6NCMKZC8YecM3/veJznX7q5B5Ct//vXdfKXMWaU5hieiUx4iKpWBYN47lFQUZHK0NBYrJdsw0k5TVlsoKJHs9j2rquJedcSXz/dwOCDCwH+w3yKE4D9tCpLMgddaS6RNJC+p9YJ6aVFaoqzChcB209P3l4yjRwaNC5HL6xvKQlLpgrosMEYyTCPtUHF9MxBipFlafMiDgcViQUyBza7n2ff2lI3mflXiFYzes7lqaUrJw0e5ETBVg1QqgzQut6hGcZQstVKsj0ourz1X2x4fcnFXBcFxO/L04pq2bWnqBcerI6ZwTUqR5tmS7aHnervjwdkS0gteXGx498EJpMTVzYaxTxz6gSAE3QZePQ1EKfj5b76Xz/9Dx7XdcLxq6Nt23miAT4kw7lg1FaVKnFSGrZbs2gms5nS5xNqCoe/ZXl/x4KTh0emaiMjbq1JTVRUkwb3zI87vZeJl7wCpuNkd2O32lNZSSMHU9YCkrAQuJV682nJ+ZJl8y5MvN7R7WK0anl9fUOp8l3r2bMPLix1fe++M06MltbS42lDViqNCIpYl3o0452j7jsM0oArFZrOh2O2p6gppQFpFrSuIisPW80d/8Qlt33J+oim14t7JCcZazu81vP3wDW52e477mkdToJaK4X6kODVsthte3rToxYJKS4Z+oi4U7WFgc9HSjQcShrqpOF6VfO3tU3qX6K93PL6/AApe3RieXEfWYo2wJSOZbLlaWdzoaA8dry5ueHZxA0JwfLbi+NiyWha89XCJrQTXVwlE5HLTsu8ndn1g2UQUkaOioaotUeV76fOLLVFKfIj4EOiHnik4RrfEmmmOh7FUVc3l5Q3OR7bWUJeWk3WC6JjiyOVlz+EQ0EYQPbT9RKEFKTimoWe38Vxebdm3A1++2PCnP3jBvdMFcoLPFzuO14Z331iysgX3myUBxxQDb91TrAtDlxwH57h4sUdESb3MyrPPn3xGUVis0YRxwrvAyfmKfdvy+OyYx/dPWTcli6rm8f1zkDkr92azpzCW1ULjA2z2HX5oWSxq9t1ECB4rBdLNerckcZMj+omwAS8864VEWgPWYZWkKsAH6CZH1AqF4tXlgd1hh7KJuixYVMsc56YMhexplpaHJyuUUry62uTry5SElLi5aum63NSJmxapJaYoEUJx+eqC9armnTdOKJQkhkRKuSZSJqG15Py4zE11UAxTfm1SSmx3MI46cysQTM6h7YKu03ifB3JBZDjP1Cd8cBkumCCkXAe74Bm9w2iJUCHXX/F29QCRAGr+MwKzWkvK7JPsh5wpLmb1mzEm5ymmmO+XCRj9HC8SMv8hzXnqP47O+Bt7/MjN4uHQ3n18qzS8UxuL15EDwziQLVqZ9hhCIIYwFxS3W6tcRrdj9mIhFf62MYuJ5xf7GUijKI3KBKaUsIWksgapoDyuc48QJ5KEJKEuK4xSWFPQtz2MOTajODWYQqJ0JhYaWyKR9H3PNGRAxjCOKKkoiwZhNOM0ZvorkeOjiqYsOTlaUFiTA+elQpQlzs2Sppjo+4mPPv2CYZwYhohzIfsRdOLdd96kKEpC9Hg3MQ4DhVVICV2/IwRHVTzKBU03zDJYTT+GuwBWYzT3HpzjnWcaegprMTpvMzab7V3mYs59BKfh0HYcDlOeVslECG5+/bOXEZ9IMudUfpWGKmRudG4bQ3IrhPeZKpbmNxM1A1y897MkNpFEJJKLb6kyTGiIDjV7jIiglSWlgA/TnZ8wS3IT/hbYoWaOqpjR4GGWFKccQJuYm3qy3JiZuJpmKZ6QWbrK/HVyprPKOaNRKHFnFC+MJqqYvYZzIK4Q8+R7PpRQd5LinHUYUCI369bm7KUUIy6EebsiEZG87ZwhO0pJkkzzpE1RFwqjFdpIpFSkKDhdVCyaBoJnchOHtsNHT7ASayVSK44WNZrIyb0Tumngj+//d0SfKIIjDZHV2vLh2yeclvA7v/8pa2NpVKDyLb/64W/wc7/8DT78lVOUlSRzRux3pMMGeXFNlBFpcsZapCfFhEtjPgdSyvS4mGAGCRmbN/k+BJJzJKWoqoaf/sZPcnVxzSeffsHF9SX37p/SrCvGaWTqHDJKxi5SNopFvWS7m/j9f/6CX/uVd3n7nSXHqxKuc9OtlCdISxQyewBFiTQrfuGDGmsM17uW7z59weaVQ5mC/YsrXvSO/aMl3/hgze7lJTYEzhrF6KcMPkiJzWHkO9/7Lj/3e29yHiOf/OAJRVHwE1+/x/GRxalTpJQ0qiKGEZRC1w3FsmA87Ek+obXFlhbf5utsucgBzTFGkOC7cfbzCUpTU5oaH3dgalpf4z14n7PBxqlAaMXyZMH7HzzCTi94urlGIlgut2zba558+TznXiZeb94F3NKT/7LHx/9SRl32c6NLRtcjVR5geZHf44u6QIlM+1M6MYygbcELarrYU+++YOd7nhw8v/lb/4Bf+Im3WLiXvPnJI77/sqUSEvs7vwMJvvb2e+x3l0g2aCl4uF7xh59c8sWzSwrjkQi8SxAFx8uGZRNwLuJ8Hr4tqwYfe9CaqBXBZJ/8Z0+ecdRYzk+XJF3yxZeXHMaB8OQ5x4saLUB5Tzh4ftBNWAWlVpwdL0kp8fGXW0QU8OSKRMAhiM7yll7yfHfBp9MT6qahLiyvrg74EDk9XiIXDX/wp5+wXpU8f3mNlDeUxYajRaKqLFaN1NZyvzlh2geuDxfsD4EXtme1rLl3eh/cJd966wOOz07Z7Xs++sFzvPccrvY0pebl5OicJ6UL3jtdIJ3hybOBbT9xfXPAjRNvfO2Eo6qiQpMKQyDyyfefc7xe8LX3H7JcNuymlsPnn5K0ZhgCl5cjo4tsdy3bcaR3I0++eMXVzcTJSWRRlpwfP6KwhmUVWK8XbNuB6BPLuqIuCr53c8mvfusXefOdBSE4lLa8ePqKfhw5Oir5u3/nA6wo+fjFJa9urrl8+R20UCQr8Tpyvlpz/2TFyVHNmw9OOD+pOb9/n35wfPzxM/Ztj7aWx+f32T79kmfXey63Lfu+I2nFn3y64eG64dHxFQnBZhC47mOmqWUIEKLg6w+PETLwf//PnxNi4t7JMXVV4yPYIvLe4we43uH2Hc+ubkhS8f479zBK8ud/9oS+jfzp86f8Pfkhv/Lh17NvM0Woa8ax4/pmT9t7/vwHe5z3nJ3U1Nry67/8c9SNRUiPQDG5PBxnk0EnKQi0Vzy695ByVTFOPTeXW37vzz4hhcC6UazXCwoN67Jh3xkuDgeeXO4xVtHuW5paow2MXc9++5S3P7iPHxPPvrzCSskvffgWKytpB0VVGkxR8OmLju1Fx9lpQ7MsGeXIdXugXjX8zNkjpAaUxCjL1YsNPnpOTmuEVLy4PrAvdjTrY6yUlBjeuVdhHp9xtG6whWLqE+Pg6PsDk49cbUaC1IQIVxdburbj0VvHdJPj8mbH892WGCVDDPz5P/sOLoIXiniYqAvDYmUoraEs8rawGq+wumaMJbt+JFy/4JsfnFNXFaWRxODZHibGUfIHf/YRUQuWR2uKIstNGW7QpaIpS+wQOT1d8fbjc/qxYwqeMUl6l7h6tePly8sMYTMKrQPGCHxIPHhwH6kETQHeFVS2QMvZspUEzkXeev/dTOUPgeChHTztMCCVYlGXGKuIPnJ9fYWcM8a1znAxJ2FKCVwgJUXwit1+h5scx01FShGpJopjxer4bRCKGARXNweub7a0bmKcIuuTBdHDi1ct/TRQVUtG1+ODy9EeJtc2KSWsEhiVvZ0uBLSps+IPQT/0JBKHoUfEhJy5JdooXD8Qo0Mbm+NIEH8NvvofH39Vx4/uWVR3MHSESHylr/ihI580ZODBnWRPf6WIJ0v3SHPe3+y7Im+HYpqDR1PKwb1pzkmUcOj6mXQKWmUZoLyFxAjBVWrnR6hwk2fsB1KK2EGjtcZYiZQTghEpJJMbaZoKU1coYBh6oh8h+DmwtkBpxa6b2Ox3vLg+zD4Vg1SKoijmsNLZ3xc8zuVN2mJpsjdryhk4m12LtS4TY4PHe0dK2QPoXMIH0DpvCorCzEHFmsLkLW1RGBZNxbvvPkAljxsHCmuzN29y7LsFt82SlBKlBA7Ps+dXfPd7z/Api2KTuI3xmMWEQt6BaO7kuPP/Eszyzll+PG9d8w5PzNCKvF1VWr3eacx+zxw+m7+HVBIxx3AIATH6u/Mh8TpG5bVcj+xRRMwgnoxtDvMG6FZyN6tIiTH7WG+/VgAxzBLfSI71kAKpbtMlb32Xmbw7BUcMgUhGZ8u5YL4NoL17T0s5E1Hk5XoOrhWKkAQi5p+c1OyHnc9xpMhESSDFDPghRqYQGF1AKg0iN60Zda3QVmVvbHwt1Qgh5YBiITFKIJh4uRtxfiKGL/O2dA9P/+E5te1483+8Ytt2aBEJzrE+W/GzH/4s3/zWt3jjgzcxjc4hwuEVYuhhmEilzhLM4Egx5wpGIkbquUnMHsUQI0rZO6n57XmXI0siwzgipaaoCh4+useLlzdc32zxBLRVmRzrXc7uDIkffPaKJ19uefr0kvVpRds6jo4a0nU+P3wCLTykLFUd+h439DjfZax6UpwullSVQWvF/dWjDDG5X2EflEz6KV0XCEGAkggDaVZAhCHyE983vN8kPhctIQW2h54xeHbdnrJQPDhuIHqwJYTA5dOO43XFqqkwQuAmj7J5wivm81iS1QhqDmi9ndKmnMWCWR2z33vGMQOArNYEF3nw4A3ef/99juqI9AOrosRWDfu+4NmzTZaDCpnlUz90/NXceP8zVf8LPy9ELl4n99oaKcjXt9KSoihQUhNSYvKRKCQqRS7e+y/zJu8P/yMOref8wVv8K7/4Dd5/vMLdOP72+YqfUUf4MSJ/93dBwG/+g19GTXvC08cEAb/6rbc5eXzBi8stImqmfuRms6cfJw79RATOVgX37wn2bZ/9o9EwTCMuRnQwBB8ZupHkI+OYMMWACD77k3UO4vaC2SsQedCs87AzRQ7jREwRGcScMwsISWlzzM0QRzo/MkwTnQOlRuqioNEQQ+Dl9Y5IxE0T61VF9DBNHU+e5XBtYxTWaJqyRCAo7EicH3/aBQ5CIEJPFy9wn90wTgGroDCCKYI7jFzt9hx6z+RgqTRKOq52jn0/IWSOr5FO4Um0MkvHdocDnsQUPVeXN4xjoPWO4+OaCo9MWWHRHTq0Srx5XkOqWWlL1zrqtcp+XG9IJExpacoFKUkub/bsDiOLusFNgj/+i88IvsNIgZYF7f5A3RiKUfDquiXEA90UUMpycD1SZllbO45cXb7g2asNy0VJXVR03chqdU2hFTHkAV237Tm0gavLLaYsefio4cx7JhdoipKz0yOUNnSD455J6FVBUivaYcpxJSFSL2p++qe/RvIB6TIxc/ACI8DIwGJpOW5OUIUiJk89KwaM1ViV+EDcR02Bly8v0FJRNwVFpZl0gYmRrvQ5Iw/NcOi42re83A4URUFp4JmVlKXi9LjEas1+O1JVFWfnmUypRKK2huZ8yf2TGiGy3x0iVim0MMCINXC2zrJEUt76D2NknCAqyfPLK6Yh5nMD+PZHT2mqApESpRHZix0SQUo+f3nNyVhzetrQlA33jo4Zx4HJT0gJldHsrGK/b9l0kqYosEJyMzgu9lcsqholFJtdi3OBx/dPKCvNNI4EFyitBim42HQoo1FaocqEipLnr64wWmUP3RgoTL7mHpysOfQju3bE6Syb3O1GdsIRPLg5k7AuWyQRoyVVaXhyuWXZTDRVlTdfw4REcnZSE1O+b/thoHM9zk0UXtH3gRRzDVNqzeHQ4VJEGI0A+n7ieF1hZf79IDJNOwSYxhFrNaXVhJmo389Zs+PkmSaPMdkkKCWzqkmhrMZ5x7bdYSeDtSVBZEvW6EYmFwlErCqhBGYLgJISeVQTY2TygUSitIJSK3RZZpQEksZIThpN7+Z8a5mBlJuTjsFNVOWSru8YphGYI5NiIriQfYzMziif8NFn6M5X7hNirikzxCfc1ZFaZ+sYIsdnSfXjzeLf1ONHbhaFyHLI1+vEu78BuCsapeJOM3979twSIuPcEKj56zMIKd1RUpXKF5+SAkR+E44hoGxuQGPM0oo0b9rUHHBvjaYoczHgvEPPOTZTzCdw1w0IJZGtIM1bESkVMQSq1uUQ3ZAbETWMKPIbmDYGYyz7tmMY8jZCSDlHgQgMeb2qdJZNhuApygIhYByHDAIZckD5ZTu+ruXmJkBwK1fNr5FRXQbbqLypjSHNDY+gLC22aNm1PaXRKALaGBZ1pnAN45gXWCoTVEMIOXReqIwRHwMuxTnOBG4x+/lNgnkDOD/A2buZ8wfl3WPOljUxN0z549vmMgfm5pxFkcQcyjrTTYVAzx9zS9Wcm+xbi2GIt9mIQBLomeKaYkJajZQSi+SwP+TPzW8+IWS/EVKRkvgKdGcGFM1S3BgTRqksD5S3uv/5OYgMN/lKGihK53NoftL5BUhwS4+9Pcen6KnK7M0KPjekCYGfPFLOfr6Uz5Fbb2gCrDJorSmsnSV8ntYNuBQY+gnaDJsQt1vVFHHBI/WcpZRgSoH+y5ztWBhFJDF5x8tvvoOUgukf3+TIgBTZjolHyxO++be/xc/9+m+gC6D/mFS+hWqfQzdANxBPT5BDR+wOEHJwr3MeM3t8Qcy+0gAqzpmh8yBBzp7WAM5PWWKtNOvjNVHCftfxyUdPSSnx5ltnlFLQ1JJPX274/T/8mJevdrg+8tv/m+e9d884Oz2CT/NrL4VCxoCOmu3Vno8+fUI/tWy2eSotyHS+KfWkJKlNg1aGdX/Eg7Tiy+s9Z28c05QlX3zxEus6jJcZTBNLtDnirZ+sqI4aRPRYE3BhxPceHzWtyptVYTxJjmy2I8XCEPsBT2K1qPF+ghBwfcaQKykJ03Q3YBEI/qtv/xpRwH/x3m+jlo/5o9//Lg9ObogpZ46enL7Bb/4bv8Vv/Ku/RLn5mH4YOVlZmqMzvvudkT/4o6eAzeqdGUqVL8a/mq3iv/RIKTcxJsO5hin78EghD76kxk2OIANJQVKJSlgIZBCU9xTNmvunj/mt3/rX+fWfv4cMe65ZcGYKlqs1W5c4zAqAX/vVn2LtBlC/wM12R9f0fOMXfgKUom8V+3Zgu+0YJ0c37CjrkrpS6BQY+hGiIAbJq8sL+smRlEEZy/c/fsHm0DP1E41I1IVisx/wOG72A/3k2PU9nsDB+QyhGBxjCAgRsSkTsas5G3UYJoKA80WFF5YoJaujOt8/VCL6xKubkd4F3n1jxdVVRxtnIqxPTN1AUSj2o2DwAWsS61UJO8W+21C+vMYYSZKC0+qMq+9+yqbdUtUVb71xn8k7ZPJ45wkuv58XheGPv/OclLJfy8fA6VGFsSWffXaBEdk7PqVAUoFCWsZt5DA5UpS048jl1R5dBCpbY6JFRMfZ+YLHj86IEe4ftywKg9OSly93XG/22FJz/94pR6qgUZI4BcbgiXHgbF0j05R/f51j1++42B5wMfH4QaAoLcPQI+qa4+U9VlWBMYK6KkgRZDKE6Bi943Lb0bvIi0++pKkLisIwTIGudSyMpVlkiq4LoKylVp53739As6go5q3/0gisLdkctmx2e3Ztx8nRmnYYOEEgA4RuyPl2UaCAcWhRdcHyeMFPLY4BeP6ipfee5bLEyMi9RydoEXjx/AZQFMaQih02CgptqBcFX1vkkPftruR4mNgdRsYp4kOkHSNH6yX3T09zNMK4Q5mATharc+3RD4F976irEm0s+65ju91R2IrJdRA8WgmOqpJx8PRiwiEwyvDW20vu3W9ISTB2HqPy1meKEMJEShKFQsnEG/fWpCD47HKHlVApg0RTN4LrOXoCoSl04M1HSx6ypJ1GSm2oVEnXTST6/BoI2GvwDrY3O7Z7neVpCLadY3I9z66uOVktWTYVUmvOlg0TPYUpWfSCwTpWRyWLUnK6qHE+0k+BTbsnpMShy/YaozRCFXRTj3MDm90OQVY/9J0l+IGXFzt8TLgYaMeBdV1z1FR0bUuaHCtruXSB4CW9nzLETRoObiSlgC4tVpd5WlwkKq3vrFSgcDEPdq9uDigtWC8qjJKMPoHKFgxjJMaUDL3PwLfZX51VVO515mIMVHWJ0prCasYQMq9ASLSIyARINQ/sBVc323y30ZqUEpPX9EoS+y7XnOQGf7moEW2bpavkOrs4r5lGyzQFrNDESmWKvNZMLmYYpZhztUMkhmzr6bqBoe+J88InhFxPDV4QfcixaCllKm1IDG7CuUDOhPzx8Tfx+Et5Fl9vjl43gtz6+b6yecmfnjdVKU8lbsmbYm4Q5dx03G4FYZY+irxlkjOWXUpJUdhMq1QKozKwJs5IZx9C3nSSaVBKQF0bQgy0XSImgRAzxXOmYc4gTZCSrhtIqUdIjVGz505GxjH7I5yeCC4gBBRG52YxzJMSH+enm/2NMSaE0ozO5QIk5mbPaI/wWU4aw+sNKimTHjMYRSBxd9EPMWQZK/PlnfZ5S8qTC4pZyiCF4OiowVpL3/c5v1CrbMSfHKumRJuCIARB5qiPxG0zP3v8eB3/kWk0+XeXN2/pK6tGuA3cTrfqgTubVCQHg9+NDhApm6L1vGkW8baJm4mN4rWf7ys0ndfnDjMgRwS0VCidw1y7Vt15EMU8ucr/Rs6Pcf4Ot383DzhuiVwhzl5J5kZuPkeReUIe5xzFnOv4lWNesN6a3dO8VlGBA7oIAAAgAElEQVQS5C2xU77eQibJ7IeVudi59fbG/BoYbaiqgvPjFVplScyh6xljzCtL0t15F0Ji8h4XI8rcNt2CgKIvDigUf3f775FS4v9Y/ffcaItQWf4Wg8BFTzvCyfE9qtLi+z3JR5Q7kMy8uRQCjAZhQEtiugXYzL/feNvwcPfa3zbqt6qBNOepxvlaTilngzoXqKssOwlfXLA/tKz3K9KiZFlprvY9m/0e8BTW8PJix4P7a8rKIgVzxqpAhEzwPRx6Xlxc07uR7W5gHB3TNCEk9OOBgMJKA0jub04Zx1Nubg7cu7/mZL3k2fNLppAhV5CYfODbj/aIU8/PXWkUEmM81kNcZjgBMl88cn4s58c1hRaMfcc0jhSFxU8jisQ4ZgmR1ToT8EREitxsfXE4Ioqcf+WRfPrkc1ZdhwCcC9y795AP3n+Ln/mJx/jv/SFRLFA2wzD2/ZaLzfXsy/3/X75zO8hTWuAj86Y5IKIkBWZsukIVJg+KyN6bJHL66un5Iz785s/zzZ96m0YODP0e5RNh7BmngS68fg9Iw4Tre2xd4bynmxwnq2OaRcVWjCgrqaoaYmLZnHPv3popOnbbPSplHL0Qmv3hjG4YiFIhbc3Z+QmvNjv6Q085BRoDm32PE5GbQ8+uHXhxsWUKI11QMPuJBTIPxGZoVmkLJILdxuGRFNrijCQly9m6otaW1rV0PuVmQ0iMkQipaA89SEmSitVCsVqUXHdAP9FUgtN1xbMvR643PUYmykqjC8lCZQjM6EaKQrHddlxuD6jQz37/BVWp0RJeXu/wKYHKSolhMqQYuNrt8pAzJpJMLE9KXIh0YmJKImfGRsfNZYvTkbryrGxDCBPOB4YpS30PfY8kEr1kf2jx0VMZgzWa6BzRR6xWCA2THynrivWiQEjDhoEYO2xh2e8G2iEXxKWUeAFCSY6WFVYllotqtn4smaaJthsyzAVBofN1NI6OFLL6oS4Vp0cLbnYth64laolKgbPVKX0/YgtLVRckLXAxsNnuabsB7yJnp0v8y4FDP0ESFNZQlAatIIyBz7c7puSIemIpJ4QwOQc4gdXyNUPASIYxMM3k1cubnioKFnWJLTVLq3FuRJmak6MFJ6uacfLsOs/oPHVhGcZA1zkiiWEcYEo0i4IgYN97LrcDRRsxZqIde9pupLISHyN1kSndbgr0U2CUGTxoleZoWXK8ylL4vp2wJlFUmeLrhgP9KGhHASmwrsEITX20YBpH+u2Am6Dve5yPRC/wItJ5x3pRslgsudht5xxVRU3Bss4ArxgTVXlE8pq23xOSxVQNCMlmsyeOZAuR1RmS5hOl1ZysjvBRsY8OkGijUTJhFJSFZbUyaJOIEpomZS/noqSsaja7A/tDS2kE4zgxDiPHi4YUJ8aug5AIRPaHntIWNOToIiHhaNGAsvNmzAERq0v8FDBFJp5HdFaFJNjsWpSCpswkd58SgZShQjHRD55gFSHONiFBJqjbCiMG9KhIaSSkHPkVXI7Z0NLiwsjQTzSNRiuTs8xnsIRzWaEltSQJjfPZd51iompqEokQJhAKZXNtKGKil4qyLNh1PcMwUiiFLQzIrMrrDkOuubREyTk/cs67tkbMeYoSIQXGWg77A10rc9xHzPWKTxHtJUSDsdWstJtwwZOSQGhF+Guecf74+NGPH52GqtRdYS6RP1wYi1wcZjlEmqmhMxWVmJsLMiY5N4wSoiBElwsfmWWnzuVKQSsNXsxADcmiXhKjn+EteSs39NkbGUgM3cjNdkCpgFRwvZ+zv2ajmNLprrG7bUOySTdSaHFHrvRhgCQZHfRk2EbCkzcbuVCSt9vQkKmkUgqsVmitqYxkUVvGMbE8aWiahsWi4cXlNT5EyrKkaUpESjMwIG/UpJBILVEyb1Am54gxyxONzdEYu7ZlHLK0ryoqYphwIeKSzvKJVCHihCcgUwZNtOOI6yd8jCgpqGwmySYBaJMLn5Sw82ozJXnnVYyzfOB1Y5d9hUJATFn6w7w17Mfs75RCz69v3kJNzs9xGAKj9WtP5EyoFVKijM7oZ24HBrnpdD5PAgUJ4V2mnsrb7eec5aYkWlvc5JAiS2tvRxoxzvLklL4im/Z58hcCac5zBFBzE3lLqwTQ83MJ8xZV3UGA8n8pRYRWlDZvlOqmwhpFmLMmm6ae5b25uev6PcEnlCqROj/nGB2LZcmizujqk8GiheLk3jHee6621yilKKsqm9J9ou96gg+smoa6KpFF9hD+0v/0r6Gk5J1/52P+8f2E855/+Bs/yXJZ0nrBdz7a8/f/7X/EkX7F0//rvyWKise/+C1K+W1SegbLY8TRQ9T2BUEn0jDA5BAMoDOlN6EzBCoJrC1yltLtkEEyR2skfMzDg2l0+OQJMeBGT4yJ93/qLfaHlv/zn/4Fo7T8m7/yNk+e37A/hExfjJEw9BgZOH/YUFaavsu+OJEETWWoVxpELsaMjJQLjZCWECTL5oRlVVGWNgO4FLghMvaCn/n613jrwQl/+M++xyQkg5gIInH57Bl/9F7BP5WJf/RPMizj3skxpTEUtSImR7lQSOlYMFEhqBmQ+xLfOy6v9ly+es7jB2cUhcFNIyJlmq0yKm8pUDPRNTeNVbVkMwS+/ccf88ffjxSqJkrBr/z63+KdR54q/gUHY1nWDd1mC8LghWMYrkFMd5KpeZozX1evP/zLHL8dDgD8W2oxf9/5cUvJ2ekJVV3w3Y8+wc2ZqVqY+brN9gLjBSoonB0YgqAfPMaW/Mf/4b/L3/vWz+C3T5AhUBfHiHLPOPaodUV6dYMkoaVivWiwKtGu/oRU9MjhPTZXNxxudhxevcBFQe/yPrU4XyFNxHcTz19cMMSJzU2PtYrH99dYDVZplOj5Ox/UuFgQQ0JOChEFy/OaanGEVhmscX1xkxs6I/N7IhnaIHTJxdhz02158eKGw96xu2xJSKLI21Y3Rtxhy81m5NGbj7FGcv5ix+XVjpcXGwYf+VsfvseuG/jy8gapLXsfubjYI4D7RytsLBn6a5pC4kR+b7aAsj1aB5gU0wAHMRB6z+4wsTpaEmVi3+95dRVJcgCpOGlq1guLsgsuLza008TkPSJBZTIxNDAyTIH45RWLOis0JDJHlew2/KB7Rdc7qvI599dPUUqxHQbGbuKtxw85tDuaZsnoHdvNZyhVEFOkHQdSEizqms+fvOCmHWgay+miREnN+nTJ8YM1w2Hg299+yrLOAzTMJatlQ/KRi5sdrfPspoFKGSpTcLw+QhvJ0aJEpkitJQ+OV5RlTecGvnzVIWLCSMPoJBc3Ld59jvdZdjm5wDSOgGK32bFeH3Fyssb1DpkE5UIBif6Qmy1tBVe7A90gkb3nsNvy6TBwaCdWZzlySMQ8uN5uOmwtKItIVWuMbtg9j7RuYn/YZ8hZqdnebFEysxEePzjh7GhBYySfv7zh2asLFqVGWwvaooXgfJVohx5JojaCB8c1192BKcIbZyvWy3NEguAdhz7STYEpjNiFQU+RbjhwHfe8/JM97p+7DCYZB7phQkrJoqozD8IIrBVoIdiODo3mfF3nGAQNWkEp6uznV55p7DhMLT/YDGjZ0Q4tMYWcta00yyPL9bYnhMj91RIpEs93e1TSlOMEQmS+hEi8c/8NqkZhCkm3P3DdDdz0msk5rtuebvKkpxNVaShLRUySGCUyKerCEMmxXEYrjBIk77Ba8/D0OA/jfERoj5sUq6P7CCSHvufxyYogNYMLOFcQU+Bqd+DB/XtMQ8/o5iZq7BEi4oPhsOm5uNnRDxNMgWQTq9WCxuQBVmMNh3bHybpCCEXyAe8CwzSyb28zyxOLpuFoVbEoNOfnGe7jg2S3H+mHabYo1DjviD7hhsylSC4Q4pylWGQPoJa5rnr4s19nHCeCiHcwvuQyDCvGrNZzY1bdrZYLTk+PM4mdOMtZFbkxN0Biu9syuZH1uiEEMw/cU45rMQbnR/SRYrVYIJUhxoSPAu9GIKCVYRxyHQCLuyztlAQpwv/ye9/5y9+0fnz8lR8/crMYmHMIufWiiZxzB/MWKhDjnPKTgJA9i0HkVd6tt0zITOkUzMje0eNmuehti3d2vKCpSoxWbLdbrBacnN5nHEdevnxF1/dMbsq7p7nRMQZe7z5u4ztyrqOLt1um/PhlStmLAaCy4Tc3vZKEyo2pTgiVm03vPIl4t1GJSeR4D6lJRIbREwcPQnDoJuL8ZkEUnJ4c42KG6KyWS87WC0SKLFYNbspFuCksxlpqmyfP0ziQYqAoC4wpSSTa9oBzE4tyxXp5RNEksAWTOSF4S3do8X4HqSVN10xdz5OPXnLTB17s9mghMegcaC/njagP2e9G9qQqqTBKz/LJ3BjfZh+K2XtHyo1IlhTkMHPjBYL87283drc5nGIeJGScNNzarO6g+/OWEDFnUt7SRkWcZZjcyXaZparZyxKIKSCkylN6MXsov0I//YqdlJRgnKbsB5zR4THkBxPnmIy7RjDmDWNMaQ50EHffJKYw51jlz4WYm6O2H+n67NerypLJxxz6Sx4mrKqaRV3x8NE9irLi+fOXXF1d0bV7hmF+rD5PTMONZBhG2m4kCQFyJMRI2/WAQAtFP4HeD1z3G5b1km8MmbR2cbMnhoYQIi+v9mwPHSIZ2n3L4zcqFsUDvvz2d9m8eEpzuub0vQXG/OLsO/OkZZZPqbLL/sopb/GDnwgpzQOBhHMRoQ0pKhAepfMQyU8S70Z8kAQUgcjkR7SVTG0OArZ1YiLxp999yrpMPHmWgQ9K59WdQ+K9pDIFRit6AkZKnAtUdcEvffg+H/7kIy6vt5RlAUSGrsUoQ6MjRkkWxRpbara648t24M8+u+Sd997jvbffIIr/lYihLKD3kmaxwBQWa+Hsnia6ibHfIbzh4LK/7XhaYTSM0qHokKLn9PyY42XN+dGjTHRVmUqsK8M4jux21wghqcoKqU0u5oInALt2g6uXiJT4/f+9oNL53Hnw8AzlWnbPr+gPE3HqmYae0nfE0JOCg3lbd3f8fzWIt1t78foc/qHP/4u+FvFDn8tKhOyTPD5a8+Deku9+9zOiD8TgCX5CK4WTApUSlnGGS5VcXV+TjhRNs+Rnf/odmL5EyonkZmWFNUiZqB88wAZNtBqtFcasMH5gfPhPMM5Tf/o2V9uW0Y0c1wXCBcYUAUVSMm8OtWC9LBCm4d1794mFYndxkaf5ZZMpyJPDOY/UmuZoCT5wc7Wl248s6warNUfrFdWiQvg4PxZJ1JJ9N9K4QHm04N56gRSCuqiJ8zWBLPBR8dFH32Pbed4oBGEKfPHowFU7cPXxUzo38pNvPuQHz6/43rNrTPDZJzhmKvhHLzfEixtKlyi1ZnMY6ZVALCzHTcl2XzA6hxKgU+KN4yP2jeX47AhI+MlRKcWzq0sCYo78EWy2Gy5uNpi6giAohMJIRdcOJAuDTxwOB653EPpEWVuaVUkIYR5QJfwk2G86xikS5cRxpbh4VeCDxwdF141EHEYPFPM8eULS9geOjIAhEnTAL/NW7y/+5COSjKyqmrVtuLzpOD2xnKyXVMcaJxy9n7jcHAgRFiuDkYndZkM7eKazhugTSQqkPDCNz9gPPV887VFCUBWGh/dXjD4RYs3Dew1n08DV9kCKSxZViX9wyvKoYXXUYOf70eX1DkTgMAzc7CaGGLFaMvWJaRgZ+p6T82M2bcu2bzmsF+yqkv3VHnu6ZhktBk1U0LuBn/3aeYa+yezHK7Rntz3i+XXOGr1/vGBRZ+nfmw9PETHw9vmCe2dLBq/Y7A50/QGjLUYqnB9pNyOfPdtk60dIVAUoNBeXW168OmBKw/J4yegiN11HO44YJVhVCkrDYrVkdAsuLg9Mo6MqBIWWs3omK6ea2Xf38SevqJuSN+6vkIVmwBEBZRIFGpLhs6sbSjFydpRrlkIbrNL0UdCPBw5dh3dpDnKv8d7Tbw/Z3zyKDM476llhWCQFRhHaEXSu8QppQUqKZY1UAp9eRzesVyXWZPK5kprKlHnoGCasNWzaw0wYFaTkKYuCutZEP4PatGEcJ6bRMwwj/eDox4lnFy1GC8rKZkAjjsKUpMmhpOTR2QkhBqZpIOBo6obSWqbRoYzBFtVr5RIaKSxGe5IPuGlgd9gxTHCz2UOK1E1JAvbbAedy9ERIIxCp6yr71Ec31zlZrWJsXuJM04Qj113t6PISwEiSIGdVmzyER4GwBipLiolN26Jlys9jdIyDpygKjBXs2z3DNNKPOXO4+H/Ze7dfy9b0vOv3nccYc851rFVVe9feVXv30ensTjsOjh0RbEAgkosQFAiJBFdBCeKGC/4RCFIkhEDiCrjLFYowEQbLJk6wYrvd3e7TPtXedV7HeRiH78jFN+aqcttt2X0RfNFD2tpVteaap3H63vd9nt/TZvwUb+1C253HmCp5phREKXg/IZWqTXZZgZJS1ExopRXtwtVppZYzMPCNe81Ptz9T209cLP47v/hlSqpRGCXP0z/EbYfEzObaZKoks9IdqVKVnHG6ZelaDrolicy3P3/Mk8cbvvDePVZLgR8nLi53vLx6xcV6yxQEPjUUCZfXV6w3mzkQFKy1LBZL9nktRgqMBKX1bOythE2lNQWBnIE0GVBSVo9hLjWLh3oyV7lYLXCLkGihoAhiTqQUGUOk95FYRIXj+JFeRiiJlAKlgFaGfqyQm5AyQl7ymx9+hkgFkev3VeRMFxXMIz6qzk6AyLWgeu31zLckUJ0lTkpODhWxBK4HgdSOZnUH03S41tLZieOF5EAkhm3P9dUWayXjZiSWKkcS5Aqa2RdrWTJkED4xKzir3EztIyzEa4roXOnFfSE1f5SYZ/Nz8rcS0r08MeeCEvJWmy60gFxoTUPMNZxVqlrE+1DzG60xdd+VGoy5hwjt4Tj74rIAewKkVOZ2irl/XakkJVdqaRECWcobeZLc0r2qHLUWiBU8FCipFpb1I5bbgrnSWqvMtjZOanyLLDVPCarR/+L8sno15wiGl8qS8hXjNz/CqErFVFJjmg3OGhSS6DM+AWI7+2Jn6mpMlAKrhUMpQEy8uthQEkin2VxfMvr6Xf36b/+AV9dfRFvN7314TkmVdvvxkxue/sp/R9e2tI/+HO988BD16lPKg6+jWofwn8PuCZM4JOeJtu0QwmKiJcvI5M/J2ZOiIaXZS0qPkJYYqn3OWU3TaLRpZ19NJMZMSlSlQNYQIEZBmGCznvgX33rMzTahYoNBV7O/WLPe7DhanvL05/8J/+RXP65ydimIBT56/IxhuOL6esAnj1aS1eqQtm3QZUJIRfEvWS0dyML3fvgSfZW41z5D5UtC2HHsJj67KPzCz3+dv/fv/yX+26MbQph4dH+F1prVwYLWGTqdMc6yi9UbsrQWqxVjzPNkuvozKBXulVJGG4W1LU13SC4QUkaJDKlKqSSSKdSirFOKTlVpt6QgfI8VmkXTwQrQA2pnEUAsE1NIr2X7t77cP6Za/HEF4R8lY33tJfgD//baYZBxauJLd+/gE+ipIZS5WPIKUwRCQVCOhOP3v/8RUt/la1/7GZadYpX+BeefrUkoFq3GtQ2y04iLG1Qq0Fj+x//kb9Fpw9/+6LtsfvApd98J2K7h7lfe56F15JzYfPaU7vAAc3ZKEHD94gWfPf4Mh+S99x7SHi45//Azhhj40he/UHPlUj2P8qKw1KBlfb99Cqx3a2TMrK+uSRSk0WilEEbz/NNz1use11Tlx/d/8DlWN5wcL2mdxDuJdA2PH18Qx4Ag87VvPKIcWZQWLKzhF1ePUMqgrCMJz6ff+5B/a/VF/qv7f5POLtmOPbubS2LwfP7ZBY8fX3L/zopOw2/+8Dnn24GUE3/7Lzzk955e8hvfu2bXe2zpWbiWV083/N73H3O96TFacdhVcnccJr736oaoNQdGslmPlF1PQXLYdhzIjqZp6acdTijaxnHdX3P3wSG5CJ68eEmOhcOuY9loTu4c8exmRxaeZXPIZb/j6ctz7i5b7tnEej3xyfMtXbsgxUgqpcKkYsI2mvvHC+SUePY4MwbQRLQUCA0XsWe8ueHk+C2eXqz57vMbLJqllixlw9UQeXUZeex7rtdbjroGYzMlCN65c0aRsC2eu4f3+cq7jpdXl2z6HikDIm158cLT9y1DicQUeO/ucY3+0YWPnjzn5vd3rA6PiCWxubrm3btnfPXRPe4c9Nx/+15lAaSEUQZjDU9fvSDyDhfXG4Zp4vp6pDjH2YGjH0Y+fbkhxIQPIxfrA5ZaM2y3DKNn0XUkAwWLk4offvaMSKQ5aDjNLc5ZLvuJFx/tSKHDxwHjCq1LKJGIMXF6csLf+sIDKInLm551n7m6GVk1K97/ygkXN2s+/OQFTdvSOMfdOw2lJDrdcnxwyKa/RJSpgl0STGNEd9UTt2gtRkpco8gR3nlwxJNnl/z6Nz+mCM3X3z9laTUhRJ5eXTNmz1sHd7Ea+jExrkfGMWJ0y8Eh3Fk47q1adtOEj1uO2iPWwRNS4HTZ8d5X7zP0A02n2Q4TvQ+Yojk5Oq4wqzSQCeioIWaccYSU8SESfKDfJi7HgV1c07Yt904FVmfOjju0UrSDrPowKXl6vma9Hvn0/IZhCgybwqJbcO+0o7ET3V1XszmNYUobxmFi6hNCaFzTIZUlphrHEWItAguWEjLjkJjGoXqmz29wzZzB6xOX6wqealzEakEqAp8Fu8sN9+6dsuw6xmmL957FgWHV1nVDKStShu1mZPKJq/UVrjV0XZUkX11umWZ5ttWWo9UR43aL07oSTMm4xuCcISaYQp6b/aC1Yui3NNZydHxI2ypWXUJKQwgB2WiUEuRUo5E2m4mcCm3ToCSEuCVJiZILxmEkeM9y1TJMnqvLLWPIaGUrb8NA21leXmyYpsDoAyUm4EchbT/d/qxsP3Gx+P1PXtXOxhyYXolGpRIcKWhZ/Wlprnecrp2RMvtajNI1CsM6lLW8uN6wG0e2w4i2Cj+FGv4qNUWoGfjgZ0lbDfqsC7I64cwiMeslSfOkp+Qadi9llYc6WwlzUoFQIKVhH8VAzjjraJtKdPR+nH9PYbXBmRoHUHIhxFylK7PnMadE8HXMr6RAq9p1t9oSc/XnqNZiGlPZh0WSkqEUSSqVpFf2u2OWVSol0LJOsYyuMhikR2tqAHUSCKlYrBzEge0Eylhc0yCVREvQOdLpgs0RP3li9jXL6Te/j0hUzTpVJloHw9VjkSi3EtG9m3H+8NWXtD+fc7mV/tbHztTTUj/QXnoMNbS8/rUuTN/MayQXyhxwX0oNXi/zY0qpuUdyX4nyxnRwpoKWW8cl8+vOxNTbgrJ+gpzSTBOtP5Nyzzh9wx85e0RrPmT9u5LiVioh3xzgzD+XokrTSi6IktCqdmRTrpJXJRXWVY+emJ9AzDLoxul6zKZAKQlVJEVCTJkhZHyBHHeUnFl07SyFrTvg1c0aqdTshSyQK7U0KEmM9Tysk20IPrLdjEhgItIPgf/z92/Qao34gUfblrM2c3AZ+OWf22GNQlqF7jJlDqKXGlSj0UNBuQ6dJoTfNx2gzLKWUiQ5F/o+VCm1qp3KkKZaSM1NmJgTUs0ePgmKgrQgVSaPuZ5P847d+QklYfrg9/ln//dvYIqAIrDa0hiJwCNJqKxQQjKNI1NIxDGRCaQScGuFFYrkPXfvdHz22Uu+++HIZggYDL/0Vz7gL3z1HqRrfPD1GCkRkRLbTWTbS2RIWFuDo6UUxFZjnEXqOvGv8KYKL0qpHsM57SX68wQ/V7hUmeMzMiBRFFEn17/w1xJNo/iVf1w4v54I8gC77BguN0jdUeQlyAyyRYhmPnD3x++fwL74ow/60Snjn3DLQNASvexI+/efPaRIEQ0KWX18657NuOHdh1/kg5/9Rb5lMiJXmfri+A45ZXSIiFDIGpQ7YLpeU3yq8lotOTw+wN4/q6TgDHmaUCFRcqSomjvLeoMsGTN5mqbFIAhjT871GDNaIksm+6lm/87HoCgaIXP9QKJwducOOtWiPwlBFJB9QKuafdcdrmiNxElom0Ok0bRW1OOPgllZtDEUHyEnOucQSpJEQs73hJTivHiDk7tv15vktEGgKOOOOFWJ4aO3Tri3OgQZcE7xy3fvMM0T3BMJ31gd8da7E2Pw7G5uyKnw/qNDtl6w8xFRIhrorONmPfDR5+fspoCIEmsb+rgBCo/eusPZ0SE324H1bssQI6UEQhFsvWAYJpbtosJCSmEIkeubHSsnOVkuKVnx8uKGf/evfp0cJi4vt4hSOGwNUSZsU/H6WmqO2gWIxOU0ICgcNo6YC09frmmswfiItoJcAr/z4dOZO2CRaHphcUayixNTLAxTIIbIBsmrix6y4Or6KTFFks6cHmTGSfPqYs22n2gXlvXGE/PIevAILUkl8J2bnpwk1uwJ4pqrzQ4/RTprudz0fPfTFywbzdafs5npywfLFafHJwyDxzWK5BXJG1ad4GBpkDlwvDQcLhpCKkwhEv1IyInFwYrloUBEiXaKMRbUDHJTuQJFitaMKbO56vEhsWogpolpkAwqkmSVfq/GzNJWmN3lzcSr64ntesPWWfSNxcfAYuk4WCpWreX0cIEELq4GrrfX7IYdThsO27rO8a3GxwwxUXwma8nTJ1d0neXk+IB7d1YoIxFKc+QsRoMUlm7pGOPEoV3S+4kxCRwFqwVt5zAkrLFIKWlSQpbM0aHD2cLVRhAKXKwvSVmQhSOGiEqVDF2HDgVjNEJ3DFOmTIGDwwXtwjKOvmb8ZQNiwWYyTDFxcb1DlMhmM9FYV6+5swcph0wcAyVCKx2HpwqtJYMfZqkn5DKQcybJQg4RI1RdS7aaUmrGrNKCGOvabdm22MOm8gUiLJwlz3TQfvJIJTk+qEtvI6HkRMyFZECtJI2RNFqQxno8LJsF2tTIsRSrrcs5hRSF9s4x2lR1VCqZGEes0iysRUqFMRBMQZoK4FmnLFEAACAASURBVIsxM6y3ICTONcRYG/TVWtOgVbWW9Nu++sxFQYqIIFUiv4S2UWit2O4GjFK0TV2DxOBASMZhQ8kRrTJaFEzr0CeCkDOZul4qZX7OudkvUXTLBqkKcPmnug/9dPtXs/3ExeKnjy9mGE2ZfYgCOR/QpYCcJ26SihoPYS5MxJxzpywle5QovPv2XR68dcZbZ8eEkPj0s0uGyTOMFQm82xSkmhDSvy4M5kKqpEwsddonxBysLqgHpXwNYRDAdjvNk6a5+LrND6tFjzGGtm1q1z6MQOHu2R3ywnCx2eCnCFLRWoUWhaWrF5ZcNHLlOOxajJFoLSg54qcJbR3GOppFgzCSVCrpKmeDkpa2WWGNm7Xj9YTOuaKTc5qQ1NiLLDJJZrSxNaRbJJrGcu/eHXQZSUmQEGRVQORaDBaFBkwJkDw/+PRzPrxJNK1D5zotzDkRc/1OqkcvVvmvmEd1twvI2TM6g0pqUchtQVbLyn34/Juhm3WTuhblWr+eyO0LOaX3RWsl7OZcSCVjbb2p7AvHWx8js4dyniK+ORgR8jVsZf/Y+U3dxnGk20zI197J1/OS+fdLlbXuZap1knj7jPU9pVRhK7M0L1LjJZTWbwxj6v4zxlapLJXUKckYIZHS1e8yJYySfOOrj7h//xhKYbudEFLz6vwlOWWMaQgxcr3eEnIiU70ECDU3PCL9NFDivF8olBJwj3eVtjgO1URvDM4o/uWTEVMi26tnkOFf+8b7PAqRX336HNM2uMbx9tuHHB8sWb13F5QgppGQx/ksbLH2NdTJ+0SSEVSdHk6hhjqLoqqPeS6QYgDvR3zwNKrCqJSoskslqx8Uk1kcCb7w/j1230z4nUcWwRfeuUPTGEKojxeqcH694+JyYJwSUjQ1mkL4KpEOEh8niqqSJFfFsRydKP75t57w2YtrijGc3lvyb/7s+8DEtz96wqZf4Yzm+astwgBWgtIs2hUuJDrhkcpQssbEQoOHUvehVq7ueSNBWeR+oQOQC9K6WvCXXD3IpXBw9w5bd0TSkv/gH+xojOKf/28N3/zWh/zMV075ir5DnH5Aa48ZKWShUcLi0NQz/3VEUT2Mf0zVKMQf//cf/dkfU0DmlNl5kMdHNJ0hMxJCxqKJVrLNI+MocO1bfOmrD/j7f+/v8PDdBZdPfg1RCpaMlpEkFNksKEAuAb3ouL58gUfTWsWq1RwuJPrkEC8r0CpPIynkKvdtG5LI+M01IidW0tDeOSWnhAwBMU10XVM91b7Kp4ukXltiwgmHMpYQPMN2x0HX0Sxadv2OXAqLtsUuV8DI4dLWTqOGGEfu3j9FKE0WtWBVOWOdQghXAUyl0DiBbtpKZPVTpT8ri4qZMfQcn57Sr2+4efkccxRokCANSqpKtz5V9OOEcYL72sAcy3O9u+LULnkvTgxjj4/36BpH9gPJdIwlkKKHUDjsFuwSPNtuGceRFCwFjSJQCBy0BlUkH3/yjGdP3mFMicvphjgNrHcK7xPvnCxwznCx7nl5UaV8RiSUUShj+fqXHrC5ueTiZuTz51tiqA0rH7esDhpWzqJyYghV4radBvIYuLyeiFlw//QAKSVTiNxsJooutCh6P0JKpCJ4FQpWCLRNrMdKkj5whtEHfud7z1i0tp5bOdMay9OXa4oPUKq1QOsFU4q0bmTLrk6HtGKXatPz+vwZy2XLwWqBtJo0AUay3u24We846jpWx57PX9zw7OU1i85xfLDEUli1FpSlnwLDNLFsWyiBo0OHQJGRSGe5uYm8DAOu0yyXjrvLFVOf8BSkTjWjNylinziPV2ipmKLAx8w7Z5rGWkSR9FPg5TrjU2HTez55ukZpiXaWt+8teKES/ehpVKazFi0cRYJG4kOqVOxdzziOdFZilKJxFqkEKy0YpkiK8VbCmVNg1ycwjhQFToEkME2CmASLznF22DJNlrZrCTeC7AuNMaQ4EqaBLESFic0k8FwK/TgwjRPb3uNjYfSJ1cLQ6Bq9Y52m7SxxKvgc2E0D221AoJAy8eLyEnHTMowT/a5nueiIsRBSJpVEKQmK5PmLVzSdmyO/JFJqOmeZwkgWEqMtnZI1B7IzxOyIuVpevA9kMm7pOFwscM4Rs6ffDthGI2QhOEihrt/6Ps7RXFW9ZZQmxoSVCiMFqhEzY0HjfcDHTCrVMjX2O7QArQTKGKSQ9H0kxIgfKsCmaS3GaowxjJNn0/uKCkgFZwy6bYBMiJ6DVUPbtRhVpaZ1XZ1qmoFR5Fzl0E3bcHl1gw+FkCVISQqRlDyHBw6l99axPZ2wyv7JCSlrlqSPkaa1hKiIIXK17WnaBaloUJGcIiEkjK7SVGWqHQqhKKJmXf90+7O5/cTFolGqFiWijtDzLGtkv75IAlkEtQSRGGtnr9lEKZmmbchB1kmMlHSNpVlozl9tub6phLeQE36sPj6dKk1Szu+4hsrvPWm5UlClpOwLgrmwkXKWHJRCDBVHLcWcB1fmaSTidk1k9K5KPXMk50TIBrsYuLjeMo4RKRWrRmENHHbVgCxl/Xyi1LBSRJ00brZrnKnFojLzxTEHYpowusFoy/HhCct2MU8cmH1wnlwikw9IMXfsSyYphdKOFCILm1EHDe2xBTwmamKhZgxJIFVqlVICLTIlRfr1jsvLLVlppKxY6Uwhl7nUK7l2fIpElZlSOpMxqy6++jbn3QxingTOU7hSymvpqhC3kz+ok1YpRDVJl0KMcTbFK6ytMpecSr24k2qRbHQtq8os/pyL1L00dj4QauHyBwYl4g/9eS8dTWkvr937MGsRWPgRAcT8WnsZ65vPs59G3wJ23pim7otNmGW788zztpjdTxfVHBmTaodTSUFjDQ/v3+HBWydUX+quUuRUIGaYYmaYquTFh8hiuZj3WZV0ipJYD4oUE1rX4uFg2fLBP/wUITRy4TBO0bQLNjvPsqmyJxMiJcHZUcv9s1O+/9kFer3B6QvG60vunJ3xYNVhlaT0PT5EJl/Iud7Iak+hnoxKCvbjQFlD2chTqXjtuajNucJoUs5Q9sdJnSiLVEEtysHyoOGdR8c8f7FlHDJxSiy++wG/pCO/mv6PWYYeudn0TMNIzBmtaxyMIlaMuNKkEBBJk3RhLJkSIuieyyEwxMLRwnJ6YLm5POdqOzApwRSWkCMfPdsgrMQsHFobzk4XtCYRikeqghMOrQOdqh6gJAXZR7IQCGcrrjxEhJQ4bap8O1M9tcAvv/UYckY6SVGVtrk/uDujePnsc66uP2A7vY6dSVhS0shcUKLSn/9U248WgG96GP+UTxOnzDhGVq0ixAopEHOsSiDTh8L9h/f4y3/55/lL33if0n/OgbtEGVtRSNOWLBxlLubkVBAlgNHkkPg7/9ev1xiYv/41YomUMjdbZEWNSalmeXnNDURVyIKSijg3CRWCIEqVdsc0Z51KBKJmQZpqSyipNg+nccQYg4+BEAPWWlzT0o8jOmUkgqkkBj+RgkcZS1Y10kGnTIyJ7dajhMQqRbescnTnHBFBTBHpHLYkpjgiZyCaVg5tFY1taNvqcZdkggw416BNofeeGBLEjHECFPTriX7nMc2CtuuwRpCNZhciUSqkk9w7WpKs5q46JJUMyZJi5rB1QGLyA+PgWTaCL54lAokLf8PYj7x4NSGQnCwNRisudp5X1wMx1fibGDzLznJ23PJrv/0dNn29BwkjIEta4TBo/JRJIXI9eYIvPLrfIoFtlOjGcHbnkHGYSLGQgyBluHPasR3qNUZKkCUy+Qg+12xlLemcISXNeagZe9rVRXophWkKWGVJYSCnQIwWnxJOREpJZAS6aVg1S7IQvIgJMXqEFKxYUopgN3hyjogi2A4TiyNTqddKMcXI9XaN8ImdtZjGsR09Ly92tKZlsdSsh4ESBVJr2qVjuxmYxkjZFJa9oSmS9XokaoVWtQktRCWo9sOaRbPAmQVZJkoRaKnpVhp2EjbTLUNhGAesldx1ikUrWa4WSKM5bDVWQL+JbPuMl4FXmwGfIjEkGlknZYXCEDIqSXRKDMHP6qxclS1LRz9Erm62hJAI/QA54poW18xNlJQJISBMbWoYKdBaMGXFdphQzTytop63UqnKqYgJZ+r02aoGVQphnu4LURBNmnkTmcknRh9RMiNV4eZmx2YaGQbPOI0cH2TGwSO1pmkUq5VBoGmcorGayXtKqR4/lEBmQ1GFUBLrXWZhLcdHC1LJlW+RBY2xKOmxRrBsHEIo1ttMCtV7V3Kqtz0Nofest/W8dk6RUqRRtlqmZF3mxTkyS+qaLaiR5FQJ2DFnYs40xqBkjfFKucZjVCJ+Be0poRiTp58m+qEq/Bolqz8w1fzslDKtczTKYBuLEALXWKJPTNN427SXM10+1dw0pNYzSb+uExGSPfAvpbrmFlKRUmEaA1kze5UzrltCDkRgM4yMqccHgZQRkRM5SrqFxooKQKs1smQa4y3U8qfbn73tJwfcyDpFlACy+r8kYl48F0TJSDJJgJGW996/C0Lw4sUFw7jjy184ot/UhcZ1v+O3v/NJXVhmxTBVdDaikApoXf2PSsoaIp8LOUVKmUErQoKY5Ya6UlMUpQJSqL4hKaoXQqVUixAJ2dffVaoe9Ptip8pQLDllNts1YqxyA6fr4sxPEh8kYwClQiWj6oHLq7HSQcmkFJimCedaJDtSSJScgYSQkeOTE1onCKln2ya0ErTW3U5rpZQ0bdXEpyKqRFEVhFD4ojnsGu4cddw76vCTgtgirCLqRCqR4lW9IMmCjxDQvPveQ57cCPqPXlGo4I+cMkXMRY2YpZt5poUW8XoRmat0UMwVVV2E1T/tQyXeHGpUDWctJEspyCzIAkzWwOuoC6kUSim6rmMcR3zvq6RMCGKouXF7b+J+gln/bf9a8nZy+OYmZhlyLq9fS8i9PJVZ/iBfNzmo3zu53MJC9q+5n9Lt3wtUj5M0uuYH5RppIkQl/CpZ8z717L1Mb3ohqZmgkj1xthBzqGjryjjk4tUNU5i43qwpJPqpwoNeXVXwS0yFkiGkLZRadLemRStN11qM1oQ7FwgE7z14hxJTDfUNNYLGtpKbXc+RO8B0kYOuRRfLwd07xOWC1TsFoyVWFL7/uz/gO594Hl1OnJ60vHP/BGssQxigzFNAarRKqiNNtDb1RhvHuTAWxBQq3lvOiz4tybEuCGN6LXa2okOJEaMaOntEIxq+/sE9Ll7tWN/csPiXf5X/MPwc/8z8U0Jua6dWJ4oWGDFnV5Z95KCsncvGIchVChsEZMF2OxFLYtEIVtIipsz/+62PKULQLFqG8Zhdyfw/3/kIIeYIEQQnnaVrNc6CUZpV63BOcvfQslyscKpKGouEMEuqffRoqTk9OqJtGqahr4WRFPwb6ocoWVifd4SVgVxhBAjouoYXz15wfbljGgRniwXGGBANKaoKCZMVWCLka3LvHzhBfnR7feL84Z/9SeSoe8BNKQgl8SXz+JPn3D9s6HtIOcxVZOZgucC4wvuP3uXP/8UPyOcfMlzvsMsGZep1AFbzdWWHjKCmiqs/ePs+65fXHN9ssEYhlha3HhnIiCxQBYIQNF3H0G/IKVb1gqmTxzRNt3J0H+L+RoV2lpgSfvJIoDs9Ypwi064CnBaHK6QPFdRldUXQp8hu2PLqxSVdt8BZU8EOJM53G7Sx5FxlxyXVfLoxZRaLFaKzbIQkX+9Y3pWIzuGTpZBYNoZsjmF5hLMdJ4sVsk1YLEl1UDIyDWgVUd4gtMamkclPTMNI6xxSKFxaMBhN5Td7hDEoURBTJPlIAHZyIJGIeIoQrEfPMEwM3QESsEajnOWtu6eMXU8qnkdmidQL1q8ukWSapkJFRgpjTnjtGHaF4gNt2WFF5tHbb7HuJ568OmezG9jtJho0NxvPR0+fs/MTPiUyhl/44CE+BJZnD2gPj/j13/gtnj29YBwmnNQY47hYr1m5jiCgayRaWa7Wnmk3cXBqaTtDwwJTDK0dGINgPQRiSmAlzaLll3/pF/it3/wmjz95zOmdhutXE8lnUg4EMaHtwP0jX71XGCSKnAUXF9ekLNDOkWOgpIK2DduhJ/pQ+erCUDAM40QokCfP2Humnac0km3Y8eqmsLBLWpfxMbC+uMa1La1bgNc8f75msx2YTCVGHi4cy7ZFSMdNnxA6YxuIPvK9j1/gnOLsbMVuSPzwwxdsNzu6ztItGqSAl69eUoBuseBgtWA7jlBgioHdMKJQDD4QS2FxsEBaSx/7eo0cqh9+O255sb7B+4LR1Z5z7+iA6AvTuEMaweHxovqy/UTbNhQBWz/RWME47HCqNgG0ViwXS1xr8WHCGYc1hrbRWC2wSqKVpDHVxvDk1cDFVc9HT27YbTdEH3n70T20ypweLTk9OeR4mWreaZ/pTpYcpJ7RN0xTh0xw2FoWBxYhFeMUiX7irbsnnBwf48eJECZiTihrESyYgmc3TGzHHZNMXFx5Bh+43gpiqvaTt88cSguu14mcoR8n/BTR2pJLvWYIalO8PWjrZFGAKhKjNUZbhj4iSkaUGZJYSqUQa8GUAkkLlk1X/fZSVow7Etc2VUJqq5WjaWyFhsVI2+qawZ1LtSVR7WHBJwQGKQz9UKNuipjXI7kwDIEQE6nk2/tvzHW9nf3ImACRibkwXN7MsMPaZLNWY5TC50hMiZwFU8qkUri8vKlrcirddLcdQOhqoUJCkfN7gywSMRZSoK4bsv/x956fbv+/bj95dIaOkAoUiRK6htrnjKACbqSu04IC+Jz46PHL6uejxhA8f75hCqUewKnKJGMqCJmQuh5owWeELNjGkHMk5YAq5la+uJ/17IEwRdTJV8kSZox/9UjtZYcSbeZw9dosoZRI8B4pa2dFqdcIeiFrZxhyldLlevDLus4npUCIs+wyZwY5IIVA2fqAXAo3V5eUXLswtViomXXPb85rPp6EPFO6jKiTNKM0WguQAe8zQhiEKGQ8ShrClHh4/4z3H55yNRwxbEZss6qFQqMpAky7wrravVmtVhw2DQdlx8nTF5QwkorGdRalMiWDICOKIGeDz4Gc5P5bqDt8niBKZqCG2INtKjDm1usHlFhup3ZyLuZzKZBhmvztRRTAe8/Q96yvrlFK3kJk/Pw4pSpUJ4Q475u9BDST0lzU3k7+aiGW52JUSoFSVeYXgifGVAvEXLv/OZWalWmqvqLWx+K2YfCjklOEeO3PK68X50LKWRq777zVcHofJihlnqa9zniEfX6TQ2rQxXK4WHCyakl5pFk6Vqbh9LSjJMEwDvgYeev0iBASg680MoCYqmRXG42zjvPrazpr+fTf/l9RSvKgHKKU4Hq3Riw7TOOwXWbIR3z08chXuxWrk8JVP/GtH75Aypf0Y+KdOy3vnLS442OefPqKD3/tKW1reevtE46OO95965im7epkRivUHDCdkuLi8hUxBZRqAMUQRoqoPuDJZ7yvk+V6hdAoJVkdHqCU5abfkUTmL/75t/jKoxOOu8zxYsGjewuePD7n8qJnGj0sJw5WB7TOkVMiJo8QiuxHEAWlHaAZfUALiZM1+kSZOo1SJM6sBmkpnpmyp2ml4vjY8OC/fo7Wjjt/7iEiCi5veqYSePedM3wInJ9fELxHO8dJu+JLDw+Q1IZQCvX4EdoSYmQTIcfIcHWF14qr6yu6ZkHjOrK1+LADtugzi0txzvdM9OPEbhSMu0v89gUqb5HJ0HYDXTsirCG6BUW+Hke+kX7747cfJy99s5B84+f/SLg//BTAqnH86197j4UT/OPPKqX6wd2WtrFo03L+fI3rTjg2Ap7+HtvumPXg+R+++QE5Jf6LD37Ag9M7kEfUWBcPU5nwY2AVBK1uKnVTwMoe4vQNQdVug3GWmCOkxKqpkQmESImAExQNMXiUECyXHVnCuN6i0RijsLarcUJDT5wzE42ymCwRXUck05gOQm0GiUZz74vvkfueMg44I9DdCe3yjOx3pLglZtiOBpMdpgyMw47tek1xgu1F5PDmnKurS777g8cMU2F5dMSHn7xidXxA61qMdqxWhWNnMAvF1I+8etGzy4b3vvou0m+wISG1oTjH4ckp2+2GzhSOOsdRY9mMhd/6/ue8/fYxB8uGUmpRe+ULMflaWMeEMg2Hi0PW6y2Hq0WdUmx6rJKEcazFMi1FOO69fQ8tEz/88FNyhqPlgpXI+JRoASw0TcdCwVd/5l2mqed6nKonOwZy8gxj4NW1Z/QFbMLqhs2La7736XPu33V86eEB8fIhT+4eVi8zijDA1eaK1WnHFx+8x8X5Fb/9u9/BF/i7/9Ff5+L5cz757Cm/8e2PabTh/TtLXt0M7CYPJbPsGlbdiu/+zrfZ3GxAGC6ue7RxqHaiFIMcMjJEnIGulfgJhjCQdhv63chqcch0sSbl2tDV0nDVd4RhIIs8swXAxEQfMzhHax2uUfTDJZvJgAIpd1gpaLSiaw1+9FztBkpJNK2hnzwUzcIKxu3AZ+GKKAQHjWO7zjyTa5SIdMdHsI18/nyN0gprFSfHS1zbMk0jGUFnFyAD11cbzs9viKE2x52rsViLpeLBg1OsnvM1UWzX1Uv37PyKTT9hli1WHmB0JMeM3yQ+Wb/g+OiwymoLpFAb00bA9dUNo/d0jaE9OiS5ht1uy3S9pnENd49PuLtY8uJ8R7eytAuQMTIO8K3PLxACjo4PWC0MZ6eGe/dPSNHw9OU1F1dbGq2RSvDp9y+IURA1SF04XOq6lpgn+MuDlu3NllwEmyngrEIKSz8NXK03fPzkhmGcyDlRZvtU41pOjzsWneHdt+/WfGrTktXIePGczXZgnDKX20OMBC0LWguOT1tUqytNPc8xWUgWrWP0HmsVzkn8NPH500u0bTm/uMJaxZ3TIxpjWXQNrW3Y7QZubrasDg7wKbDrR0TJWG1xbYOPie12CwUa2yClRClJDBFnHcuTFSVHhEq4OXIqpkSRgutNhSv1QSKFImVBTBkfAkabOsUeB/zkMdYQgme1XNA4g5SZPkeKdGila8M9ZqYQySIjbZW4SiFg54kxslroOW8a8mFLTAkp6z22Hz3D4OugoiRy0RgpKVrinKbs7RQ/3f7MbT9xsZhvJyTUxYWs5tuY9lOY+l/XKJyziFJojObe6aoeFAJ8SEwhsetH/BQIqRBiRRojJFrNEqssZ6TuPLmcJYCvF9912w+Y5H7WlUDk/WOp4eZ7SeU8pdrLWecK4DY+4TYWgz24pFJIU6qfW1CgVClBztUDWbMQJSrW4jSXPIe0FoSqkyUhJCVXBLUQgpQjMXpsiDVmBIHRBqUg5urZrP6VveuhypA+Q7AeB17cbAjjhHKWxiqc0UilaJcrGmMwStK2Bms1CM/nTy5QylCoPpta/NTFqZiLcKnkrc9z/72WXOMuaiE1y9akgDLPF2c56JtL1jfQOOx/pcz7p3ofmWUOryeTt0vUN6Z6b8pA/9C2L9gQr4+6Uiez+/zGUipkxjQ1EyglgRAGJfZZn/Pz7DW0f+Dp67NKoUBUOe3+E6YZyvPmlEYI+RpwEmsmpzWuTpxzBWoAKKFvj1ataoNACjUbQeemwv7UUhqzF3SrGaGdEtrMHcVSvXtWC44WFSxCjtUjowX/09/vSOWY//S/Hyuh1ApOuxWfuMDB8ZKzlUCpHt22yBQIUXN27Dg4amlXR4QYeVImfMqcX+2YfKLfDiy6hsPDBa5p6dqOvlVQLBVEUKerMVY5N0LMaoMq15EiVTKor53sZWs4PeyY/JrOWgSJm/WGzaVnvctkIXj5cs1f8bUZpYCudTRGzQ2qWSWg6/eUi7j1KeWcQNdGkdK27sMcQdSCNeQaB9Lqgk+RXQ/GGlSrK3wqJ7SsMSEhZbyPHC66GpXQarSsjZBCvpX0CDnHyGjNslvU5kRJKC05PlrSuRajDZ/1J8Sy4tGp4tk6MfTTfAQKTNPQRINRokqMd1tcZ2vEzm6A5FEz/OpPTByfj9W27SocSUo265s/8pzab/+7ND/y8/q/EBMfP36BlbCZAoss2O0MIQiK1rzz5Q9499FDPvjZ9zm7U7PaFsuG3z5/SMqJYfo2w7gDXaf6JRdKkdXrOY2IXOV/KRY26wF8pv3df4AyFdcvSyb5WKW+ZfZLz9d3hayT7JznHNXZVxx9BVVpNeeLVV+wkKpeL2JkO/YMOdBpSzN7uf2uJ4mAzlX+pZVApEiK4H1EiTo9aDpbF69TBUMYk8hOoqTiwNa8woePBFPMSGPJosU2pl4zhMLIxKpzmEWN2InZsUATwkAZBii1ibq5GXm+DeRhx0Gj2C0du64lZMmr8wu0ySh1hJwVFrFMSAnWNmQVMdqiZ0XN5AM5F0bv8are82IqbG/WIAbiUYsUqV7PBZXASyKmuthWWuODII2eLgyM00CZIkoZnLN4n2lbxT3dEKvWnJgLcljw/sP7HB00tEbyc994xM+kRCyCaUpMQ6AfR9pOc295wMWdFllGwuS5d+oQcUk/HvPO/XVVGbUKNSUc9dzvWstBZ/j88+dsek/vI8Zq7t6puYBXu4mYEkpI1tvArl+zCwOZBDKxbDqUgH4MIOHs5IDTZceTi6GCvERVqSSgcxZTKvU8hkhKmfVu4kuP7hG14vGzV2zHiSQjgYidpZQpJVxfA85TTJRWk2NknAIJ2O5GjDasmppr2McKmMtZ0Laa5cJCFkxxYNvvyLmg5YTRguvLG0YfQTmsUxx0Ve7Y+4lYKjTG95kQwDiBMZKQK9V8GmONPhOZxipAsd5Oc4NYo6Wic7oeU1JjQyLmhJGQi2D0nlIEzjikVKyHCV0EN0OkLz16PSJKIUXBOEX6cWI9ZRYLy/l5VSg0dkU/jkwhVslqEdhGUabM1WZLKBEplsQYKs1z5gtUlZRg2+8Yx0Cj91CbjJDQtAZJ5SFcbzc1vihHSjGQ5yKYSBGSs+MjjpcLppAIqAoOlKJGvonKC8il3CpXKAkfJXEKlJRR0iBQbN7OvgAAIABJREFULBeuvrc7K4QQGKOQRuFjJvi+NtEFeD8hTM2wTjESUsRvt0wxMw1+bn5HwrbS+sdhrFYeZ6paqhQGndBaYptaYLfOYXUmhLpvBRWc07q22lVKHQK0VlXJdSpMMSG9xEgIU8A4Vz+zql5KrS0h1n2dY5zvPwkpa054TnViaY1FaFGvMaHgQ7U95TqhqNeQUtdvMcU/vaXip9u/su0nLhaJNZfp6GDBgwdnOKvpx5HNessUIgiDQLJoaoTF+fmagsDPE6fsA4iMFpkYcj3wiNXDlOQsxayexJxn3eAc0M68nt7LH+W8AMo5swfgSDH71ISYOyLyDVnh7D3b67DnjBohXmf7pZTIFJzTpPDGtElA27TzIn7OhwzVG7UvNuRcFNaTId4u0EpFRSIV5JCQQtIYUwlTrQOqRl+qgjKCySdSLHMRXjtF9bMmrHYYY5h2FZoyXk/sRCGlESEyy0W9IcaUGXYTORfuHR9ysdkRZ59ZjlWmiZxhKKKGxiMUGbGvvOr+nmWbKSVElogZMFSVlfN+mT9nKpU4KuturF42VeWimVkSPHsFhaiH4D4hJIu5hJ8N8PC6cJdzVV8XhRJEJqc5Q3E+LGWd8+7fci3+CyilaJuWyXuSzFUGlyuIaC+rYy4MS5klpaLCcFLKWF1liOUWqvMHJ4tzLwNVRJV0KYE0rnoRW1s9kHupKpJYqBlJut40rDMoqzg6PuToeAlGMpaCEBqXMpIK9SDX6VXJhXbRkmIiRk+aKW3j8ZJhDPTeI5TCNQZjFE5I3n2rQcravWvetXzryee0x5azk7YurO6eIMaepusoucbALFVLeHDG5CO73chu8FxdT3z27BXWNLQLiAH6Hg6ONNZ2rA4aYsgMvWe5MuhZcphSJKVCSpLtsGOaMtt+qh6vPmB1Yb31FCTf/v1nfHMGC2x2++I48h/nVLuWWXL/rOVwaTBYrE4UCZ2t+VrbvtJlxSzRlAaErOe5EpCFYjsOxOQpsTaB2uaEIgr9JqBlpO8Dnzy5ouTMnABEeXVNTpm37pywbA0Hy0TIAx9/EmmcRCiJdY6ma7i+ukIqjZSaXKtOtJY0TuFjYoqJf/T536Bk+F9+8df43jd/yOMn5xQhODw+5T/7L/9z/uF/8z+jbUOWluubARO35G0gc8OhgYf3l/UiKdR8w/4xU8NZJ7A/r778lS9zeHSEc45/+iu/Ml/b3jwP/uht3/SBmi/r3TFnDx7w7x084OWzp8TRc3J6l9N3H/J3/+Yv8fCu4e5ih4gJHTJNa+icJqM5OOkIDLjSUAhkUZDCIA1QRoSItaBLmc2zLSqAkh6tPUZqjlpFyIE4JrbeI7WkdZoSCzkWyJn/j703idVtTe+7fm+3uq/Z3WlvU72rynXLvS25wUmIUeIEFBgAovEAoTBAFkJGCIFgkCCYgJQoypgwIJlEYCJIQArEQnZA2MZdXHbZZdetuv1pdvd1q3lbBs/69j63DAxqQg1qSVf7NPt8d+213vWu53n+XSmJaRqIXmKRUgZyIo0DMScaV4vxUspkrdGuJhwCOSWmOoEzqFI43B7oCyzblra24D1xOBBLJUi3VWASQ9hinMb0CV1ZqtoR8sRqVdFpR912LM7PKDnTtvXsjiy5Y0rDzYc7LGAWlqA0j18rWF3ofU+lz4SxkDRD0PSHA061LKyYdWgNTVJ86QtPqayjNeJGS9H4HGhdS921xBCIGvppz7pr0Fazn0ZGH2m7ikXX4kpBTRPJB3JUTCFxcXoOShFyJJNxmdl2P5OmxJg1m5stIUamYUIpja8qxkkyX30MhJRY1A2DDyzWNY+erskxM06Bp4+XWFMRS2GcRiDz6PwUXYDoiemEn/i+J8SQuNzveLQ840ufPuOf+YnPE4tjH/ZMvSdMmVgyWheWbcVv/fGHeC8RVksMbz46ZfCer7z9nD9695K6ckQqXlwPRJVkYBc0VVcxDj1dbbDWUVU1SWuurjdkrXE2i6O7dbzsxQimroTC37YtWmsuzlpuh0BnJdNYMPpECoHb2xG05o0nD9htDrzcD4QQKMUTYyBljXENq9YwTIrtLrJYaG42B2ICZ4U+nItmmkZCkQbC2gZnW3a310BmtW5x3nJ1K0wepRVtU2GUIQ7gw8Rrb55KgL2WRjAMA/vJY5zm5KSjqSrQiuubPde3Sn7GTuiZ06SwFiorjdPgA2eLhkVb4yePjwciOwyW/bBnGGQ40SxajIJ111A5S/KejY98c3tgt9twdnKK0RqnFYduoLKa1aoFq8mbicNwoG9bSobdob97/z86P8U5TaWQjF8Ui67BmII1hqYSaqy1DvdMYrYaYxkPgefPtuQC7dJQGcvJYoHpJNbLxwFjLDkq/BS5ujpQkLxfVKKpBGXsgyeHJMOY0qKt4uRkjTGKxaJmnALDFAgzqBC85NIulgtCmMgxU9U1zjREH9luewYfcc5RNQ1QiJOwpPSc1TmTruingj8cACTnWkv0WJzr2VJE69tYK+Zqc32zbGuapuVw6O9kM9aIE7/uFuhZkqooqJxpK0s0lhhlaBiz+HRYZ9nte2KS58E14hOyOYzklMSbpG7JMUKR+rgU0ZtPQ+ZVB/3vHt9Zx7dPQ82GxmnO1ys+8+YTus7ip56rK8cweRQVRjtikpycy+3EYQq8cyVW3YRM4xRGQQhKsmzsiNHV3BxksoKMoRhpAlFywmVe9HKo+y9FnBULkI9gYslzwPwxQ14Rw9ywaEELY5HG1GjZVLTR9EOPD4Gua8ghoJKghtZqHlycCg3AidYv+yAbTy2bpbEV1lQATNMIqDmAW0TKpoJ+d8Bqy/kM93dtI6YQMZBUwNUGH0DHhB88/Ri48YM0FyXz+tkZ56sVXVcRVSD2FUVl9v6GmEVDkBWMo+fm0lCC4s3XH1I+Mnz9w+foosBKWG0xed4JNLEUVLnX5h2NXoRWWmQirMW5f8ZdieKQg5nRL+GwF6zSyBUSh1NBiSGlo3HMke4p2YzqFeTuY/f2+Lv577QSYTRFIbwzdXe+Rr9Chy1zQ6ukUZ+CJ+dESlnsrrWRuAkt6OidIc98XkfDpGPcxsdcVmezETWfy/3lksJTaU3dVDhjcEYzhQmrLe2iwxjLMIzELG5gxRlwoJxmsepYna2g0ljtUKrBpBFbAto7VIqYIpPWplsyThPTODENEYtFDwmlBz7/i7+AxH4YfuEfZxTwuRdC8X324/8Y82O/xzQcWP7e53j9a39R8gBtBVnQr5ISJSXe/Tf+JtseRhw/9jt/meXmkWRqzhrMVBLTGPg/4j/kNz/7d6lcy1n/Gv/8N/8DKQhai3NWBjRFHG3/wWf/Om/HjxiGyE9+9K/y/cNPC6qkmCNyBBB6T32Dv2b/Y8gVWmX+y/i30eaoC1N0S82itdhc4UwmlsCydQLOoilE4piEWpOF+urDiDOS85lzIiZpspWu6Nozulpjfc+v/VsPiAoe/WdfQ2HxthDShNMWbTQvbntCrHh60VHXDf0wga3QOaOIVJViHBO5SIOskEB0rWFPpmiDMooQZeAx7a7w+w1778kKFusV/9y/+Jf4a3/j7xAwRCo6bSiz217JhbN1x9OHKz42jP1/bfTmwcb8vD15+pRHjx7RtO39nn5kUnzLZ/z5LJmdryKMSim6ruVP/dM/zQ/94Pfx3rsf8n/+2m9wuz3wPZ//Ht76wR/mR75osYcPGV68RJkFZtGSg2TToeHiYkWceoyHEpIMiYwiRAVGYZXDyxQKVyp06UXjrOdraWUwJ+ZPEVOgsrIXpDjn5qpyl32qUOi2FhOOGCXvd1WhshhHlJJRtWW9WqOVZlJZEMkpUhkHTYszMoX3cSSmRFM5VG3pU2QaR3bbHcZa9AS2dWin6YcdlR5IrmFKnv1+BwneeOMJqIGq7qiaiqqp6TX0+z0qNXhl6CdPReLizXNOVi0li25ucXqG2mwpVt7FpEIMI74PuBNH8plxtyP5AFGzT4W2caikxKafzBhGXls/xlaWtJVnoa4cVsnw6mS9IgWxuB8OI1o14nqOn5EVx367lWZZGZp2BQnapsMYKRJTFMYKKjP4gX6KNG1L1dWksWcKEEMWyvX1huQhBE9Wnqo2LKMgFbqrMWSWjcGsFwQ/UTcVbW05Oz3B1AsiEzoVfMmEkjmMgRQKTz73GrEPpNGjdyOVgkFBwND7gnGGxemK5uWBfm9JQUxdggqg4I0Ha5yzXA2R9y73HHwko3A603WGqrF8+OwGUwpvvtay6ByPTk6YYuFr719ye7Nj1ThWC0eFwtWGISkubz1NY/ji51/ja3/4Hle7CTt7FohxnOK1kzNWy5rdfuIwwpufOGUzDPTbkdRHyDIIzXFEOY2rHW01xzw1htrIwD6mxMv9xBgijbUip1FaXp9l4nZ7zThFmmpBVzUYpUgxEJNmZyd8TJiS2G88w5TIJGIcCD6wOUTqSmG0xjY1rqn4oU+8BrnwwcuXEvmxPKHrWtIQiNNA1hpY4H1mcp5FXaOKEqf4E8t6ecLJyYrYJ3wfGXYTudLkpIlJ4ipq69BaUzctCSQTOcv7Fw0ny072Vxx14/B+JIaIz4VkLdkVkfy4BqMV/bDno8sNrml5aB1JBwbvyUWG7rUVMCH6wjB6+mEkF40fE0olzs5O5O/jKENxY0lk/BRIqUZry/5wIPpIDpL7XC0qjLVSG9QNzhhud7eUDE3d4WxFWxdSDDR1zaKrUDqzaLWgdkWJOy4JHyMvDyOHfWboR7Y3vdRr1hBTou4cxmi6ykHRjGESt9OSaeuGRmnOTtaUEiF5oGCqGq1rhjBQiqCnfgoMWZIFjK6gAoLU1hpL7/ccjf/SINdw9BGjEnVds1hUDHsZ0FptRcpEFlXb/5de/rvH/6/Ht98susitL2zffcHXn19jTWG1aOVBVRrthGq6qBxQxArZwZ2DaiziXkehahQ5i+aoHPnNSiHstYIp4vBU5gm6Vlq4HzN9tLyS2aeOk/XZrakULZQIBKJXQGUsORbSJDSarJVk4pXMbggUNE5JI/vyxRZVCkYJfO4LvPP+rUyE79DL2cpeW5TKGCvnWOY4DMjys2o1O25aShJakFVmzi2cO2GK6DqdY0iZ1hjWbU2hMIYRYzNWVYTecL30XDxoIEZOT9Y4pzmxS2LoKDHRLSyq6Xi0OKdkw2a4ZYw9q7qT6ZGW6+aKI80/iykz+qmMuM0ZKRBTlqapqSuMkpdR0a8gunO0hUJTz/TMeydQuXdKiUZCz5qoI21MUEh991lyFQTlu/t9KRIMOyO2ORdSlKmYGP8cm04ZBBiBJ8iq3H3eMAzS4GSx3DfOYLWi0UJjm3telIIQwl2DqLUmpDg3t0f6s5odUF/Z3Gaaqe8PQGEdl3RdR9Y1Q0wM/Uj/4TWlyNSvMkbyC1VG2QZrG3z/OzRdQ0yJ/TChgfXpKeTCNA5QCikLAv3g/BQ799aDH2VC2DU4q3kwhrvrFpMMLnZ7jwI2L68Jzw5sbq7Y+g0lZCwKneMcBSMTWoxh8fARL9VANxVsVWGMRRcIKTGb1JIKHFLmt/7ghlS2vFEafiYJxfPgEzB9bO/4ldt3+NA8pxT4YhqZyn1SpyDMx5GPuCkb5ekahfaCZoVUoGp5elrTOMfmMDHFgTxFJnWCsopFa3G6odc9Rmsen51gZ9e6UgpWG1ZLoQQNfU/T1TiTOF0v+MSTz/H7PxRxVvNf/MIX5oZv1leSMcrgVY/ShpVbUrsaszRYJcOHECZCzHzvF1+TaB8/UpLct5wLIUHtDFob/pvfnh1te8uf/rM/yf/628/4D/+VzI//+Of5mZ/6HznsdoQge+P67ISsWrzWOFv4zGdW6PUnBUnPCa3KHRlAzRTsO4r0TJfO83V96wtfZLla8PLy2UyvNpiSUSUTtRKIf/7eny9y/34+yfpRSIG6mODhX/hJFsNX+f43HvBn/ruvUD3b0H3wh7T/+/+A9QMFxZlxxJ/8ATb/2p+DXHj8/I/4t//nv8rp37+CV0yfjkT025//l+Gzr4GPpFywFh6/UcOLxPaLfw+Uof7jf4n9Tiilrq54revwwZNjxCqFM/IOCCHMVOhMNAbnpTgydYPTjsurPXWlcFYxKc3NzZar6xtOa8PFsplZ4Yqzi4qYNcVAURYXDdFHQbMXjgWSo2pffzwPxJI4tWp5d03jSD/0rEzD40enFGCKisvnN3z2zUfkMXJ7s6FqFc36DDD4MdARcbrCP7vl+fMbxjChlaJ2smc5V2GMmU3fBLGJH0jjPU7TzISpUUrh/RarxHQrjoFVtSSWTL/fY3LhrG2Y9gMfbl/gXEXbSlP29LVHkh2LsBWsqcglc3N7S/ITy3XH2fkZ4zCy3dywtEtOFieklIghYteOMAVO2iXOWAKefujpVmuMcYwp0XQ1b77hUMqismjCdO3otx7SRFO1+Jy42m3JYUe37EhT4LCb6Mcr0Fe4mZGSVJbIh5RwXcP3nndMVSaFivrNNT5nbGX40vc+4C//3E9Qdw1xTKgQqd1sJ14Uuxi52u1xWHJK7LxnjJn3/+gdnl31fHC5Y7VqeHhxwle/+owPn1/xqU+ekkrhN7/yvuTVTaIx60PkZjhIge7hrS99ki+/9QmuLjf80v/2FdCOaeo5PTmH4vBesnWTSry8veV6uwOlefZSs9+OMoAyBlcbnLaErGiMRCldXfYM8YbPvvGA3W7Hhy+2KGPQ1lHPAzmtMova4pYLYlzx9rvv0tQ16FtKCkjqj6F1S1bG0ujElBQvbjdCz58KJ/WC1y86skrcbkdiKmJYMk187b1LYohUjaZqKnZD5GZzzY0/UGtLZQr9R885bANX44jTBjvHRBgj9dL5SqiR1sDmMKCdYtUYXmyuudls6FxDSRUpZZoWkckoxQfPb6mbzOKiI6dM2PcYbUi2EIdA281xWVkG3En1EqPRWD7ziTOUcbii6aeJXS8uuJVSnKw68ZLoIJcaw5K2bXjxYktIYhKTo8iEnK3xQ+ZwEHf92hTiFBmiRxs4Oeto25o4BYIX34FD30MpnJ6ssJXhsBONPxpWpxofera3vTCGXI0fR4b+gHViXpdSYrHuOF8vycsl/crjfaIfDhhdY404ajvr0FrhKjub8wFFjICebXZz1nZEq0JlJ0ATpoCds9GrWoZ9IRaUivgYSDnhnETJVXUzG/9lCpqmarBG3nl58rzcXuJqhzGWxkmzmGLC68JdQfPd4zvu+LabxWGSieGDszVvvH6GVYmT5VJslKuGdrXAOovNnpQj7777kqvrW15eXQOGMTJHQhSCz+IaZTJ5pp6CuDJBJuWIVgalxJUUZGPQMy30OMU4VvrSnMzxBuoYdaBByQs2lox2woHPOaNSRjlpdRqquWYR6mROmagUaW5mdEZs7imA8ONzithiIAo6EmKcjU6SaJnmAh+grh0xTljE5r1UCm2NaB98RJdM1VS0zmFdws0bUEEJcpekaVJrS7VYoCsJf+0jaGXBWJJJxOwZvEUZTSmaVDQvbxNXN6PoTIwCA0WLhtIqcdgkCQprrZGJl5UlIi6ieQ6m1/OmfmzwZkRtXhsxxjnUPkv2I8coknxH/zXWio4v3Tf6wD0ldG40X9Ur5nKkGau7773LSDyCgrM26RjHcIRdJKbDzjinkmiD2ZGSI/XuTn+o7prXO6rpt2gmy7c2iojBzbHxNVoz+QnvJ1l7s0bT1TWgsNpQWU1rNJBEp2odV5sd7Hu00rzx8IynD89ou4rJj3x0mdjsBm63AyjDFDYoO8eLzJt+PwQqp/mffuo/5+jV9Pf/vVNyzvwLf32LNZpoMhcvG6ra8NvLX+H2R3+XqoBuFaa2JK84XbWcrhryNwf++O1rvvKHH/FL5dcwJ7ISx1xQ2XP+oObFZuQbH21xg6IkeJ9v8O+bn6MUJJ6Gb9GcFrBGYkd+0fxX/CJ/a752ClMSRZm5yc/oonAGrFX8lfbn6MdMtI4np+doZTkcPOPgqaoa07VC+zSAEi3WYr1i8oHNrhcTEyfXv6SITwPGGpLPHIaetjXsD1sur/Zc/8wJWit++f+6xBmFRUyQcjWRDpGooHKwrFoq7Tg7qbGuEVqakcYnJ2mqIuLsbJxokyDhqYX+QJLz9VsW6hFPLi44PVly9dH7fPT277OwFkeNLpUUskph2jW2UahuiVl15Lmpvkuq+Vb94nENF3CqkJTm+77vy1ij6Tfb+fsLSRWhn2cDJFQ5irmPjWMBjpRzgzKGph7pn2851YXaFvaHHcNw4NF5h5mHUWRNiRlbLOgyD9pmLfl8enLKUuxZtySuGhKB333zk/zws49IwaPHQKwvQUEKI2iFqcUhO8YkjtxzPM7xdJXWWOew1sl6nIePxjmsrRj6CWsqnLFMPrDfHLh6vmH5+AJ3sqDMw6UUMj2w2+/xfuJk0c261YLVmhKFuprGncgmlAw2ZO1bTEp0phJ36HhkYhQenJ5CDqTkxejHOZzV5FwwtThRClAi7op2lIGHArpuKTT0LFnDIWXWiyUle2RmK0rupq6YfCTGjKqhcpUgWBTGaUJpRVN3aC1uo1o7YpEoAawwXLzPLJYdIUxst1eCnmqF0pYyu8AaYzg9PZcA81woSnMYRvr+GlDU1uGMwdQGV1ma5ZoYI5UtLGxL6zp5XyRhHTXLlrb0+MkRijBqWmcoSjH5YV44BasUTdvi9+LQ3HQNutYEP+KT57AfRVpgHWSDTRmXDMrV6GiZrgO3mxt0ylSrVlBrZRgBUwo6R4iRtYXzZcPT7/sUhylzvd1hTKG18NannvLsckvlAuPk+eTDM067jq99sOdqf8P20LPbjzy73FGfLvj+zzwhBM/2dsd+inStoRTP86tbRl+I3kMuDPFaYhmMoWprdkNiDAm0vK+mYcJnybgOjUapTNdZVrphc7vHx0goDSpqnqxWPH1zzcvNlpfXN6hS+OTFSuiURobui8pB5ZiySF6McYy54PuI96Lf7DQMUxZ6o83UypBT4dB7hjQSS2SzT9TGsIxSvyg0/W7kxRSx2ohuPUbJdFwuOT+tZbjYe27n91+xE9E6lBajmJQyUxpo24ocO3II3N7eUorm6uClJlGGWlfsRxhjxlqFD4VxKCyWjlwifpNmt9AR7wun5x1cLLFaYmuMRiQYStacTxlfEjfXO+qmxiiRFdSLmqi10HqtwVYdGc1h2kl0GdJkgkEnhWoLSjkocNj2bK93lKKYQiFmyfkEz8l6KYjxTFGfQiBMXuoHV4NSpLSla1v8OM6xcQZjLPEm0jRgrLxLDXGmnNYcxpFpGIhhj9aaqrL4aURpzbJbiBY2JXIM1LXDOkfJhWnqcdaAkkGbDwZrDD5IPJ2xDl2EqYOGuq4IQWJAdvsdOWcePLrAakXxou3e7fcYl+7ACJSi5EAaPl4Pfvf4zjm+7WbxJ3/gk+BqDqPnmx+85NCP1LpC3tAKbR3KWHwa0QZqJ6jjOBZSGHGumelUM83PQDHyEBorrphQCD7J5Hw2q1D62CjwJ5oEEHtpZTTKHF1Phc4k5i0RZbKYViSL0QajDcVATAGlNWFuOE0xQmGtLa4kKi3NZ0yayQtsnmd0zRpL23WYOJFSoWobqrqei5g5PBZLzhljxaAjFYVWFjdTIZ2zNMt2tpDW1DPd1BhHSOImaWxNiYlhP/H6kwc8eeMBp2drpgNElwhKM2lFUQWnDSY7wuRJo8dpxZOmJ04bfv16CxSaFOdA1QVHbEc8V0GlQpnCTPmVv7mje87F57GhOjbC97rQGSGckVxVCsZIYybIM9iiZ2oqd26hRkvu0rFBS3dNI3cmNffNY7lHJ/mT9IXj+rn/UwVakVO6R6LzrOF6xdTo+PWo7TpqE52xUn/eATX3n328DjLY0BgrWZJHR1aJlGE2ehEKrs8jfhLtZ0oJi2Xdtnz582+w6ISumJTG4/GbA13d8trpgserhu1JzWEY2ewmwnQgZ4WrGrQ1VLVE2rzcbAk+MI6e/aEh58xv/OE3KaVQ5YhpHF1UvPxgx3avaeqKly9uCAmmYaJpFG2tuVgaLreey4OYJzgMFoMzIuovWGpbc9q1XA57ictJkFQRmfGck/nxu1PIxxxPNd/hLDmVaV4v0uQrCoa2zbgKDmNmGDJtC6tO40MmZQPUM9VTQn2LAadrKrdkGidylOgZlKJ1mqau73KxrCs052u5V2ROlysWbUtTj5SSeXBxQS5CH9cYdLWcc/NAdZaz84ZVbVmsOg4lkQroZIQeZGVdjZue6CM1isY6WmNBe8gBqwWlP+wHHjy45S/+uR+gO1nwv/zDX+abzz/i/GJJtXREl9nt9gQsdV5gfCHvFX7XIsq0jH5FXyy90jxWOQ5AlCLFRC6Fs8cXPDztcOUashUdb5kHaCpS5ma9AH/JrO7Wt0FiTt787Of5oR98i785Hgh5YnPVY/6jf53/9h+9w1e/8Zw/+1Pfw89+8QJHknujLWp3i3WOZ+ef4q/83N/i7/z5XwFzdKwGM+eCldxCyZTk+PU//WP8wbrm36ktwTV3P5MqWrRzE9ze3tLUFYu6ARSJRC4ZoyzGVpKIQxGNeZSsxjBEPANdbUFFeU9ReHyx4LUn30sct2x2l4JyF1ApckhOiu8YCNuBQ9szTUHeIXPQd7teYq0hTOO8jqFymq6tca2W/Y8kTpbaUbmO6TBSu5rlasU4DDLAjBGjxMSkKMV+u6PkwsoaSoYUI6k/MKWIbWq6RU0cLf1wEESnMjKQRKGUwTUKlzLTFDj4yGK1Yhh6jFaieU8BXeTaqdZic6TRlrZqmHyPNor+cEChODk5wzrH7fYGHzVDyty+9z4xRRbLJc460n6QLDfvAUXMmd24FR0h56A07//B2zRtQyQyDAM5GVzbsN8PvLzcoJzl7KTiQbvg6cWKrAsYhWsWDLs9ZBnOhWmiHEaI8oyn2w0KWC4X1AuJmen7Az5MdFWHMoqXzwbdG2wCAAAgAElEQVRIAR08WhWWazEgKZswNx0a30/kkjk/OcVZh4+etO3pGodpNW17jtGF2gCq4lOvrWmMROpgDT4P/LPunFIScbwlBzHxGwaHVYGswUfY7UewFS+2W4bB887719xsezYHcdPNcWYIGM1wiKzac955/wofQFGhVGHqPWk/URnN2UmLaTUndsHVfs/Dlexv+37gnfc82zhhTYPWjudXPVc3keX5Q8Lg+fIXXufhScdv/pN3+eB2Qz8deLHtmXzgbFkJ6jQFKqW5vtrw/KNL2oUlTsL68qowjj0XraKPmdttEFOZlaOpLZ9anPLN5xuS0vzw59/gZrvlKx/s8GNC28zgAylKYP3X3n6PWCxaO9540JAmxT72dHVN62qwlmQrYgI/wnJZs1q3hMNI7j1VtCilCSEzpcR4uSWVwmp5inUVrgPalj55uBnJGa6uB4kTK+IOe3pxipvNsPwUYQhU1qCBcXPL7U7YPydnNc5MhElYEtLTadqqYlFDH3aEohl9RmtD6xZ0WhFKAD1hc5EIM93y4nKDUbBoGjHtMZZqURFjJKSA1obT5VqG0kpouaXI13efPxNgZR68Ga04O+1wGrrWsV42OOeQ6DSpcY0W+dRhP4hmOCfGsWc4DPgg+/7ZxdnM1Ejc3O7EZ+AgjWbjHGYGbYy1VJWT5k8JY8+HxNtvvwdKcXqyZtUtWC7WWGfR1jCME/u+Z/RlNv777vGdeHzbd6ZrHVkbQgBSIvtAqQ0ooSTGwYOyhOyxTlOCNAXOaDQGZ5iDWzWTF7cxrQwYLeYgShGTNAh3heasSxRN2REMukd+7v6Du2gG9KyV496l1RihiTqtcU7jKsPtNs8GOjOKlSAXPWsdDe1sfBOLoqvFah0rjpaOQNHgRwk87doO4yRyIwaZzmtl7umxzpGyRiuLVVByoGksbWPnogmhg0WJ/yhKcrliUijtsJLXzDiN7Paafso4XVOUgWxBGbKtyFiSsuBaMGDyFSHO0y41T1qTQc2FDrMO0HC8gOoOsc1zGPYRhYAjpY27Rp47hPc+hmIWkM2NpJbsMqPumrBXD4nXyHcUhmOzdTzuXFOL0PmOGY73a+P4jUc66z2yCLzyb7hrUv+fjgJ3jeJxbd2dr1IilXzl3F89L1k78Q5NlIb2vunNOYmge6bemRzJGfY+EAPc7gZKkjV/cwhMITOOA01V47RoD8eQ6IMneKFvANSNuHAKXJyJfiKnJBorLfqUk04c01rTUSqDKo60nTg5WXB+vmTVOabSEIq4PTqtebiA00PP69Molt67A37wjEHyMGOUc2orQb0FfLpHgu88de9Q21euWZnj714Z+hyX3AxfoYrGWrmXKSkKGqs1Vhc2mx05G3FUI6AxqEFclYwCVQwhRhRga4eOSYp7I0paM9PdtbnlSIJs6htaV7E9nKJR/P7bt5JNFcIcmyOGFlonirN0Xc3CKapFxUSSl6etxCl1UZNiZDqMaBStqbBIULV2GWcyPgjquI2acrVj0V3ws//mb/Gpn7jkv/8bS7qqojEZpyLWdfKMY9EmYilYsrCi7xrDea2/0i7OjyRaazSO9ekZ3bKjWdcsHy9BO7QKd0jNn7wZ95+rizSLDx+c8YnXHoEf5+gSR5UKn3hYsTsseLk54HmAA6L3aCfGWDknPrEU99UQPCXKvqB0JpKFkh8DSjlCHKkby7JrMcaRjeXO7dRWuCSUcMltq0RSkBM5RdHUGlAIHTfGJLlk+Xi9ELRJFzLzAMsIS8FZBUaRrQwrFDOLIjoaZ4nZyYBHG7ySgVosEiOUxhG0xY/jfNmKuOm6Pe0syTAKtNEoV+O0kSgLY6mcIHi5CFVZG0VllESyONHyWy00cLIgyTYFiYwq4JwlZ0dRgZgLOWq0AV0ClavBCOsjRXGB9cNE04puHV0oSpgXKWWKCIg55IlxGGnqGq1k4FmGICZ0QVFXlcRmzeZrOQp1UhgjGWulIHXGzU7pEe8DCsvJeoW1mk3vKaVQtxofRwqBdtWibCU579Yw5Ti/egw5jxiryF7eFVMUNkf0ipAipURxCR4DhzHiiiOkSIiZzU4imqag8CmgU8AZzbhXGBWxen5OtKZG43NmP3gSEhFltCEcJkIpJCURUE4VjA0orfBFtj9TV2ALLh9QJYtDtbaoHKnrQgyyV7SNGK8lNN36jGE/0NpCPyyZQmYat5QkLpaJwjjK4OWPH68Yp4QxNdYadv2BaZTreJgmeu85PVugaxlmx1KIWhx3TV9IOTIGRT8ldkPkT/3Yp/nwvUuubvcc+pHNMDL4ArNuM6fC0E+Uo1ZYw6J1qNayHyfZN7TIK6xrqawwwT71+ISQEs92B3ofqeLIupOmZbVwXN0Wnly0MGb6fqIoqEw1y4KKGE/lRCmSySvqH03O4GNmGEZSSeiQOTDT/EOg0kreqcNEHyTvbBgGid2ZdrgslMn9fk8hEiqpZ/oporKhpICyikM/ilFNCJgCWkdyLc/i7iARHbaqGaZIUAVVBDXtfRQ/jJgJwZFzYfAD+yGgjSE0UClLKlGYbgqMmbWqKVNVVqRXSYaaJcnQ+Zh1aI1EuFXWkZIhJQhJcbJcSMTZPIQsRe6LuORKc6mUEq1llOe0EAlBTKaUkr1zmtI8oNfkotnvJKsz5oT3kSkEAWW0nZllZY4Oy9hKmF3lKLqylhQhlsQ4yvCoMgadMlMMeB/mGI/qvl747vEdd3zbzeL5+pyiE8uughS43TVoI/zjGALDGJG3kwUN4yBh08UcoWvu9FGTD6hiSX5GD7MsrBjDx9CeIzrEjDgdlU3I3wh1QUsxoIu88HLJ+Cwv7lIUpSgqp7GmwJyX01WGjRYjBI1sUimLFisOAW0dB6knyArOlo62rqiNCIzbpqMfega3YLnsWDUNFHHfyzMd0lQWZy0+eLSGOMnGWtcGZTLGysu5lEJtK+qmZu8DRtdAnDWDDmUqcgPrhwtWZxXKGYpDhOVKoTGkokkFcpkoTkpGcubl7cCH1yMGsQuPxQo6mgaUEr2DNHpzfqGwzu4K9yPtV8+6vZKkJNWvaAblEMS1cGwQ5iGBUTR1Q8mJlCU38dWm8IhIpiRxCnXtPuaKKvewzI3c3WndrwGtZnRS7OI5FtFzFZ1nevCx33XWSDP6qq5r3qzkHApVJQJ09WrjCPfN9PG85uJcK7mvxmjs3ICruWG09iiyVZg5QN6okVAUdIb1cslbP/IjrBYNMReudwMhJPbDhpQSPnlSzriUWReorRU3V8AZJ81O094jugqM1ix+Vyjb3/OjP4pWggT70eNsxzfe/5CmVjxerzlp16RScXMQdN9qxe32kmkqVKXGGkdxER0ifdSgC/tenA7Plg3v5vnavmJS9C3zgOPFElQZmb6qu+unQM3X65UZQFWJ5XaIBoWlNharMi+urkFZrDEoIjkrQrofauQsujFnHXWRNRMnTyky3XWVZLZu9ztikqefoqjriu3+Syil+I3fexttxREzx0gIAVtVEpuSJH/VWsfJaQU5zM+O0NQXqwUpF8Ik2rpcDMMQGEPCVmL8885jaZz+9tf+CU4pTs5O+Zl/9wM+94XC9vZAiYWvf+V3aKaXpN0Vhxyo6gZTEucXlwzR3vfgx2fulSHa3bMx0z7bpub73/osTXyO2kOTb2nn7NmQvTSZd4MXMeYS6rfElVQoIopPf/ozfPkH38LUHrNaolRFUZEf+/IDnry+5B/96td5NnyOpyc1Vkf0HFmSKfyn/9Sv3d0fUha6VZmNaHIGv8foB3z48jlOV5w0K9TYk/KAKvN9UuLyp0rBnZyCgslPYjxiK2bhLZk4U5wjMUSMntH++R5N0RNnh2aLIsZ+po7PeYZoQdJVha1Ba4nQibMGe5mERuWzmG74lGe9z1EGoUlBjLFCEFZJROQRZRKNkkpmXukyIE0adn4iloLTihpw1Uw/86KHNkoGk0pDnPcFi+QdR5t474MNu5vMyUnNG28uub3uqasKnxL9ONA0cHV5zdnJiqapWHaiK0oZrFIYI01giB5VW7pG9sHovfxZVtTWYK04Ji7rU2JMpJl6sdnuyUkGs0M/sFqvuWjW+Gliih7nDA8vziQ2qqsEkS8j0zRS1Wt0taQfxDhk2XaotKckoBhKybRK45YVSml6HwQx7xSjHzFG45ygG2PvMTbgnAVVMYVATInXn3YST5E02lqC+Plj79ymMxRFVVUopyCJzMNVFeNuizKK6NM8FLQkNwkanw0pBjJblqs1u2mgpEAqCh/FdKeqLbWT4h+Q7OcYcdZgnKJ9dAZFhtlVK5FkwxTIRKxpqBvHy9vXJRAei3GacZTc65gL1/uB/TTRuRqUJoSJRJIBi6u4vt7w8nogpsKu3zP0kZ/+/FPeWVd8/f1Lplx4+PoZ7TAKEh6FJWS05TBNwhZJmjffPOfBg5Zf/6336IcDIXq0cjTG8dYXPsE3nu9oFhoXQd8qQp/ZVCMP1y2rTnG52bDpM0/OKp7HgakX6Y+thWnkqhqXDM5Y1os12ziQoxOK8FQYxsBhitQt+BAYponbW4NuwdmKzUHYL6DJ2XLYj1IvJS/xIEGRgget7uLTlHFU2sp+lxXX1xtCzPhQ6FqNUxqtK0IpeL9n2TmUcez7IM9qZdAafJL3WY/c+6V2+BTZ+wMFxX7vhbpJwejZYNHKeigxouoWHwopB7wxkhFujTDasjRXWika50gZJh8lim4S2VbX1iw6Ye8N40jwEWMqSvFMU8AncS89RpNpbe7kXnp+T1irUSTiFHl5ucMaO2dhW1ojjt7WOAEXSsZWDqWVOPnGeeCvFc5aqqqSPT9GUgwUp0k5EIYBpZRoF+fc7u8e35nHt90snq5OUHUR63KdqTcN+33AT0GyXJygABRFLmHWfkTi5EEVtHHkGMkl4ZPk3+RZi6GLTI5yztKIUOZpnyCPx+PjdeiM4hybARQ5iUlOmifHpRhK1kQlCzpg8QnGKdP7KChkPjYYQl0bQ4CYKUbfD91zZKgjlU4YDLE0hGCo2kocway85J0x0qCqgq1rXFULkhAHYo4SAr7oMJWiEIjThEZxul6wWi04+FFiFnwiZ00uDYmaMWYePDjlyWtrtCtsR5k2V6agUyJnmSDnEsgxEPcTcUpcT3tSiUwp4ooiR00sCjuHuYr9vmyUOR2nTndX9xUKsOAYmvvmTWn9LTdkbq7mZq1wjwSKbvEYn3G/WXH3eWUu6O8dR7WWIuH4/59/MSME5ZVznOmuRyRE/8lzOn6/aG70PXL46mdzzJw0966p6l4viVJi8PNKUZ615I62tRgSaaVmVA9iiDAH6SqU6BmcCMVdypyvOj71xlN+9i/8GRbrjlQy+2mgaMU4bPF+YjOO+JDw04BKUUzIkuhDS5xNoFQ1D0UMZEWcMp/0GbImfaqQSmJ7s6WonserE57f3nLY92z3isNUYNjx/nvPqVpwFfzOH32d3ZA4qS2Nq2gbi9aRhDyKKRWM1UJ7M0I7ebXTU0V9y3P68eOIih2L5dmK92O/P7rbzh5K0uSjQBuUNlIIFjEsEJddgzaarDJKhspzHmyhbiWzVCtNXdcUBcrJZMgmychcrpd8WEtDeLY+IjoGVWA/bGkay+A1Kicq11B1S54+XpHjgWmY2G08MRaIHmsUBx8YQ+T2MNFPnn0vRYnSGvfRX4Vc+NX927SV5rTW/MT+QNc1fPKTj/jq177JN9/5BtPtM56/2HEzHDhdnREofO7NF5y2gizk2VDofpUzA7avNI2qgMmcrOHw0R9SDOTdjtplvAeP+vgnqHL37xVgFBiVSEVjqgrXNAzs8FWDspEyRepmyWqlUXHgag+LpeWsqci4WauYj+RIVFagErkIVR80uUykMkE+5ebqhpINlbbEww7vd+Qi74iYZG80SkmxnuaCVmkpTLI0iqlkzBHxSOLWXCgze0Ha4JjFvCojA6uSIsWq2VRtduRWioRY76vZWltpZlMRqO6iS45TrPquWbTWooqa3bDjPCjR9GmkZPCHTEyQI4QgsRR+2DHFiM+KVCzokVgyu6EnpQiq0C46XG2xlZVbmxTaWEwTeXl1y9XzgRhPefhkxbMPL1kuliSj2I0DF7ZjO0xixmY0SkPJkZwjtatwlaapaqBm8g5rLbkEMbAoYs6kx0SYPLEoqqoFrairlpzhNu3FdTwbpiFxsoJFU+MQtLNqGg7jQC6FZrFGK8X2smfRdiwWHTFr+m3Pcllz0jiid0QyBYfRmmE/sWyWtF2F9yPb/S31oiZFK7urEpaQWi/JaiAnGdC1zUMOfuLioiYNntErsnPCdMoAkTAG/DjxcrNh2XasTxYymo6JmAq2MdjKMWqP0W7eRwIxBZzryDHhpwMViqptAMcwHMgl09YOrR21MzgyKQklX+XEtO+xzQKta1KQ0HSqihAzhylSyHQN5NHLHqg0hIQukWVnqZ2jspovrZ7QLjuuX24wrkITMKrQdC1OV9zuD7y8nRhD4mZ3jR8i++uB89MFDx6tMEpzuRt4fntNDjDtZUi2WKy42u0Zh0iYNOvViuWywVUNTKM456pIZeB7Pv2UD7eer77zgdR02dFqx2LhGEPm2XUvjUWpuLw8ECmgNT5CIgolPUsUVW3MHB4/CJsmB9F7Z2gby3LRsOUgpokBnIbdwbN0msXCgbJsNl5MykohWocPkf3hwHLtyBSmMd0ZKk6Vp3YNjVL0cZKMxagEXChOBtolQSmEaaBtChRLsAofZBet6gqnDaHAkBImF2xlJOM6F0iRmCW7NEbZE4suWFOwxRDjbOJXMgmotSKnQj+MpBhpKktMCTuv8773jFPgMAUokeWy42EpGMpMBddoK1Fg/TCK6/5sGFjXNU1jIWXxGTGKbjbFOkziqAp69rAQLwtnLC+vr1FFSVSQ03TLmlIKN1ciWSkZ6qYWSYGKVK7G1B0pRZZdDTmxqi1FacDw4cur+dffPb4Tj2+7WezDnrPlila3uLOH9I3nstrKy64kQcJKRqmWYdiz3R7IpRBTwHsvEzudAcuqrkEVusWJOEqGTM6S3zIFz2EY70KTZeHJOdzTIo9fpLE8UjZUKWirWbenOGdwbkYxskRa+JnKM/iAyhKxkecXflO5OR/NgrYsO0ELxyGQUmHXw+h7mYBtZIqqraGyivOTSkJ6rSHN6ObgEz4kQlBQAnmmoXR1RXU8t9kye9lsWNQNu3l6FKP8f6K3pCSaNKM9LQM6bekHePLknLayVFoadFvVVHWHUomw7iAZHj/p+PB64FevNqRscEa0Vco0UsgdGzUhXqFnjSBwr9ErxzgKdX/Nlb6L27jH5qTYz2XusOfvH8d+1i9qMcKZJ1uiX7zXIx7pJPeGN/L5x6xFKHwr/fNIH4VX8xCPjf8R5eWunsu5oBFHXF75+UCRklDyckpEjrYe6o7enEtm9lq6Ox9KuUNfVWHOeFSYymLv4iPk32IBbdDM69o0eByX18+YktCOffA4q9EhY1OiiZGKwqQEebfKUFctqkBQgZQLlbYSEG5mZzll0UgDlQwUDfrNJ8SYaC9OOdjENCY+99lPcLKoIBS2t1vsqoPWcfH3/i5f/d0PhO6qIOUIWaahBiP0rCLaYOcsMSaIr17nGV0+roPjUe4vG+oVZLG8ck2V3MeCPBvyR4qnTx7z4KJiv93NMS3pjmVgzNw4JslZyynN2XrSJEzzQEopBX0veHASQ44UZaL98tBjf8liSuGdj7b3a0ygSapqT4yGrqr53rce89rrTzldtUQl1N88SdFtHBQFh8mTCpC0MBu0QTcNpjKsjKIxHTv/CcIwoXLm4uKXaWrHf/Jff0hMhtu3z/no177E/83em8Rqlp73fb93PMM33KGmrqpuqieSIkWZlhxDkKxYhgELUAJB3iTZGMgiEKAE2QUBEq+yytbIIoOBbIIAcWDBCOAECAQZEmIloiJRMi1REltNNodmd3VNd/qGc847ZvGc77vVkpEFV1zwbKq6695vOOc973me5z89ebZBrV7y8//gz8i10LgNVhl+8tck1LmUwj/6zzs+/JZoh/+9/3jiZ38xcuBhKCXn5975H5Drh7z3278AGU4WPf/Z//IhqfzVqa6q8E//ccNXftMRK/zc30v8/V+bePDgn3B68s95P01kW3HWoJbwrX/2y+xC4uNvP2P65f+Gl/du2Ng6N/e3bIPw9F3id34eVMEtnrH8qX8xI/7CanC+4dHfueE1bZne+w+I012qcYfpwny9xUArBdEZmdl8K4TxOJCau0C0MTMtS4y6EgqliwwWqpX3LoWUZtdjtNC/UBhtMVqjSpTvMQfZ11qpIYHSFK0pCiGtGnnIaKWPQ06t9F+ia+WZXldw3uDmQWffGVKqLLseDkO0JFreoiCUngOlW8/nMxcxUtNRqGs5WN56+BoPVgFlYHu9I5nKkAOpaMZceX51w1QNV0Mh1InNPktUQioyILMGVJX8VifUt8WiRauKtkJ5nYIgjM6KkV2KifFCdKlNb2iVINbGtLhGM6RIyoGr6z3x6pr1siOlSq57tBYTpM1+5OnFgDeOxjlIhSdPn5PzJEM27UEb+kXLddyxuRkgJq5vJk79UqJlaiaXSA4Z48QsqzFilnc5XIOzvP/Na0oSKrF1FqUrMSTunJ4QwsQ4TsIuQrEbBshZBk/O4vuOQqF6RyiVaRpZrVqGcSSViFGGWAzTOLBcLtHFYq1H2UKrnDxzlcQxMaMq2SpMIw+XMA6kIuyOWiJOV+6ceJTVOG3JKdN4MSQhZ4yR2kOyhwpjGAibQN5u2FMxzmO9FWOcSbIYW1fxTrP254xxQD88FVReCQvlepi42K7QyrPfRDa7HSernn2MlJwIQaKIUkq89ajH+zWKIhE1VZGmkdfPW07NfXZj4vnViK0K0ypUNFAMY8zspoG7/YIYBnwzO2tvJ1SqBBXRNbIbI/F7QjGephGlFeu+xTnFy6tJ6KumijY9K4oO6Krp/R1UqVxPO4aYcU4JOKGgX3TcO1vxwdMXtM7jrcZ6iUbbDAN3Tw05Jqai8UrhXWXaBbJTvPbwhLaxfPidT5hiwriI1ZWiDDFXVLH4WghU9iVTamZqG7Y3I0OUeJTeWaxW4i7sWwowxoRCdIFlt5fYrpox3kIDJQTGcZSh52zWF9IkVa/J2BbWbYuZm+uchGKfYoRa6GcWQH+6RGtNCGF2bJ6R1V4aWWsNqmZCLIRJsp4zkSkMEnNnFbtcjkMZAUCi6MKVxjUN6miCaMg5kVIQIyxEgnO93ZNTpvV+Ht4WcXX+K0+gHx0/LMcP3Cx+7U+/Ryyy2badxzpZiIK+ADlSY6TtM+2qCiUCqLSkmMhJU5RA1cOYmQKkfWK5aFisFZXCzSZzs6lso7ikouvBi2/eaOcPUytFF4zSYs+LcLLvP1iiKJQgFJPWFZpW07UerXt0Vnhn6HrHbj8CQk0ruXCzHVHKoW3h3tmSd14/pW0MRWm8XZBQ5MaAqUxXN2w2gXAzYq3i5KSl6TwYjVoswTmCcoRYqciUdmGVaEkQ98rGWfbbPWGKpKzIRbMbd+IqNztdamPRVnOznWjbJdvUoSbPdjPi+oYpTAzjRMoQo2iBpMc21Gq42H3Ek4+vaWwj7uBkdMlAAWWpyOT/9pY98tFmqttMUTs0ALNG6KgGnX9cac0hNoO5R5Am74AQi4PYwSn2QAk7Gtu8EpdxMJs5XHNpJstMWbhFAl/VrB4ayQN1+Qh0HajMCrR6hTr7CsX08L0Pfz1QYPVs3nM8KsfC9vBZa8rEmGYd3uFzCUWmHpslOYeuOkGf5sy8zVAYJ9h/9F2WgwTv1mEUyujM7d9tB0pRDJPQkrwx9I2jGsXzaSJMUYpfrSk1oxW0zvPn7xSmKfDXPhC9HzWTYuTdz7+DHp/w/NkOxxV/+0t3KRFWfcS1PbppeedOx8s7C57evICiqKOmJDC+oFHkQ3NfE37piJuKmVGu8urOP2vN5qtzBBDLXM/XwxSnzKjwbLBSKITo5wGJDKB6L1P+aYpUCr5xGG1JOUGU85u13DfmQDVGvWK0ZGZKjARwWysURL/uaBrP/fMTmj+xxBBJ777JME1sJ8lW0yGRSqLUiaIlY23lYf/8BaOe67VpYhxHbNvz8mbHx5cDB62lKZGz1qCNwVvDyilSAXqPKQmjkdDmMc7oCFxdbPjkkwswjnurhVDoMzRWjLCWnSPFSKmCZNXb2/OVlVihKhrvWJ+ccc+vef3f+iw0D3nz/3gfa59QZjOSA+20zDe0IOEah9xz2joWqzXrkyVpuCBXjTViWHV+2tKnxLvv3Of51Z/iTxJ3lg0lpxmpq3wwvcnXP/gSn/2Dr5ErmPUln/vMJRUkCkeBcZabzcSdkw5z+T7/8r1v0vqOM7Omf+v7/MXv/QG2a2iahmmcaNuGs5MTnLVUXUW/mCsxSqt8GBC2fStZgk4Se52xgkLOg0XvvcReHGKDFLN5jcLaVijSZW5+qZQgwzKtZwr/EUEs5BypteBnR0c1OwDPG9ec9evYToMgt9ZijUZVhZ0Tap1TYOdmsVa6+RqLtjBTSyVmGdjoDqwxpFg4XTaYe/3siKp4dLagIuhorXl2M71HmKLsz4ehIIpYhBIc5+Y5TJGbzZ4nz66x1rFeL/FNZTfsqBWsE9rvMAyUJMhk13m8dxgqV1dXDE+vWZ+v6RrLxfUgmtcHJ4Qp8/T5lhAi9x+cE1OSTMH9CGjJqUuJ1foEgKvrgWEaeN0r9ilCqSxdw9n5KdfX1/S+EV1briw6y2rV8+T5FoyjakG8DYXryw1GaRbLTvaZEBnDyJ28pKLBWeyk8daz2+8xCrpmAXhiluFEngJKKYw13NzsmIZEchVnIQS43O0Zs6J1rRT2ITFMe6jgbEMqUe7bpLDO0Hi516y2R4nE9mZDjJUpC3KtkViQZiGGPIZKjpmYE143aA1RR2pVjJuEtpWmKzDAtNZxpYgAACAASURBVNuTQ6BfLlku1zI8IVGSZA8qlVis1njrUSWy8EtqrcRTS6HHZ0dFcf9OwzglrrZ7ckp88a3XWDSCToesyRhaAz9TElfXA9sxcrndU1KhXXjurDqs1VxejZRo+PHPPeDl1RWXm0SsiqvNjv124MVmi3MN1ng2Nxt2+4Gcs+hkrRa088WO3ZiZJk0wkZADMRvOlw1XmxuGmBhzIiZYWIMzmW4Bp53lnvd88HHitfsnnHSNIK17cXuNKKLSdAsjxrO50px0bMZIiAlNpWrDg0d38SpSM+yGiXF2J46jIZCoOtFrx8e7PSYm2mVHoELMEuFWKnkaZbhUoChDroVlU9mNkmNptkoGQkb2J6sNN2nAeydGkblgbYNXoHUEpch1YioWYx1Ryx6Zxgo1YpmE9aYUlXywlRAtb4iUCnFKwtZQs1yMiFayvzjXstvu5ueoOvpDOJ+x1tA2jhhHUp5QRRx1S1JUAhh5Pmst7vQhTJJ7nQvbYfhRs/hDfPzAzeKjeycM04hS0DQa66QJs+JbT02BHANNY4XD3YnbIVpg92kqMgHMma3NjFFBLnReY+dMQ28NfedouygPsFpFN1UrOUkY+yEqwejbBajnJkUhxccwTeRUGKOiSYYpFlrfzkYvYKJMSEDQoJQKNWeU0WKZPgReXu5oW4s2htVCoa3F6w5nLctlR6Mq+7lA9xox/amFOIzUkMlGzGqctTRKse4dVikabfDO0nhHY8UmORdDUYZub0hVApQVStAiB+0+odSKrukxxUIT8OsWkyNqmsgZpiyTbAW4udn67tO/4HI7SaFkZpG90hiVKdK7MSstj6jtXzah0QeagHrFRONQlM7g0Ks9lZrRoIM20NpbtPKAFB0jMI5I4Sxkf6UR/JShzP/fwnzlNfWxefz093g1juNQDB5RiL/UOB5RqFmr+CrlVf585fVf+fwK5kKNGc04NLDc5oSqKrmQKNHKUmRimCvUjJrhhBIiNQZMypLXNybKlEjGMpYCVlGy0EqMPZwDed+YJ37rpwS5/fIHMggIZSTHSBkH1r1imnZ88iSS3l2w2wa0VnSqYOPAxeWG/TRR8myVoqvAk+Sjd9ARcdbzteawNj69GG7L0X/D+Tv+9xxLc+gmlSJGaWG0hloUtQTGoZJzQYuoTpA7mPWhcm7r7QJ85brqW9MkLbELKNG6GgOL1nJ+2tM1jpIkt26KHUNMcn8EiCUBE6Z1/Nibd3nt3im7F5potNAVx8A4TtiuxS08uuvISLyFJtILyIbTmq/xS+SS+Wn/W4IK68JX/8efZ5oypSqmWPBO42zGOk28XPDn//MvsJsin339AfdP1vxv//T3+Z2vvsf1bpCiYjYW+PX/vuHX/7tGmoH5Anzhx9/lV//Df583vphoF9Ccr/CN57/+1Td59uQpqioiGY2gbwZx6TvcS1/5Dcef//Ej/uF/+Z/y9t/6AsPH/w9RtzRWqP1vvpEIIeNWLb/+v/4kn//MA37lZz5P2m6YsrAy/vHX/wHUyt9+/N/KMCQsuPhnD0klsxknGmf5ZDvy9e/e8Pf/7bd4be3RXKF15fqrP87V778FtWIypJhJIRNVYruZEap5geVcxLl6NrWappF+bGjbhq5rhSXQNKK9rpJbp2f2Sc4yzAShbhUEldUzFbqY+bljZoduq0BLo1erQhklkoCcj9R4pZWgi4ajzX9VCmkBoKSKMopSxbSp1ELMyIBHCSNBz4MWaYAFEU85iathEs10KYk4CiKvlcJZLYY0WmNmLYWhokrGzREMSutZaqywc/GY0aRkmVTF0KKrDB69Fefa1mpSLugq6KrTGpxDKc00Cd3WaEVIlXFMmM0AWWh/ShvGKRJjZkqRMST24yjukZ0jpQS1sNlFjDeziY2i9SK50BUabYQpYUQTX2siZI1ThlIquykSkYHrfpR8ypoiKgqqpucGHoT+izbsxlHMYKpkDacpUlKhGkUqGZ0jIQU5Z3WWSVQtURco0AWlM8qIo2dKgYRBqSKmQ7OxaUwFbS3GK/bDKLaoymP0LNOohRILIYnZ0T4kYko4I3vtsNnTeEfnJDN6SomMDDSDCqRSsbqVoWGWwVksFZQl5Mowo1hjGAj7gYTQjFO1OBsgBfEUqIUxZ8pxlzaE0ZOLNMnaah50HqWSnMdqqRi6RgldVFWWueW1BytUKSjtOF1YrIXt6RJTHQ8frrAm8/LmghAyjXeULvJGf0rbtjTOM44tFy+vqcpRgP00EaNlvei43gQ220pVhWrEbHHZe15cbplSYgiZYSysG0/KEwtvcRpCyiwaoV3CXCuK/SfXu0DbevrGkGJmmCKrZY8JlZubge1cBnWNp8YyuyQXMbQpVaLJNHijsKbyWn/CuN/J82EeqBtnb98zF7xWWKtYLlqcnc26asVZN+eKy3Ms5jwbt2n6tmfKQYY+CoyWvaCUArZgMuRYiUlM4BQVXeaEAW2PQ3dtNcYaQhTmUEoSU0ZJpCxN4oHRZ2ylak1KmVTLjB1oqhKadkqJlAMlZxrnjvm/uRRykfvYGdmn45yFO04S91M+NWH+0fHDdPzAzeLf+KnP0Fkn3PIwkVOeLcuF8oYq1BwIwQg1pkjxqGfNltaalGdKQxZutrWK7Y3o9pzT5JwYU+XDp88Yo7iDOtsQE2x2kzg8OYP3lrNVj9OKRe9xWr7YlBUhZarLM+XQ0vpGtEL6tgDSRnO9v2HYDZwuZHNCb3l5dc3q7JxdgY83Cb3L5Fjo2z3WKKakCFNhYQv3zxeYThFD4cXLDWOQaex2X3BO3L+MkjBl7zRX60aKVm3QuhUzgRrFDh4lNIIhUFVlJIOyaNUQcyQmxev3PSvbYs1A02UaNH3vKb0jlkLbeYlfyOC0pbGQNmveW7TstnvsjNIV4ziU7JpXkLhDs3akls7N02xEI82R5ODJb962AAdUL5d8bLRylkKmHkTcc5zCAQk5FPBHl8w5YFehOGQ8aiX6oFddUwRnhFrEpIJDhpu6/fPWFOfQ3Oqj/vJTnefc8B11lYhm0RpLLonb/EaZxL+Kv8pfFL5ppKhUh2zP2wZTzgdiblMzRlVi9RiruHPnlLN7d/lwUnx3GNE5ozOgLWmolKxBdYQc2edMtY40BVTMopNVlZxB+wbjHI1WM/1DkGNnLGfLJVZnKi31VKOK5cce3OFP37/k+YvM4vQxg7rEFEe7cDhf+c7zG15e7GhtR9GJrEa0tZRiZiMhaQxTKvSNZnIaHYCikGjwTzvWHlbJvMSYvTqlwVS3hiqHRagV7Mc8N9Ma7yzGFsYhYK1DqUIMSXQuSuFm5JlaMWhUqeQkdL/DkCCn+XKXKs628+rdDVumcUvJI8Mb0kTceSFuyKXIxDgXAzOL4Wy54P6Dcx4+PsM8WJKQArQx4jobwo5cRdct+hP53o1zKC1mKf/XH/4CUPmlv/FCzLKM5HPFnKlG7r0QCyEWyBMlFlSjKcPEnTvnqG5Jbk9BGwwzcnXIon0FVT/cxdo4NFuefPMJ/ckZ95pzBisxLaWCpKJKc6WNOOsycweK0gRTWJ2teHyWOeM5m8sNbtXhhy1ewbiP2JL53P0VJ02Hsi396ZLFyrFTBVWh+06HAt798ufJKuNVoQ6BHDOmsfTrJf/kt97nARvuPX6D1056+vYKrCB2MYsVoEPs2WMIlCJNRygzNbSUV4YFAFr0M6WQYiAh5ivFWmIMQqWuhprAz1S/mqREzhS5d7Wbdcga0zh5dmTZq4T1XrFm1uAohTavZLsqRU4HlsJhOCl703q5JCUxnPDOorymbTtiSqQQRednJWpmNuCWgrBKkZqtQpdKnWBMlZAUKWf2+4EpBMZpwhqNtaLXlYxYaX6axgsNk0JJaW5sRVtt0MSUaBvHqnGcPlxQqeQUJQC8dxwYJEobWC/IuZKzDMEK8gx5/PgBMSRxiM2F83XDNO740/efoqwmK8k6Hb6/5+7pGmsNVieolc1mIACX7z9l1Te88/oZpwtDLBqjHVVVdmFku9+jdcvVdsuyXaCt4fvPr3h2dcO7j+7L875WhnFAK8Ny0QoCNEXqUJmmwGK14tnl5uidcHayYrfdUmeDq33eoqqhqoI45IquOU+RqhSusRidqDVinGa57inTQAx7sppHsNbNGvZI45c455hCZhwCVTmmHNlOUtuUVFmerNE2YbJo/E5WLbUorq/3hCHhlaVpPKZRpCDP+5wrkFj0hjHCsJ+IJWFty8lqRSkjUxzFdGeK8/k25Gy5Hka0qvTGEPcBpQ2hQIwRd1pBFZ5ejCQK2yBUwjtnS7a7rZwD28ma9ZoQpjnuyuCKEfZHMVxeTSgKtrVUE/jGt17w5OWOP/72c8YYOG0bksr8xBv3KDGi8sjqfIlTkLRjP0gjsvCO114/YdyG2TVb8v/SNOIaQZgLlZCTmEsFw5PLS+w86Ek18/D1e1zejDy72IKz3DvtUVmjrGK5lJia7TZyUxKpSMbwNEzkHGmc4vLFFdt9IMSE0pnGG/rWE1LBVvDGYHv4W194lz/6i+/y/Y8uhMK8cOiiSSVh9Tz0KZVWZdat52KzZRoCSlvcqqf3hmlKbPc7colYK5rFcZqYQpD/n6UZP0g+FBmlAs7OvgnTJM64anZCDRO5VpTR9L2lZi0RJlpLA63EgTaVTC4NsSRqiMQklP84kyScO0TdiQPyMCaRBGhP0/bzs7hgqqLW2XE5RHIJmMbinEGZVtZgTPzo+OE8fuBm8Xf+9fc46VaCSoVESaDcwDiOKKVpnKfkiTApnGtYnSzRRtM4R+Mb7p71mDSSS8TESg4J2yhGV3nj8T3unnm2Ny+5vM6M+0uq9hIboRKxNlxsCjEmnFcsFg1vvnafdWe5e3fBwnt67wm1UDX4VlFCpTOO89Mlbb8g5gTF03pD3yl++1+9x4cfveRnv/wuD+6c8VtfeY/f/aNv8DN/84vcubPgrc+sMFoxDkUcSk3hW5+85KMnV+w/ec4XPv8mjx71pCAi5O1+y5Mnl+TJ8/DBGZ99+w7USEqgVSZQmWLhalsIQTPsA9fba2LU7MdI2A64McwFRgbXQGMIIXG12aP2gXjZU8qGm92OtrRo4GocGWKm1QXnKhmF1Z7eObJLdE7zcrfDGo2xnlwjqYgmT9h6UlzlkiS97Rg1IUVEzlI41SobhriDHvR+6sg8VWqmjc44kzHihFhymVFGjo0o8Kmm7dAkmtk8QgLnFcZZyUk8NHSliB25/JYULvW2OTvSPnkFEVQHiqlQ7RTyvY6aTPngHE0XZ81RSVGKQoTidjhPh0b6Vncp5jxVCRWUKsiEvL1QAQsVbTMFTSiGPGSmqNlEy//9F5+I6U2BJorwO4aJqoo84CninKYhXb7EGYOtkGNm0pnkWhqlMWGk5Eismst/5wEGxe/+7rewjGKEtGi4a3tOz1qefvcJH18kdldXMFyz20+43FO7RnKTjKXquY0ohaoCVYmDIVUL2lkq69ay8Qm1O1wNadwEpb5FGhVzTAq310GyNhVFlWOjeMAcU5Qi21qFM0JRl9nTHIOgK7rM2opa5ozAuXE6Xu9X8e55vWh1XHeisYab3cAwJt77hz9BTpm3/5Ovk3JmGDIpKVIawBSqWBDz9PmWn3znjHU/sRvBKsudpQQ8X1zdMI5RTEmqYiowxcyDBw+wruK85moUd8vvfv8lrVY8vntCax11ChSl8CtN7zuscuRBTJJYVMpYOTk7JzpPMS1VaWqVe02Mp9S8gOUeyPPad4sF6wfv8GjpcV1L168J3QK0p8gwHFsUWcm4pFSNUnmewRRQhbZv8E2mlCt2L16iB89UrqgonrzcMoTMa3fPmS52vCzP+fC973GvTTzb7zGxsL/eorTi21//BqEElo2DkpkiLLolr9/doXY71i5gw454k9ndbFFeDMimYkhV0aBxi1ZC3lEsmkaQoikfnZpd02K9Z7+PaCXDLUWZv49BO4X2cl8b3whtaz9hdUZbOYW5FNHGKitsgQO5oooJDhXyJFr9amVBjdN4HGotFguU1jL9R2Fnc6OqMtoIwddZhbENWmu8cxjrqaWSQ0RlAfOVEgRAaahKsR/2qFLwtVIyDDahcjwaxDnXEKbAbjNTk7XcjxUl19qIaYWxds6WK4KWoaFqIoVMZSqJm2sJ8nbOodDy7HUV74wA93NOMSBZwV5KC2MsXddhTCVEL1E7ykrhWxxN42kXDXXWVuWpEsZIVoGCaMuuP77k8vmesKgslgNdVFizxFAQ4d4gVvxKkaeKObH0fc9pTOzDBBhOTk9YNA3XF89pGsc4x0ykOQ942TrWqx4qhDSQc8C6guo8KYFzQrct0VBVlqzNsD/q0AFsY0kxkFOiFqFjhghZCUoTQmS/H0nFsGgdIYrXQVEB3zhynA31KBKl0ji6tuX6akMYI/2io/UNMUZOFgumKc4GOYWYEo3rwGtapWmc5mqTcNaz341sdxPNonDnfE1NjnleQN97mqbh5Ytrmsbhi0Yh+s1nL6/AVE7O1yxPevquRelIMo5xN7DZBqjgu4GLl9d427E+7dkMA3lrSXnk7tkp4xT48PufMCXNg5NTWitDDzsO85pe473n5/7m52haC6M45q694/Lyhs1mz/X1QKia65sbGtdwfrIgpsj1fsR6w4N+xTQGtruJ/bDn8UnP0ncoW7l7b8GdRc+3v/uSu3c7VGNprMLpytnyDt/83lP+5P2PiKWwXjg8lmbZcrO94sWLLaoYlHEYO7BSlppbxqTYh8TTqx3jzF5YNJW2s9w5XfL9Jxv2w0hdGk77jpfbaz653LEdIkUX4hQwamDZG1aNRxvFFCGOhY8/uWRbokSzUNgNe2ryTJOw3rRTtI1HVcPF5QZjNI1rqK4S5xieXBJQsdrhncI5w7gfYaatj7FQhj0Fi2ssB0PHm+uEdgpvC9ZofGdxjWHcV6Y4G3TFCW8MuXp8o1ksG4wxpBgx2tK3HSFFUkrs9oOsEWvRSnIYldKEWnFKCZPQOaxt2N5sKT/KWfyhPX7gK/Plx+coK6GabduI3TaBEkdCqOTq8J1jN16Sp0JrF1Slebm94umLp9zs19QoE2AN7KbKvbOGEAJf+1ffIaTMdb5mrBkfTzk71Thf+OqfPGe7HdinCa0sOShKTiyXlq43dK6lqkxRkdPFOVbNGitbOV17urbFIy5Se2OpJaNK4sH6LldXW/6n9z4kUthsBnSBi4+f8+zZM37vTxNae9b9CU17gvWZpYncbZasf/ozuLXiezcBq2Hll5y3D7l7t/Jit+dmF/nqhztWLbx9fgptR28TPfDaY4trFSEE/uB3RnBLfvaXf4rT+w0lQzVGnP9qodGFWiN0Heb0RGygOURZnFFTEKRmFvPXDFVplHHEac//8F/9I0LecXZnjQaZEFco2qDI5FwIQQhzXddRSqZtO2qVwFYA5y2d7TBGBNLTJCLpY3OlJKcr5Sz25TNSdNBgiLFoPqLLtYqgOqU4N6XSZMQkJklaa5mK5ULO4yzGPnRp5ph5eOtSWo9UBmkMX/27/Eyp4pJZSkWrVwLj9W0TqOZmNkwTSUe8tUJ/BqAes87UrRAPM+syBcnMQj1DDCAO30sZ0RT2vUeZicfa88brr/GL/+6v8Oitz4JfkLTQRarWJK2oOWK0ws6aJ4lsqDRVo3IBEtEYsu7RVuGrRm/T7G6m+Ojt36CS+dX/4tdIUWFdBka2W0WzhIv7v8n3vv0RPPgib9yB68uOxUJhzUjvf5vSJBQRXQymGqiJzjcoBRORlAtTyjy4f8b1/oobGyhp/pyKGaVlbvI50lnmUynnWh2op391r8lH6h1zwWoJuogWlwJVXExFxzyb4VQ1Iz71SDOl8ilr7oNZUo0VaxTDVFi0PffuLvmw85RcePPRfazVGB+oJaFTS9UB7xe8uLjkfFF5/Oghv/R3/xovPnzOuN8y5C1TrCzOWgqGD77zfba7AZML684Tbp7SenHLS2VPKZVvfeVPoBa+VsUFcMJhfMvly2tOW816YWjvn6F1x92TFbFYSO/RqD1fvnfJXywqVxdhRlClp8yzgdchxqFWxXD5hKd/9i/4+F6iPbnH2q5Y1oAh4GsW3ZEBsqEUyY5TBXSueKMIyXN/eYcTc4qOnre+/JOCpk1LlIEf+wlH1Q58y9/tO56/2JPvn/PGX3/Eow/3RDNwerWCAl/64rskEhrPdtyRY2TRW5Znlvo1w707b7BYrig6kpYtTV2gVKTVsleEm5Hds5fovpf8r82OmjO6b9Heoyik8ZIpZq5uBnwjyKAxmqZxGGtovKVVBoOiqJ3Ei/Q9xijiTL7DC+q6Mg0hyjk2VtA3mWMpcFbYH1ZcXduumZkZiC4nJfqlxNoI2i+6yFIKaT/MpjcysNqDZIYqBbqKBnM2bapVBlk5FbquRWmLymCUYtF2DKGQU6Vf9mISFxJ3756xWi8YpoHtZksp8z5YClcXl2Sy5Cg2Pc65mTWhOIjQrTXkXhD8UirGOtZLyzSJzq8UySAsNdN2njDuZUijIR00SQW877CNI+ckjarR3Ox2vLy+khgp35KD6Ld8awSBN4Eff+eEH//smhAyN1cTm6eJzfQhSrU419D3HaUmxvEpaHh69YKcFCEIDXRpWpzSxG4g54hKFeEiZKpRWC1mYLv9yHa3nR2dNdPkaDz43jAMmd3NhPMF61rszOCQqJzCNA3ybFaiPdWzM3TvljSNkbxANOphg88jY6mEHKk5k1OPdobNdMlZ31CrEvdsFWnrBr3SdP0ZBnj+8iUxZfrFmuwhhIFpU9AGtoNQuBed4/K64kuklAHnNA/vnrBYOKYYIGVKEqbWOA1oM4hUKA40VgxSUIa79+7ReGjn+JdpHCBlhrjBVM3jOyuwBpcM/X1LUQlnEiftGd5aQqoMoxjLfP7Nu6RS2ZWANRqK4uULaWJf/4zDu8zH3/yI3Rhp2h6F4/evnvPOm4+489oDvvavv0u/cLzz+ik1VJ5fD+ANX7hzn77V/P43vssuRKZccBaefeMjXtzsAMW6tyy6yhdff539PtEvG7Q3YBX/+x98Bestn/vMKQvvicWBtRgzcv/kPo/OTshVJE9mNo/JMRBCJOdEpaKtpxTYbkZ224nryw3hQUvODb6CqZXvfLxhYYB1i/ZWhgf7QJgKmzJTfkvlaZyICRo8jXcooDWwPofrFxFvHH3fMIaRFxdb9lPk4aM79Iuezc3AsxtxeLUKdIaaJ5xpyCmgrLD8Ssl4W3EPzsgpEafC9UWh6x3L1YihoWDIpTBuAt5YdAn0WkEza3+dwabZb+FypJbKNHsz+DmHsiImhW3TMIZJhubMwIMCk+BiOwgaHuU5r/WPaKg/rMcP3Cz+2Xc+oVRx/nLWzcV/xTcahcEaj/eKrtWkmAh1hzOWVePp9JKm9QwjEgaaC7ZJYIw4qbUBHWCMjilUnr24ZDMorJOft6bBK3GabHt5ELim4ls4WZzLBJxETZG2MQxZqFQRhcqK7RTw2rBcWFLVDEHxycWOnBJDHKmqcr7ucNbzwZNnVGUoiNB3akaMf4nSFZULuhq67jnGJhrrsHOWoNGeQmEfwmy9PKFN4gP7DG9axmlHKlk+u1VYU7h4viEXzdP/c0e/1PR9Q62WkDKqVDqKUPBy4eT0lNPlEq8dY0wk3UCKUqAbA72F+YHlXYvVEphslWM7JpSqEp+EhFfrKlbHqt6aydQqTm+13jZECkVJkkUlVFFNSjIxN9p+CsE76sRmZMkYPReyGuc9zoql+C1FUBCRQ2yGc2aeeBWsOph31NlUR47b95PJ+cEM5/Y4kB5foT7O73UIrdXm8Lq3xjpm/m7WWmkyXuVOfvplj9RVpbUUQcemUSbKElA9n4ks2VxKJSqZi2ngYohMv/EvefDgfc5Wa/Z5BAqhFna1SGZRraTMjDYUVCm0RaFLkWmh0mTbYY2lJbGcGyxrCs/+ox3GwO/95j+noDBKtGhoxWLZcPH9D7m+uOCbX/8z6hsN49gTe4X1GaMdtkAur3xhZT5FLYVZM1ihcRJiXpDJ5u05uj19RzT21evyl4G/uYusqGMPWWeasZrpe4fzfnulD6+pji8oBkrqWLxaZY8ocC2CTiujMF5hg6JzlrN1j7OiF26cQpmCsx6rO4iWkC37FCkKTs9WnN9b4TrH2eO75LAihYFcC1OulFx5fP+UcRgZx1GMVCr41oJSLL+xpNTKZ157l1LEHEmo1+KcOo2vo6xo4KopqFChKZSYyMXimnPeevd1zv/wJR98eM0hueSWEXBAdWWdpknx/MryfracTIbHyxFXBHU79OxFC91ModG1zlpLaT4VcOfUkzbf59sXl1xc77BWU7GEWpnGJNRkaxh0ZvNix3emyANXePnRS5SZ2Gz3KODbTz4BI4ZfqkhQ/IvLkW7jeLnZk7XnyZWiaQq7mCh6j1WVJstgpjlbkhfi9JhrpnTiqJmKAiOTbNtLQ/XoYZmZEVKsNk0rOj3J1RDGQJZ9yM6shsP9q4Ba1JxLNt/T814RgryutpZaKinIoMdo2a9eXdhhnOZ9Yb4c87ZQysGFuuJ9A6USgljwa2cPLasQy4poq0rOQr009ijLVdFSSibGyH4/oLWdw7ELm+2WQsG3LQojesecOT07k89QBWGzRpDCXASlqLUwTSPOezCI9i1lqvZkEs2MShz2AkFG/cwGAdpW9oeDXk8j9M0kDq52NvVh3uerEtZByJFq6mwWNPMPaqVtK84p7py+JiiF1iQipWpyuz66b4sTo6WQ0CpD2RIHTeMbSiqoIlqwaQxMeWB9sqKqSr9o2W73DMNATgvGWrHVstmJ+dzadcS9IIqS52jx3sGM5hot51aXgrdaTLIqtI2n1kosE2PJglTndLvP1UqjGwyWUsEahXWtxJsYyZbNOUlBrxs658gV9koRYiLWijVCxadoGisBFAqFSUUil65npoUu1CJZMazNRQAAIABJREFUxtY50cW1DpNF6zbGLLEdWgMe47xoLsNEqpq+X0jcQ63YmplKxjgLVTNOCVUjox+oyoBTmFrw2qKNx0d7HHj295dQVuyjCDnffeMuSsEuwhgyD+7cwXuNSSM/8WMnKANeKVxn6VtHKmBtZIqat964N2ccK2KuxFSZYiCmyjgGiQ/pGm6uNgx7jR0SJQbefv0hvvMsWiPP0X1kiiPTRszixlhR1dB5Q42F3TCJjMk2TKMMBxZ9I3E2raU1inXnuMOpyBdiZBp2TKXnrYdrLjcDVVVqKoz7SKywD4G2dawXnovNNTErGtsyTjP9V48QA1ZrijEUA9o5VicaHyLjNDGG2Q3dg0qyP4WQKVWzC6B14rPvnFOmwtWLQFaVGBPeGXRRkuNZMutlQwqGzSQ1VtN4lLbUaYI5Ha2kJLIiIIWCMsJzHUIQTaY6SIgghETMh0dyxeoZxTSKOhcN1ii8tYyz+/+Pjh/O4wduFhfdHd79zJqTXtFq4Wy/3FZiLVQOrqgDzi7RXcRb4axLrpNoEdqqaZvKeaMJASjguo43H3q0rry82XOxGUn3IrVIjpGxmqfPthiamf6nscZKVIWRSqnMhiJ9b/CNEQt7wGNwwIP79+l7S9/Kg8o4w+Vmg3cLSjwT6+JawfgZIfKcNB5vhY7QLiLeN4SUmeKEQ+N1i9aFlAxTDtQ6oFXhQbvALiwpWfYho5ctnbOYaDBKSdixURSV+dy7rwt3fZpYLnp6Yyho9iUz5cIYEqGI81bbndGvljit8KUy1IC3kmNVciETBcWrgJZYkC99+Yt84+OJFy+eyrnTooPJVbK9ZLItysWD1i3GNN/4QgmNIX4KoYPZsVSZYydwaP5KlV2iioSCHLPQTGCm60hAssrl2LDlWUyvqhgaKQ4Fg2QAUesxw6cwC6+rkOfrke45NxNKqGIHtE9or0p+8ZWm75gl98pGVeuBHinmFBlBvvTsRKjmZusQyyEDCikWzGzRP0Nmx3pR2hgxnbBz/mC0nsY4Ht055a13Xuf1t18jR5nWJyUZnTkFconsxy0xJ1JNUuxUQRgJETZbVBnw/YKihK5H1VTb0/YjumYe31dkNJP2KOVQcUfXVD7z+ARs4cNPLnn7jbfI8TkpOQqKjz65ZBoTvu0Qu3fR3qmYMQZQGcnOU0xDYNG42RhqpjVXaVbUq8318bxLtIo5NDUHmLHeNpHyo3I9tbZYO2sy5oZICmV1e/0Ol3BGtI9r+vDz+hC/Is18QYr/HKBdNJzdXfKFzz7mD08aNJVf+YUv4LWj7TyVym6aUMpyPU4YpXj3c2/z8DOvg1vh1hFXCrpoMJJJSM6UKGKiHGe30YNuF8XZsxOolb/zs1+CWSslobNyD9oqlPWIwjQ9pURKGqAa4rRHKc1VeZt33nnCH/3xB7O2bnY0PWpspeHRWrG+f87n/vqXeHt9g+/OOH/0BVb9/yuOnYemvGok/tmCErfjgxa9aVfcf3SP7l7HePGSkHpKCixdI3IEPd+zCr706B52q3hxWXmx77gYAyVZdvtMKpXf/N3vMQ0R56BdiA5RuwVtU/jgexsWfuL5x8+oKrOLE0xi6uCdxTiDXXYYo3DTJFFEM8MgxkLVRorH+TtNQbL2mk7Tdx3r1QJtMkoZjBMHbF0yJ6sV1CzDIi1uqYtGzF1c7+R5oRWaDKTZTVVo0MbomU4tRjXMA6hSRDPv7CFLVj5TrsKyaJyX9ZnEuAKg6BkVT5KRl+eQ73nuySEiqtREiBPUKlETSIh7LZGcJqjgnCPGNO9z8uZ6dqyuRTT11nnJmZtnbVqDVTIUVJ04mKsKGJhCIG5l7Ymj5mEIByGMoBS+ETM4Y8ys7ZShF4B3HtOKpb5k7crdYLSSpr9UMeRBo9t5PRdBxrvFch76yPAnFzEw0cZS3BxVMu8lNVVqdaQcMPMXG8MAWmG0aAcbZymzEU2tGas164Wnby0hD5QIZQ+oStM6iYyIQfTa1VAz5ADjWLBW3ChrVWQMdaooV4ibPZv9CxpnuLvuCVQsGlu9nKu1UKONXlPKwTFV0TrPoDRqHjplDI3pGVPkJu1otOGs71BuKSwSr9kNE5fXO1Z9Q4uV4Y1VKCPfwQLOeGJM8uxFcvj2m0RFsdvtSDnhu5aqFde7AcgYDfurhG0sQw2AQVeFIbHqHN63KAUxRUKG/bRHYzhd9Fhj2O4CN/sLWq9wrkVpw27YkmPkxx7dp1B4uZV79KTrWHSaMVa0FQOaxUlDSBGUpmqFJ+FKJlbLdgg0Rob5zip8afDrhtNuxTAmtmOgbxRf//YLbobMkw+fM+z37DY3/NxPf4HpZmQzDIQxM2z3YBONW5NrYbMd8UZz97Tl4b17bLeZ/bSldZb1ssFYzeXFNc57UlXEmikus1SOWjVZQetWFFUwznJy5y61FLy1uMYTYiDsk6D5Gu5Pp+z3G+6dLVj6BbkUhjCScqFWTUqeGDIvbp5J7Nu8NmOKTDET6Fn1DapoapRn85R2pOxJg0dTKDpws0sMNdB4qaMLkRwG3NBTkkRmVF2ZcmIcBh6ft8RaGVNhuw2Yojh/o+Hh8pztVeDqeocxFutnNllM1Fw47RbkOUvdevFwSFOCLCkKw3RDTFVq2rY7DtB+dPzwHT9ws3h1M3Bx05CSonNSzE9R+NC5FEoKUEemopitH4E5zNhUhr2YNWhdaVtDmN1Qfes4DR1Ww/Ww52afcElJC1P1kdInWRqKqjJVVRb9Cm9FzA2iY6rKEPM8dVWC+FhzcKqqjKniaqWxAo+XebJrteTATMPIovMYWwXTR4kpSUqUKg9JjeRcZa0xRmyswxipKqFNpbiOUg8TYUUJmSFn2lLQRs5NxTDGRBwFRXFoGuMoJWO0onOS36h1gSC0xvXKs1p6nJFx7bKKq6qB2bZdywNNKVDywL5cNnOUxhxvcqD+UVCqiAbnU1dZzRq8IztTCo65ij9Q+tQr3dBRhSjymLlYPxTHHOGlXGZ33ENRf2jo5uOgSzxW//P0VatDq/ZpukKp9bYhmKmpt1XMqwP+etSZHNxJD6Y1R1OcKg/nOhdLHI0+ZB0J0jAX4zN1rMzNh7hRHhDVw2e5PZ8HfWeuQh3T8mUpeULlkbUTo45IxtX533UGDY2tFANVWZSqGCUTaJM9pteoUlidLECLS2GuCmxP196gSuLxw3MKhkFrqjaY6DAosBllHd/92NCcnWDMjnbRoKwU3GgtFOPjGSxULeuuvoKUpJyx2h7Nig79323T92q/eHsNP30l/83HoSk0B7optw398bXqq+/wioPuvAZrhZIKRdU5o47jv5cqRf44Rq43AzFZjILdkBg17KYiaKkuaF0Yh4i3YrLhXCtrKIY5z9FKbqOuqMM6Vwrjm7nYqTDreY0SXeG6W3Bo6pjXby4FWydShayEBkVtqHmBUooQxE1vnFoePX4oWYIzWnG4424HIfJV+9WC8/unvHYSMM2CxZ17LBa9OO5pGWzo2zN4PPlVic5tsVzStL1EVHjP6qRFx8CJtTSpEP0ksSalsGgb2s7DNpOy4u55zzg5fvHOVxhiphk7Zo8vjFOgLK5vWHSy/zeNYbX4/9h7kx9fsizP63PPHWz4De7+pojIiMzIiKzK7OxKFeoWotQ0jRAFajUSG1izghUL/hYWSLBEILFBrKAFCMQgVXcLVKAulFRTmVWVU2REvtGn3+9nZndkcezn/qIaJJQbcpEWehEhl7s/d7Nr955zvpPKCtqEZv+hA7RqINVGpJHnrNS6okO+Eh/pyGd33FQiKVb2dgBJNHNCRNkp3nlqrthaIBlCr8Ypc4qIFTZBn+uTyy3WGpwYgtMSq3h1mnReA6vF6GBJ8nuDJ1TfXeqq3324vwaDUNY8VjAPFPoHJL01SlM2Qv0a3V7f99YKdl00uRQd8onX5mI1ixBRumspimi11vCrU87ZVbw2tGG2Zj3X1HG0Ns0orOXMCtF3TJ2I1XbqgUFiDHlFFaxx+jNnjeo4504aY6hFKfqaafnIBKlOndRBWR2qqzPv3bN1AGiEVBdMM2szzjrQVGfptrJkzrR3a/1DA5lb1XzY1jSUHJU1lFSoTQejIo7gHEtOq26/avC4lQdE1FpLq0ZzZquiMrk0KGrss2StiezGUufCm5sjwVucccwt443DriZzkjUCRWTBypnVA9N0Inj/gPZVaxidp1BoVaN4llyISyYvlWHb6TDBKKpmXVjRIXVRP++VBnX4NOiAwznHsuhQyFqVi3i/RsEUpbJbZ3BBCMFxWtJDdEwGfOcf77kRkErOENYmP9XGFAvLUlaKq9Ld59g4nRaOk7p4L6mRUiFIwjnD3SmxqdAHR8yVVAqd16M4t9XLQRzWQmmZloVStPE3YjlQuD9EUjb0/cBuDICDajkNhqnXvafmTE36DHwINFEacVzqOuBpXN8esK5jmlVT6myloLXWu+sD3TAi1qhhjVOEs2YdfHvfsQmFKVX8mnVsHFgHnTUE0ft/f1pw4uidDsU3ncUYdY6NuagJ4uKIkll5ZrzNkWE0iPTMi7JZtttB6+NSEVuZZkMpBmoAk9luA1jLpjplsNCwXY+RglRPFUUoMRBroZnC0/2WmykSa1QdezUgggu6Xs4slkZTuUxRttp+9GA8pzjTjFFHYLN6H9SCFUttum/91gj1N/v6tZvFd3cvef3DVxjRw9aJ53IjOmktOmHCVOZ8IBXPMVdyyZrfEivOKpSo+SqVYoRgBbEN2xT9mdrCVDL1EAi90HWGXiwpG1JJ6wGbsLZx6LZ4J9wdD4reiOPmLgMzrSW8s+x3lRAsv3hzC9WoYY41BAutCr4Txr7De6HWwvG44E3BdQ5rA9YIvTfEZqimses8nfPMKdGs48nGE1MmzmDE0PeVNsyUBktWe/F4HxFRzUBZGxZQCP/2/gjV8PRyx8W+5/Zwy9iNGDQo2hrLNE80seTP7qnPr7jcqq24d55sRDUuRgNiRTzWCCYIzcLhcOI0HdUkZT08H4xG2tmSxFCbipu1eHk0GmmYh+iMxqNL5Dln5zGmgvdC9rQgOhf6rZkHlIMz8vTez/GAMH3tWkNj9RMeqXXtkS770ND+P9AYHgmP7zWsnJvFr6OJD9TGVevWqh4KvNcYKwKqB73WeIqK5pxptaoG9EwNM+fGlIevV5qXwRlhjjPH+3v+m//+f+CD//2P+Zf/xvd59faa4zwTF6V7VBSlXGLCiEWsUmM7H+j7jtAFQt+R28LV0LPtAmEINAPOev69HzrECj8LbxDvyabieksIljYlQrfjydOR/+0nP+bLuxOX/YB1HmMauz7wLhRqjZxTGawDyGA8a/IEYgypVPpOGIIwLZpzdx4ynJ/q+yX0irsqSvLeoOCv3jSz6g077xhCeO9JmjUrSp/X2eDo/BeuBEJqrasDJSsao7EC53gNaQaMME2F0/GaJSbeXn+H2hp//x99QcqF06yZapejI5fEKWV8g6kYjIdvPw20JdJqIees2XfOUZvheDxpaPyqVzVGaZTWGD5wv8IY4dWbt9Aa3qkbqhE1IJlTxa2GRjLNtAjGb3VAVTPWCft94fu//x1ccEp3N6JU8aZ3wK4LsTZh119gpaOzAet7un7LB8+e0fcjzQpCw5ayvuuZ1labc7Rw+MHv/4BPP/qEvenJvufy8imtVsZaqKZxTDPzvGBOEd/3XHxwyU/eXvPjH7/m3/jXPuF0n/nOJzOpFab2e4reV0NJjZgTzheunu342as3PH/+nL/z/Rc40/jl9YHBF2x15FmLcz8MlFYpxxMeyxILhyXx8uaeuGRqSVgB7xw+GOKS2V+M5JRpuRC8ZZ5mZUIEg7MdYhLj0LOsAfUlZ25OJ0XW4lGzxpxjux3VbbgoLRPAO0sfAsY2UopYq+6Moev0ey46yLQrqufXovBunh/GS0Z0jZwRRmM13sWucRDnNV5X5sT7w7vWFPV3ztNqoq3NT8kR3cWUZnqmkZvWlK1hGnE+aQC482uurzZttTZiUcRbrAXTsEE0I67UdW9/zM7t+7CyOvSdjTGtTtJWXVeN0biGZSKmNUu0qeZdpKzNoxqFGXN26xQaZf2Whpz182SlulHBtIKxFefcevQYnNW9wRq3useCFQ0MLzlR1rw8K0JuZi1cK1UKYqpmXTqLcyp9qCUiqFmHFac6+7WBtr7RVtdynUrNSF2Qeom1jqthQy6V23s1ODnWCqLREsubSK5wmmf6rsO5npgzp/lE57QhT+ug9Ommx1oYvZCt5d3xyKu7E3MsPBl6nl0OXO577g53GO+pneV4zEqHrQXjHU92PcPgKFUngJvNsBr+gLgNDchLxDbBjhbbC5jK8WIh2I5vc0nKlcMyUansxy1TjBrOXhujEzYidJ0aUc2psNkITy8vEIv+PDlzte/ZbwIv3xzpBkfvPEOAWhNTgjRH7lNhdo7gLKU0Ys1Yo0BAWQctnfMgjjk3UmnkZWJJhS9T5PagjvTbNzu+/eGOzVB4sg/AFWKEu5i4aB3mqRow+qGnFcPt6ZrTITMOG2JJ/PKrt7y9u6NWFI3LcP/mjnleePn6HmdP9MHjnTAOHc6qThnTCL5jCEIqjU2vMqcpT9TS2ASLmMqcGvNi2ATYDyMxNe5TxBqYTxGMmls1JsQlPvn4CprH1bf0g2OzH/R9m2YwnlQjFYv3A/OkZ1+wHTkn5qiGaMEaYlI/CKRireH+pOh758AgHKZELIlvvniOf3tLOJ7YDz1SHZXKfMhQYbvpiK1QqfTegUW7Ysn4vkManKbMPOv3jWlhXhaurnaIbRhXmOKCD781uPlNvX7tJ/Ov/sFnpKq24q1aTLMEWxA8DUtaecnBTIgdKVazk27fHZmOiWl+jTg9wH/x1YTxHopQSCt1q3J/rMwpkTIcTgWZKnuxdF2gmkRtjpaEKSdevvuSKkbtd9c8l8t+izWJZVkQMWz2ia4byMsBqY7N1hGCpXMO18BUz9vphPXC8+dX7H1PnO5wWMZuwDqLMQtXuwt2g3DZKb1tqYVUPU5YHRY1a2ocM8+fPiEtiSUt6mBVI713zBgOi2hD2zJiKz/56TtOUyKMHgLkElkWy6vrG1KqBDcyp4VYC5vhktHt8E0pcdIlxFmGflBKTi5YmlKJSiV7Q8ZTmqUW1JkKgDUUfOURnVkAtT5SlrR41ym06xzaG7aHyv+s8Tt/XVupos6pLsugjUOt5SEX6zwBtrJGdxilH5WiBW5tUIpGb+jHywOqVNcGWyfdq+slj4iiUhb15/l6M/j1NfwgtTF/NV4DztmQZnV8Pf+y59b5wTBnvW+cP3e1nX//cx5R0vPHBIsFFow3uGw4TZmXtws/vS1c3yamKXE/VWKsHKc75jRTWlkn+Q7TDLZZgvf4rke6QJpOYA3DGHh2+YQgDmcb/UYQO9ANDh8s4gb6zYALC0yRiyeGRQzX797yf/yfP+PFByODa0iOvHl9T4vQbKUWlI7YVBtWXVkn/4Jdc8SMAe8Ebw1LeW8NwYN07vEBnJ/B13M0/6lrfU5iDM4qfVtNnbTxatQ1g4pVn7giM2cqcHukq54ddo1ZsyD1lwKT8K4HLKH3a3QA7MaOnAtjrxmKMR1IS6TzHSU3bq/fcPvVT6jynCVWYoqkOIM1CJZaDa9+9UqHPBlEHLbv6DqL88K/c/kf44PjVz85QmsEG3DW0XVqoPH6mNh2lbGD3m6Yp4wx18xlQWhsxw2te4KRok6wtMd8z/O617tHw2KaKH3MqD6Klgi97m2yPq+CwT6YSKlrrGlgKnzvd77Dx9/4mIunQh3B7y6pVOxpobmCt4WUK2WuXOyvKP6ef/zjyI9fZbrnH4McsFMGCrElslkpr9WTY8bZif2up99d4vsNNQupJW6PC69iwhvLhshgG9+4uCCEjtvQaSFUHd0U+Xn6inf5QK2WkDImQb1RrdG0TOQU6WxlOwROpwOpgTjLdhzxPnB/f0tcIvvtBUYMSz5RvcEy4Z2nC4Ijk0rUyIPzMrKW3vekXDjcL5ypEyEEjn1H33Urpb5gjdrTp6QuosuaNeacUyRwZUlYK0r9O81gNCQeGrkWdpdXgHB9c0vNkRdPd2zGLTFmWtOGR8Qo0uEEFzxD12ManJYZZz05rw1bp+ijOEcumVw049E29RxoFEy2X0Mm53mmGVnppmumo7UPe54RgwsOaXa9FXrQDENPjGmNFZF1cLMifKWQc6JUtd5fcmTJiWYqIXTkBHe3J9ywwaCNg5P1e7dIPwzruaP3uS2QbMZah3WCmEKtWVkZXinEKtsoOBlVn2v1T4xqKOSCNio16R4SOr+yUXQfEiuUXPHuTJOHbRCEgO+3xFx5vuuptRBLIgwbRbOMat7SXEk1c3tvSNlwPydSySyl8vOv3iEIxjhSLvwwRYYQuNoEttuei8uR7a5nHxz70uiVvMVm6LGuJ6fKX/z0K15eH5kqXOw9/+z3vsG3PnxBzoXbu3u8FUqnv0sqVXMlmyGmhGSDd4BdmE4zi1Q+vtpwxNCWhrTK8T6BFXIqkAtD57E7R5XCfNLXoOscnYec9IxfYqaTyosne2ytLCVxiguds2xD4FRnPIAN4AOhS9QoiHdQNTJmSnC6z+rIaRyHHLktM3tTOc0z3gYwlpvjiV+9nri/O2C94e2bG4wR+qFn2Hr60HO58xhbqS2yGwdc5xh80Pgl27Hff8ThrnFzP/PLV2+5OdwRcyHGog6nNWOdymnK6yO0RM3q6GudnhVP9hu+8Y09IpBmRaiLNYSu0PWeyxcXlLjQ9Y6dE1wopBSRYNmEERFPYKLSGIIihx8968lVEAEjlWVSLX5NGZw62ltfoFmsF0I3kCrcH0+43rHfbTHWkvKM0OgGoVbPrne0CreHRKGyxAXvChcby26/IfiBN2+Uio7L1JqZS6GJIVXIMZNT5s19oh4yU8osS9KIkYcDurItGv3lnWM63uPtPz3s/+31m3H92s3i4RiZ54h1qiNw1pDbAiRqNSgrX2itUMqEHRrihN3G0ZnKJy8+1YBdafze78wghjwZYsmcTkeWVPji3cy740wrDW8V8frZl2+JreA7hcKHLhDcRrnSthBcT62ZVhPeb2itarahhSe7nu125DgfyEXYBEUWjYCtjeAdySgxtPOOofOkbWAIA5u+x0ijSsfgNngpGKd5WV2Z2bqBZATvHpuwru/46lcHjIHdztOqxjxU6ZAa6a1wtQ00LNY2xs+fkyJqXdwLnz3Z0lzHffkmKVVKVGODJWU+enrFsPM0L0jtqC1RcsUsCScK63vRMGFTIZ4qX/z8S3IsWKvF4MoowiBQs4I5K33HyhlZXGmGrT3ESLR2NphZ//1eMwSPDZxZm0zQ4lxNY9p7dCbN0gRtJIzRaXApj0iRiGCF1S1sNcKprO6BYEx9+PmEx1y5h8898/rW5vLcu7zfVH69kdOfN3G+H6th0Lnx4/0ifIWxVoT03LS+b7pzprt+3YhlNVUxDooWMs+ePuHFi6d8+vkLnjwdWWLieFyYpsjxOGDErBb3Te3Xk4a0l3ru9B2p32BHjzGQ80IjgjdMB9XSkD3VO5qcON5B1yopV25f3wGNMJ346mdfMM4DtVfd8SyWpWTc2iZbDBRoTRFlYx1mNUcpVRuwzlktJpeoyKF5j1753h5yjhxR99JzU9keu/jzelrvuYg6WZZaKa3oszQr1fKcxL7+f2uKyMpqMnKmrr6/RtVt0q5rQjOivHfsx55/8T+7xwJPL3YaVp2VyeDcSG3C4ZixtvDZ5x/z8WefI5cbbMx0pdCVBE3t71tJfDQE5pTISfWTvlvdo8WseVRC2FxQV5ogVmjBs7TKdvB0XsPfi7e0qvS+rjliKRyx2JoezAfaOthoDYSVPvxw3xumRNp0zfHVK/yFxb/I9N5jrVGXOwyUollyK5ohPAbdv/jwCZshMl2/Id68wZ/UsMEXjYKIJpNKo0yJ67sDNVXGsJDigXZ7w/T6LX/0y28ixvAvfPBTmoFYI0Y6TDO4TlnXwTWCFTbbQOcCn1pHk4QUIaQFT6HzgtREb3V9i0nsh8oPPr8klQvIDYlKJS850WrFeUXOpFW8FWrJlDUiJ3QdrBbwOa8069ooTy8wInTC2swZasrYBi4VjZ3Iir5ZZxHj2e44b6YaP+P96hptVso6JGPJ0pjmmVwKpergLs2z5sK1pjmvs2NeFjCWebrXgY31HH9+R22Gm9sDpWQuLndY55nmZd1v8mrC4jXz0Xl679dolcLY94ouOlmNqfQAGLoOsapzMyJYp4vLilLMzsM+NWA7nwHamIbgV21iW2UfqyyiNcqZLpotgiiKue6ZiFLqcZbsDDSLOM84bFf3bMH6QF4avbXIZoOV+sC4sSJM80Ff7gKtFj1HRNHaVCEuhVwXpctbbTq8tRgRqqy05un00CyWs7HXqawuul4pdynpUJqq0RvV0BDSWXPaGq6BuI58PJJgzQiuOlyfZz1Di1LU60qX3vYdFce4gZILKQe+/fxqPYdU13WYFz2TxIBFTXKKal/vk3A0hXI3k3NGrAa038ZGEY9zlUTiL1/e8OrdUdFyY+i7jlo1hD54h87eDN56isD94UQumW3XMR0Kf37/jn7scC5QagJTqDURRO/3dD8RNhqfUHLGO2EISjeMq8tRFwLbUYOVMg3nBdsqhspSJnJObJ5syEulpYXiBGMbr94caRSutj2XXSCfMmkpvIz3II1gNeLEeaHg6Izh4+dbTFU9rLWWuutx3rPdbmjlpCyyg57nph2Z3AGcVSkKOhxJNRFk4MneMXRXxFQ5zYnjYSI9VZOr7XZUlLM1TCvEXHh3O3GaFqIYNsHz9u0tYLBGaLUy2cDh9YlCpevuaLXSdQ7rlApsOKtQjnjvGDeCSKVywluHEIip0eaItY3BecQFukHpviKGliviDNv1fS3J4WSk9yCuoGpYlXQEo87mx/vyUGN2BjPFAAAgAElEQVR1XkhFuNhtdD8LYEzm2aVmecci5OrocyGVjDGOhkb/LLOusZgyuVaN30iNeVFUfRz9WmsKqYyrNOu312/i9Ws3i18eCnkpOGvxKWFMIbeCmIipMIpnM1iWVInlCPao1JFWMTWznCJpUaOCrrc4GnYAaYGu1+Zl7D23p4G4JEoxTKlws7vj9nZhmfWgT6YSQuPZ0BG8wbk1xqHA5V4INrAd1OY/OEsfhIvxCrFV7W7WTXfjDa1ok5BaIU8FXwv9ZsvgBWMWaqvY6qBGcjWIU/1jax7rRWk0VC3SEHJJ/OrlNcPQ0Y9bckwMVkhVef7OVja24pywGTvkqVlzdDzd2EPSDMTZKcpmU2HwjpQaoQ8Mvcf2GqZqaoZuzbeZIyaD63oaeTX+Ef7rf3DHu+uZEAJCIUc13DBu5Xg2cKJutrUq7edcwDcUYahtPYDrOf+wst/viDGu9CDz0CA9NmyaDaaOqObxY0ad/TAaXg3r15WqyKeVR4SwnU06RC3Py+pm2d4jNzY9lJXmeQ53PW8+Z0rjIy31rKHRnq/xvn5Gf+322CSu/z3HfpxRzvPXl/XnO3eT2nSelR36vdra3LDaQ4s42pIpCBfPdnz07WdcvBiwW0dJ8CRCrZnTdMI6i/WOnBuH40yplTD2OumvGpNRm6cf9kDGSsUaQ2c7/qu/u2Ad/Fv/8Iw2KiUxdD3JLIgpiHF88PN7/vQf/wV/8M//HVpO3F9fs8z/E2IcYuOqSzGKxDmvlOcKrTRMLdAHaqsMnWXsPPenqPd7teo/0yIfm/dHk6WvNXN/BWVcjfw1PkQMeWUOyGoktBKkgfYetZtVS7Ei1Xz9e6aUHxv4c59pGqfTxO2f/RL754J3lh+tjaTmkBo6pw6a09IoFL7z3e/y/aSI32k+URt0YcAbLZLVNKRT9EV0rTrr6LuO4B1Ow02pbVBjIKP+S6w07W0IatLUoNaZkUZr3ar5SlRjOA2XiJ/0TlWjFPQ1Esa0tvrlaGG63e/wYaTvO2S0tHHh4vIJwfcrQN504l0Fb8r67staSlT2l3v6oSjtrwbSfEIaTKlAySxRbeWX45HqhOFqw2juuX75BT/9xXcINy/5T3/090CEvxH+hDRFsomkpA6GlcZudOSXX3F7e88X8iWjN6QysN8NlJyorZDEcohaiEzzhPHCuNvSW8PVYWLY7sAYpuNB3TxHhw8dpQitGoYQGPpBA6utkGvlOM9UDLU5RbBXO/tKwwZHiyt7AKVDYi3zynRwVi3ATEq6j65mXKDOj2KEHONDbI8RwXQd0FhyRpzTKIZSSSkpKlYbLSaFLQ0YsczzTCqK/Ly+vtdg9XShzWZUquUyduuwRveJrus5TqrZjHNSu/vjASMHlpQf2BOt6pDOO4t1mtWn+2+lVdVIi7XqrN0KIXhC6GhNh5jOacC2E42zMsbQD2Edilhk1eNZow1n8J5aMt6r3r4ag2lqrVRJSIlUDDe391zuLhAqjsh+XDVqTQv5OVXmOfHxJ8+VWloFwWCkUI2yjc509bzmb4Zu4HB/4O2rN0qJKw3vTnz4/CkpFZYlq+64Vqx4ptORZbmjH9Tdta4aWecsPqgbfF21q1YMrRZqW6DWdWi1Eu5FCCZRqgazqDO4juK8D6SUIC04lM6oZ64OI2iNZ1dBWT9GNAYhqVO6s44k8jDgzEX3rZTgu59cUFplWhIxg2nC8aRRWbkW3tzNHI4zIha3mjDlrBmbtEqNEWh88slHpDkxnSaePRn44NlG93HbKAmcWO5i4ouXN0TpSClzNVRePNkyhi3HuwU7CCkljnPmdmmUfOD5k6eMoyEeGjlXutHhm9CqJ3QgAUJnmGJiOxS6fmQ/dEgpmCsD1nN7D713DF0g10wzhmlakGZwfQ9VcE41uUFhOJy3HOaCtZU0r27EneGQMqEMYBKpRAAsFtuhGk7XCCLs+y3tcoNdy8icC7U0BE8umSkaej/g/UCzFqLj+v4EptLEkBEuNh3zNHA8LZzmRJNOddHG8O7tkZh1jJnSQoqVolsBlYS3ht1uR8pVKeeitZMTi8FqbmbvVA86R3b9ke2mp7OWZYrc27PkBoauY7ftKPOJYBxz1Wc/dro+F1ZqCTrIqCXig8UFi29WHZqzUIthu98RYyKnhHumDSYFzAr45KRZr8ZALkpRF3FML3bUkvnly3t+e/3mXb92s/jk+Y48J0xT8XspjZKVTgiVJTfaVBAEb3vEqC2z0HDec1oO5Aq1WRCPNw3bBarxeKukqVoylkyrhTlpKOhudMS5ESNYWeln6x9r1VFPUQWLtSjFoqjTV26GpUX2XY+vlSYFFdM3sEHNTB7oO5BzwrVOJ/4tUSs4BErUQ7OClEYqPXVa6H2iNoOzPdbp1G83BrrBr8VbRo3ZE0Y8rRpM0/gN0wxzVupM59yK0lel+0XNTxyspTcVMZXBwhCEMAaoBtscshlZSiZNYIvFdiOpJrxreBe4utzjZG0EUfdQEFrOj1Q/q7rT05TOewPwiE485NQ9UFC1MMpZqWVaSL2PsK1oIufCXdYv1eZJRFY6ojwgcA9OpivCdKaHnpG/c2bhGa56n2r6SE09u+ydf872NeDqHKdwBgcfOJF/hTLK+7RG3kNoHlBI89hrGB7oqOunabPJWlxSH2mX631qtdFMxtiG8UbXakq0CFKd0tVEN+NUEqlUUm00LKlYaHa1KE+IcXgfELGMg2CtpZPAzfOIWI15CEbdJDEdbrOlmgkTAAnczbd04efsP/iEtpyUWjdHTO/0fajtoSkW0ey+vGoLBS1gzmjtuclvD3fo683aeQ093Lf/l+t9cwuVkp7v/XvPvp2Xo3lvbX7dgOisx9U1rIWXPlo19zDitNEEplW/g1U6jWqyzj2lNqcYS6qG62NliYY6L5TjRAGk6j5UF82dOh0XYk7UWnGijZenUUzHv/vH/yYG+A//4L/AuaBmEavjrA2erveYumZEpoTYRms9Rgxd0fU5dxuse+9e1fOirg9RHcZoS90PW+ywZby4gu2AcYXQDSslsK6vQqOt1KbC+b1pgDboxjm6iyus7Ml2wpZGTBkphXo6URFympHQ0W02dOMJEXhzEj4JI6zNiRFLq4LxnlIWpevHiERDWRLXpxvebj1zaBynwOlmTywzxVSs8zjf4bzneDgiwTIuYGrj5uU1424GgdN0R5pnrPWMQ6BmbXqvLjdc7CreNtyqETxOk1JuV+RDgsdUjdZxYpRm1xrNGGwI6uZZK1YMndfcTBdk3WOEh+2j6sJarK5JJ8pUEK+64r0YrPfYELQ5RAv6Vit5XjBNDTusMZSmDtIpZp49HagVcjWUVDkdj7RVf2tDWOMfKl0XOE6JlDN5NSx5e+NJtXGctSFbcqVk1faWUjTCRdQEZ1rSOogD09Qop9aM9Z6x1w21loxzBREI3pNLorXKEDNWLH3nsNK0wbIWaw0xRWqpyugJQRHsWhTlF3Wmy0W4u5lwdPhgEBMRaeQlUcj6vsbE8XDiww8uKDkjxuOc4IKjru+9iEWsNgliHa4LSGscb291UNUq1WR2FxviVJhEh0m1VdyKMsUYMdZgqmXJKpWxYnHWqamKAZqlVEOpmZoXjGhd0cpqNSuObIVUCs3qeVgTNARbIC5K9zTG0g+WVCal05+jTYJFjBCcxxt1cpdm8N5x0vYOLxZMIKdEK341V8ocjoZlsYgzWK+Sk+M0c5oj0lRGkNcBbMqRU4xQIaB11OGwYCyMawzIkhLOqQSpYXVwUaGIcIpRa5feUGolJh0kXWx7WqvElFgWaDXRB6FzwrLKVzqvGsT7mwXnOozzpByxCJvOEYKjVIi5Ejy4wRJkdz7YibNSq71obl/KhiVVrnqPtZBc1UFAKRTj8M7iTX0YbqTadA9vWWmkxtH5nmoqS85MJ2XMDIMwdIFKodTGaVpIi9Z4d3Ok1MJ+4xn6Do29ET58vkckk4FsAvvBInVkniK3hwmsRxw4cXgqxyWRmrAsMNvC7d1MXgdJS6tYUQ2kseqjcHO3YIqhZoPzjn70DHvHfMq85cjVbmTsPNMp0ryaPkozxMHQjGU+TgzdhmR0z5dcyRTiUpWx1xrWWKiGU4lI8lijQ0lrNE6DqsOUnDIu6PoX4cF3wovoM26Qi2BwiLHq7r7KRH57/eZdv3az+G//63+N23cTORWssRhj+eKrO4oRppz5+atr3rw74mXL9z5/wbNdI6YIIvSbkb4X8imRYuaU1eDi6mpPLY0UF2JaePn6wP39TJVGKoklRX72pWW/zSxzY7cJfPLBBbvtwEcfXNH1Dq9tJo1CFp2yn06JEBzPLjc4x0orK2zP/Pe4MA6DFoNFEYFp0viC7W6LWDTPCqCq42UphbKiWjFm4pzZjVusKD0nOAem8t1Pe90YY0LWw9fYyiIN0wRb91QM18BcTmpIYzsKwnYYEcAZpWFaa8k5U1PiZC2lWfrsyLVQlgVJmaHvCWHUIGdXMdnQrKd0ge999zl/+k9e8uc/+wVVwPWBVoRWsk7UjRJlmjGIW4trra4fEJpHq3NoTWm4tze3D2DQuYi3a0GoQ4SK78JKGVobwNI0VWF1+WuVB8e+ulrX69+xaiCbWqu3VleUaY34qBWN6XokhwrtIT4A8765jRb6YtTBq9b2MHE2ZqU7rgcWsA4hIDfVFxhROl5rDc56St6nPRoN95aVVlnVoVXv42Nm27nZzTVTpKN3lU8uL/jdpx/y2W4gtROli5SipjLv5plpSZyWRCoNn7MG3DZDyoWckmZdBYe/2NJSZIqaWRQ6x/wv7am18ef/60/orRpKzM1R6gi5MLuC9IFnoaeZt/wn/9F/zofPLbtd4i5Fhs4gVTXF9qxlLQnEr7pA1NQnLTTjOcVGzI/utHKmqp19NlvVzDb0gHHGUL0lZkVGaDpc0GauPVChT0vm9rTw0dMBMWdNmEYcGAOmgYgeNiWvw6nVvdasKHVrjZQqfdcxjpq1F5dEbZa7+1t+9xvP+N7nH/PHf9gxp5m/+d/d46SjiSO2zBwzJlji3Bg3F/wrf/dv871/5jtw9wXbqkhUqSvlUZT+Gu4GalFEWowWT0oNXM2KaHS73UO2Z2vgKoClzQVjPUhAgkaU+G6jLoMnnSg7qYjpEQyuVYLAgYYTs/oyraiXMQxhg7WN5nUwYk7pEQ3BUJpRpEKqrmWAmhCxbLuBz7/5Da72CdkbwtbRtz0lZYZeUYZxPlDnE2VZMH6D22/5PLzmb7UNr98OfPsH3yL8kx4DvPjGx7QYtXBNqiVsDfph4Ec8509++CVPvv0ZH78Y+csv3nLKHUs8cTppcPW8FM2fZEe9L9z+7BXL6URpFbF3tFxxrRCc8ObdGw6HyPPdhu2u4+rpTAh3+j5URY03Y0/nPKVGnE2cpiO5NLb7PRdXVwQWUorklIhxwdDwIjhrGbt+RdY8YoW7uzucV1MX3w36HsjDmIxKYzomco6kuDwMlmo7R2JYNWSxhi5YlqbFVlwmKOCwlFaYl4Wbu0zJjb73dENHKTOneSJnEOvxXhi7Hi+V3lWasfzOt56CUTTIOm3eDI2+C9BW2qf1NCpzTDp46npKKRzuj5RSVm8y1SxjUESnyUMhvm7gagSzDntUm6gNTM513RcLqVSN4mmqszobqfW+8c2Pr4hJB6mpKQWzoO+YwbDperZdz9svX7PEBWON5je6NR5k1eIa9ONmZSQYa9lve5483eOCKNtgSTgau74x9h1GtLF7sn8O5jmGipgNy1I4Hu/pB8fuYuA03eFt4HCXNfNONtrQWchpNflZ/4mlYZOnZUXs7qbC3eHIvCS64BmDwxmoJWKNo7W6Ro5ZUko0I5xSgzUsHqAcF94eZm3gxNF3Pce7ay4v9gydVXdhb/Cy6ADIGLwYNntBLgf6Ty90INXamtW6unzXhjUeYyx3d3dYZxCrZlIpVXIulJrUYZ7G1SB843c+0DgwdA/OOXN/uKdJIZcNfXA8uzBKdxagRKaTYY6JXCrLqaMXwfk7Ytxyus+8uXnF7376jLfXmdfX7wi98OxqQ/CGw8s7nj+55OZ4x9ubG5wb+OT5BcM4UFtjKOpUXqsOLsYnPedBY2PLHBeWJb33+wJ2xjlLSl73zR5Op4VAY9hvCcFjbaHSuD8qU2W3GemunA4G7g1D17MPA2lZOOaFpc2MNmBFGABD5nC7ENvq51ArTgqdNPrO8vknezBaex4PiWHoqWTmeeE4FVJM+OAYxoC1Qs6Zaa7kZJjnyrube6Y44yx845ML7m8nliVzczdRKYxDv5phNa7vTphXtxynE87dUepKQzValzmEYD3eecbBETrL8TSv53yj6y3bbWCZE9MvDjhv8c4SDgc9Z0KllgbVKMXdGEqujJseQ6ZmuLl+y3Y//H/oPn57/f9x/drN4t28cKqFQmG78Qxjx7d2l5TSaM3wrU93HKeJ/+snB5a2cBN1+hisoW+JFx88p7OiB4RviPVsd54a1c0qp8ztmyvubxNI4Xg8cXN75NWrhaMpiF/pQ0tmYca9aGy9YWxqYuOHDW1Iyp2vaoF8set1EmYMvvNIhhIX0jLT9w6DpU6NNGfmacE64aOPnrC0yJKi0ousZ9ztyTEyx0Unu7FqZIATUlxIy4Kpekh2QXDOErzQdwO+6yjLRN0GqhiMc5o7FRzLdIBa2FzsML2jy9poWKdaiZozKSVtHL19KC5oBmcu13xJfREz+pJTM94YGoWf/uwrvnrzBreiewsCzmFrobJOUhNMMaq2p+mzVEzhjChZQtDGb55PiFhiXCEZY9dpUV2nuasezdR103vgtWoz11bjGufUKTUpvfB9I5KHq501kIpcaRF7/tiqUZTVJGG1ZZYHMxND6DrVAbE23qLGBOqMt7r/rbmJZtXf6HBNG8+z+6pt5055dYAVy5IaMVVKaiRbsJKUAuI0oDmfqV5ttWg3BumcDk8IOFGUeOzUEdjtewwOS6BV6EfBGsvpNK3rQbXA3ivdJS6Z61OiSuPi6VOdkuaIIAwXe/7+k1sw8Lf/4Nur06WleE/Fc3h7TWsauWFapXLgh78K2K1l/0RY5sxu3Kwhzg3WokCaPN6boii4P6+LZWaaZ953rX3vQWLMihCCmrl4S1oaEB8oqO+jwOcrp8Qyz7Q6PCK86+e195rKNXCEM5q9/oUP+avndTcvy0qFTiBqinF3PPLm5po/++6H5Oz5a/9l1WasE8T3DCMU38ih0e83BBrc3iKSwQdsEWy0arZlLdiG22+BdeC0oljuHJi95oR2YVTt9IpWYyxYiyGD6QBLM4rWNLFUwDjdA5oItSTqitRb6zA5re+KzjxY78juyVNsv6XInVIfnaOguqkqAQGsnTT2J66079LorOfi8imff/IpT8JXcP0r1APeYkyhnDIYQ6knao54LM01aJFBLFfjnr/85Tt+73vffGiQzdVISxqHYdqIywmTMrVzfPr8GT/2rxm7LR88e8LuyQ5xW3JN5HlRnVZQhMg4R0qZ1y/fcn84cHs3kbLu4Xk54m3Dfxl49fqWzz56rhQ2r0OgLZCNR7wnOEdK614PVIRYCvPtPclYnu9HcnVq/940u7bvB11DRinAOWnYdTKWKkGzeotSSDvXs8SZeZoQEUo2HO8TxyWuDVZVE6UlMk/TGmth8d1II9GCI1ePa4Zghf0gvDtGvnh3Ry2Fjy8vGC623L5LvH6beH17JItQxEJ1kDK5RKoRHNBqZhc8Qx+42PUEb+k6RyPhfWAzjlQywzpIDf6gKKrtCEHpciJZTwfjVM9b9UzyVjWI4UzTxzy47ZaSEUQNZGrTKJk17kHzFCulRqUAF0erhq5r0EGtikZXa9XltWRMW6ls1TD0PdY7Gsp2aqbgejV8a7VSjDZZmmecOE4V52ZGr7KAJeYVOa3cdx7rhJwqbm38TEuMO0WLW51oJdCKZRh6jDiedBuolSBNaf5ZEcVmNC6qlEJJWdkDy4FWKsNsWeIb3t6cqEboOqXpVqlqCmbOrs8a2SFGMKJD0zMpujXDi72n4lSHjWFwG6wXPZZFyMZQUd17yxp7MZdMa5W502K+ooPZUiomKKJqJRKcGk0ZAmLW8Hep5FxJqWGMxpPMUdkBZh3GarSJnqfD2DFPkRQTGIPvLMs8UZZGKmWNqelXWnhb6eCGzWAJ/VOshQ8+2LK5CEgr9N5xWE6c4kwxKgFyNK7GgKUxR0WePRpBcWZatDVDulIZhhFXDK1To71SCjFmxnEEDCkedUAUk0qHxFJSYp4TXQikpDp3DIQgeCvEFHm+dxgKrR5wHi69BemoRmnEy3wgpwnf93TWkYsliFc+SJ1Jc6RmQ61QiErMTpHaVD/70YtLBMPt9TWdNLwrFNGs2CyafXl1sWOee6X270fm/UiKeo6LE6ZZZRLTtBCdsLvY8+r6LUss3N0rC2HOlRRn+r5nt+1pzjId73HR4SUwxYh3Btfg9XXk7vZIdR2uFKw02k2mYek3PaYUBtMIXqguUeoq/WkKHlQrpPzboMXf1OvXbhb/4f/8S3KbVQAberz3dHYh+KCImXV0veX3Pr6g70QF5euk1DvHT//iJcukBh65NnZ7z+FuxojjdIrc3R/55ZdvidXQjVsKleO08Ef/y49gLbg2Y8fN0yuuNiNXu4GWBtLoON285nD6BU4sm7HToOxW2Q+BcfRsLkas6/C9VThJDKPZk1OkWqF1KH3QWt7VhLOBLJlalKqx3B9pRk12vPP04jTA2XsMo2o+xNKoKvgV4XCauDvcM+47bPK4EBQlXBJeCvFw4vD2Bh86NmHH/fU9bnTapDktDsVZ+r57oGUK4FpDzIq8wYPWyRlDMQb6AffsAoLju3/9M/7oH/2KL65fY2yl2IKxjSYZmkXODFNnlGpgYI4zjaYbIpDTvE40UVqOObuaOkqNlKIHqx5hdWWGCN4rmhfjogL6lTosovbXtahhiXOPdNSzjsYYQ9d12iiLrAYOBis6yW9r6LUIq+6wYMRq0bIGBuWYH5rHVtX8xBkDrWLd+hq0uqKba/6VPKKbvpnVAl6n6KtpPC1mLoeBzbORpxc7XC08e/GEeToxZ0WnazGEbqRVuLu9Z0kL+8sNc1qoBb754oq/9Yf/HN/665/xJGTyUqgxkUqhOseLYjRH7pTIcSGq/zQmnO3e4ZPcME7Uka1oqykWrKl0fkIwvHj24Qq/RcR56Efqs54qYPoB0xx/+IPv8yf/wX9LyR2telw34P2GVG6oRSnk4ixSHMZoCLcBrHePetGihVlranpzbvsrTSXCYihVJ9hiLeIcLFERwLPh0Jk2fEYYjepQ53lBxONst0ZzrFzWpoXh+9zWc7N4RhTfb1Bbq9p40tQFUAq3c+LFi+FBR+mspaOQ4x2tzlA9x/sTthVKi8R8onR/E/t0Q/nVa6UlOItsAsrGr9hakZKpMdOSIgGds9Tgae+55jq3okpngnKtmJbArhoRhC7ogGKaD3jfYWyj1owRdVYVIxRjMKZi0a1tnYEoWmCgf/YU320Qt0GsBxZK76hikBUlzjRqgdF5TKtqAkbh5nDLL7/6Bf75zItxAzhyW2gIkvJqMGWoRpssmzI2F775vGPzwWf8g3//f+Tu+mNM1KGAe/0WaZlKpRhHa8KUIvH2xO9/75IffXFBDgPTDPLyKzqp1JQYxRGMZW4ZCY5YM1IqFzExlsalh+LA7bb0/hJbM+nTb5ExiCTGTc/2ag+AnSeMaZScWZYFJ0LNlRACZUkPiKA1qz62OUDXiBhtpBsgLug+YgzkRFwixiq7IuWMdZ55OdG7wnjhcc4yHxu2eV682OGcunXGmB4cUq21pASHKdKbxpILGcNhSbw9nPiLLw6Igw+vnuODI6bEX/7sHd3GYoeBbsn41hjHgT/9s58SS8WIh9K4u72l6zpaKbRWGbc9Q9dRS+b/Zu9Nem3bsvyu35jVWrs4555bvDoi60g7SScOnG4gSxhLWHSQaNBB9IBPgPgOIPEpkGggd+gCAmSELBq2QHbiItPOOiLei1fd6pyz915rVoPGmGufF8atkIBsxA5FvLjvnLurNdecY/zHvzje7IneUXNlzY21L5yXhdw8uMBSKpc1A5Ep7phSJIbKsjzaGaiOOe7wzhyfW1sJDm4OB1JKtm+nwKsXN3iU45SYYgJ1aLD4p90usN8lpmhniNSR3SZmotNbJgaPS9FMcZrlTQoQZFB2GywFumbmacKJY10zrcM+TmBxcyDdqOc1E6eZ6Eyfn9cF7xxdOtoqVQIqgfXhnd2zU2DJmfufnGxPQUfMB2StNpHxnt6amZEM07jd7ogI3EwT0cHhbuJXfu0jfNrTW6eXTM2Z9XJiSp5cKu/en1hz5vF0HgCp53y5kHMjpEQInuM8jVrAQNAa05iWGaul1mqTs+BN99+VMHt6leF2aVRqVaUN5o6EIdPBGqjWA/ePC7VXpn1i2s+oPqCjEWujOVRl1EBh7MdKWY01JN60de/evjdNtbc9uHdY1gtvv31Lp/Phpx8jWgmSScGolT5kXj4LSAvU0nh+e+D5ixvW+8LzmyOvnt+SF0F75s3br/HOcXs4gFxxSHywxlDV8/7NySjK0bQMXmAKnpYL58tKTOYD0Lq5Gj+8f0Aw+U+pje4E2onDPLObDrTeqbFCNtBPgp1feW3kS6bVFYkeDY6miVYqoRd8cOyPM4LndC5clsJ+Z8aR6/II2hHxaINaK2/fvgWEEA5ccuO8LhjE6tnPR9NR0zjsk+V9+kichfluh4+RXJTlksklU4+eKUV2+x3f//hIXgu5mUHU+7cn7u/P7G/2xGQ6/nl6Rq+Npo00veAQd5Tc+fNv33HcJ/CV4y4x+8j7b85cauFw562Orsb6mdMNvSmvv71n3iVubnfwsPL6zfpzdCO/ePx/8fi5m8Xgupmk1E7yew6TuZC2UoYAO1B74M3DiUBgf2hmtNgAACAASURBVJxRlMfTypotF3AKDi8T0FlPSl7L0PZZXlJIe/IFzhcTYq9F+eTjV7y/X+gi7KZICJGi8Gdf3fP5m3v28xC3K0Bn3mVudhNTcPSgXC6Vt3WhNTP9iCkwp8TDo03WYjBq6mVdiClyzG2ENq/DdMCNsNuGOtMsTNNEV2tYdFDMUoqGrmGbUOmVVjqP9WJ6ukFVE+3UobeMo8mSVrkJYWQfbW6XttNlf7YDczfhYyCGQENp3iZXruvgsHv8tOPbdyde/+QrUjrw/u09+11gdxw23sMhsIuzSlIBNX2FiFFDZbz01kChntas4Wp1DG260qQOeqWJq0MI1nj1SmuVUoxDn1KCgVx+13RkK+i/G3YPPzuBdM4CZM2R1f5eXvPQKdqfzZXPDVcuuRpFlFKveks3qLKbPlH7KEhGg/izZivY++2KjqnlgHjpGM2vtQwamCdBq4I00hQJySi2znmmmBDgMAuqjdubHaflAn7m1W7GtU7PBTkEfG24GA0hl45LAe2BPkVim5n6ZizgroG4rav9mQ614YY7o2C0IRFhut0PKuxk32FtqA/oYY/uZlQd8XjHZ5++4t3br/niz37Es2d7M6aoQ//XoDvFi6NrM0rlWLtd+6Dw2itv7R7b0vpuAzjoU31Y15slv00Irxd3XAAbDm7xJk9mONtzyvaK8mTbr31bvNu6GK/sBzVNsfvQXoG6LPzuD/81/upvfMqvvjrw929XFPhrP/xLKB4fE84LZVkQrdTc2N8+46PbI6oFt5voRHD+OhWgK027If07HVMCLJTYjUU7DCU4HsfnHu9z3HyWCiB2f4ZBwVYMKXcdUUeTYDmYbJrfp4+u3cRm2/edvMO5kWtZK+QF181zSaWOMHYzBDHqrE2TO5WSlS9ef8uLu8jLGOjV4Xwav1MBh+uR0EfAN7aXpmnH0c/4FPn6IvQQcCj1eKD1QtAw7knFL54pB+TZB7j5Ncwz/ngk9g/xoTFX8E3wAqlVxBs1uveGv6zUWpmaUnu3hg6j1y5l5eH+kTkltIJUo116EbxhA2gLxshoBsSVYmtxPwxjsog1V31jGzhUDSCrZYW62v3ehdoqbgTZa8f0a90cm72A68qcBDlOdDpeKo5ACo4QJkrb6IArSVbER44hkKtlR6aUeHG75/yw8Hh/ptSO84nD5HlxEJYQuYm3gBInz/53fpklK8tSBkPllv1xx+nxRO+Nw3FPmqx4Pex37KZIFFhy5v39AyEkpmkGEU7Lwvmy2tpVY2nMMZCruXxudMvelVqN6u3HhNtuAXNolV7IOfN2tfD5lCZcs+bicq68Fbi9OdhOMu5f7wUfPKHL2FfMwGme4sBJZWgvHdMcQYxiXFsDKuIgBRBn54Z42y9CsEXQymoT0GHis9ZubqUiBqaqo5YVF0akyNiqNiMkBv1WmsOr4NRMe9zGgNFOXS2fcz0bEFFZceFs08Pezf9MGZNlc2vHeWLasdNhjIYZBOU1gzhjmNRRI+TV3NBDtAbIWcMSnXt6Lrd5toN6pY5G1jRjSs8G3ppjutGFe6h2z9DNZVfAaSYGO5cVi2jRsacbOOvGOdtNcjLO7xHNbBE9YvpU1YAGuxZddHhEmIa4t246z9rJuj6ZfhUlRD9osI5ajMnkQ+Du9tZywH2yRrZkAyNHM2usE/v9dTFKsHPBnJCx38trZl0zuVaCN62kc0pZVnAWD/bwmFmL8niy/TI4NSATZQqRGAMlVwMzvYH7yXmIkdLBx0gphW++fYf3gd0h4aOn9Dqm8jpqkIY4N6JhdNQiDTZHeCB4R2kLZjprJ+OaM8F1EOV0qoCB7dDQlpGh7K15pVVFeyF5uzfcsx37KZiBowfnbcKda+dSlMfLhff9glMlhc40eWieljsPZeF9MfdhfWzMacY7T+sK62pGN2JSjZwzc4obV+gXj7+Aj5+7WZwnx/19I7dK9AslOtDCpgmb9+bEtLw+02rn3CpVO+elUrsw3U124HuIB8cUZ14934MEVBulFj54fsv50vAznC8L50vmg+dHvn1/tsniHHl53DNPiRcvbvBB2QVPkGCLkgvee/bznmlKHPYRtJNzQ/C4CL01cmk8PlgQ/aKVUldyXvGucD4pKQpz9NhO22l1NUqZ2EZXp2bFShxRD3hKM9e5nLPpOkZFkk8XnA+4UfjWocFI3tNWRV3jfF6I3tuh5x2lGm3DOUc5rzgvtC5431j9atOFtDdhc1M6Decb025Hfrhw+TZTZOHxmwdu7va0L76it2ZonhqlsMtglbUtV9CRpkhKabh+mm18SumK0uVaLAuREUpfqrmoNdMyxJgI0SIYnDMXyBijPV8tFgOSC847cjGb9xCtYba8LWv4nJg2TzZ0cPxXUWKKgxLar5qg1kzo/0RRHbrH8V79FK6ub4LYJixPDevm6roFqGu319LNPXWMa1SsEHh/XnjMhdyF4JU390YXza0O105PHNS7FANpjpRmB+Duzuzz85t31J8e8PuPIYshxNLoqzkNC8K6mkmKd/ZeWxlxJirjRnakQ0I00oo5WZICflBs0stbO6xV6QLuzXtkP8PxgKZkxi9S+Rt/69/kf/4f/jf+wf/+e3zyasfp/UJP85ildlQ6YWRyIXYoBe/HtMHcH40OKmOybI5uWx/YugLODrzWyWrrZZOK2oUZzSTWZDkxIKC2wrKWMYWzgkZUhrX9U9Zn7XYNDd1mZHMazVNEzBTK2z3dKmhuLJeVlx/c8pu/9hn73Y+o2vjwl56jTcz4gc6u7SyGIFfCfmaKwxU4zOMGwtwrsQ/sMEppl4Y207h4H+wzDdMKgF6aLbQxCWQUaRbtMehoY3AavO1DdAMEaI46zHQYf/XJ1MYmjH2MZz/56Bm7UPFecb3A6S2/9eu/xC9/72P+gbMCOKo1eXatrbHtqpRS+OMffcmnn33KLwdHW07E+Qi90STamvJWdKLgMbBInGcfI598+jF/8CdvaWnCOcU/v0PmCS8RaPiu7HNHdaHunrP3f8gf/egBiUd++P3PaPlkJjJutIB2Yw4qnrArNs3BOWvItVFFaeK5OZ+4XBbi3Z1RCrua8YwLNt0Qqxt7q+O6KXFdEVHmGEaEyFMhszXffkxrNhik9YZ0SGP/18Fu6MXoYzZ1saJybRWJgcf3j0QfmMJshkooLa82fUXB2zmxFMdlKYhTDinwe//0x3z1zYnX789mzOMFbZ1PP7ohONMX5rJyWRunpdjEoGRUlCkmnhf7DlSVen8hhNWKdh+pvTO5TusO9bMZlyxG3xPNHKbOzc2B24O5+orb8Xh/4e39O3bHHTc3h2G6U9lNB6NNumr7KaZja+rMPXfkt75+OOHU4Z1QquUj748Xo/7VZhTY5M2J13twQmmWRRvThI69cYomMfBDhnF7e2S/31k+ojZCjCyP5h68IU0uCNEHpDe8iwRx7I9HWqv4YA2xdxHB4dwRMAO+LU7KdJkbaCloNeOsXNt179mcvnvL0Bu5GXSnCKorvReu/0YEVcdarJHyQz6TcyG4fgVVd3PCcB/BBYsQmSZz1J1Soqk+SQjQQXu3nzNqtVor8zRfazcnjsPODTCuDXBNWNczrVX2O0fXocNv27oexmMDIN/YOuYMq7Rme7D3AbftS44B1I5s0W7v1ccAzrHmM4gnDHCvq9EmHW48h9K70YRFArUVWi3ElFib7buoPEW2+PgkjWjWAKcpje+605p9/hS9TZrRKwCq3eyUondjCqrUppTT2UDzCARjkj2eC4foKU3502/vuayF73/4nOPs6bqO1/KIBlpZnvT2ePJSiFEHw2lbBzaNM+mJQ0VZl0JMkVJPg01l55ob0Ta9q2VabhIZX03r24xFIQIp+XHNR8O2LqzZ1kWInq4mC4rzYExoo9XOkpXTY+bdYyG3yjRFpujw2ojJEyVQqlGUp0Pi1juLmxE70QMV5wKNzPPbBCr02mkiHHa/0Cz+RX383M2iAk0dOvK+XD6TgqFAOrLNQJhjJKaICkTt7Cbj1DcHJWdyriQJqEtWBIij90YZltOdSnQOHxQXbMKYkqf2Fees6J9i4OXdnjgJOx/oKtdQYycQ3Jh3DT2fT2IUlujIxYq9II7oPfk7BwDqaGpxFj2GYf9teT3inW3gA7ntTdjdTnjv8S6MwsPE+5vJBAgpOFywbKjWOksfpU4HhtZPHTTMsMR5T3Mb1clE+84JLgbECbVZ0xVqhyZmdkMlqgUS75Ly0UczvTu02saxrtnytXwEHXEE0miAtg0XdngXxmTTGgLnHSFuDRPg1EwUJFwXRe/ZrJwbzLNZcYsHVPHODrOrs+kwwTEqasWJGzlofaCPi01KRJ5iEBz0a41t1JDequnO7J2iwDTPg864xXQ8Oal670cjaBSb6OO1mWwjdPqaw7ft5KMAtryszZVzGOmIY63K6VK4u4mmOxtoau22aXo1q3IXHHFJPDw2vCo3IgRmtK64y0J7/0hvlS7QtJgVvHpEoVwWemuoN/pmbVaIbygrweH6DsEztOSINP7m43NDn6vpixCHOtNkQYVWcc1DXdFc+eR7H/Ps1S1hP0G3aA3PMGca34k1gU9OtH0UI3aob5OAbaL11AWOQS1b3W2H8b+8u/yr0UUdNKe2TSHHOvrZ3+FpsvgdGrEVYeO68J1J4/hfL56vv3nNw2UxXdP4oTqjelatNnyXCXUegtJipzmj8dHHyhtUUtXRzADSO24AKKI6RP6MsPth/DEOaev0uH5v6kdBvxEMbBRgbXsbLokOeq3Xea79bb3WwtvDifD8bk9yZUw6lFYLn3x0wycfvyTEmXU5MTlrZEWwZlHt3QQnfP6jL3jzl1+iv7THababsbfBQlC6dAOfOqgf31mvuHzh0w9f8M//8Gv+y//oH/DhEd68X8n3ivoLXSt0T3CeGC/s4wfsU+KPv77neHvkh99/aU6hvtGDH021XDVWINdYGucd4s10TbzprCeUaU7I81vbh+p3nJfVClDB9gCHA+34lIZ+2RZWULnuGzD2o2DFvAz6f2/d8vN6MyO03pnTdGUkmIbL9qq6ZivOC7g0EXYzumRkTEU9inc7cjUQbr0IXs3hMXjPVz99x5v7zIrt4S1Xci58fT+xT4HaFs7nlfvHxut3D9RyGeY/gRQCp6VznIzZI2Pq5oPnktWid+g4P9PEcz6fxsRV8ZJxXnm22tkYk6dVo0q+ub/nJitVEnRYlkbtBec6MZrMoCPk0q25iRP72cCsdTGdrYgjJmdaXBVKV5bLikNYLoL2Sg2jBulqbsR9HbtN5bA3w6ZyXim58OzZhWd3t+z2M7WZrOR8Wuw+cVZ8+2jZizvviIMBc9jbPRucGaqFWG0qF7yBV32waVyj1YyIGy7bA6jpSunCtvvYNmiSGCdig1m4nonK0KzRryyK7bxrbbgwd2tejGnjcS4OINcTUhxAktA1ITIyaVs1ALe16/1cxzkn0VNaIyZ7f20AWtEPoFRsvTscrkY65jHg+gBVR5CrOXsbXqZquLqO6fh3+B3DyA6QjdXTrruWajeGlm4etmPCJxa74oeruncekW6NZg+YBnUA5WMKWnunZmt0Qxguzv+SS/lAOhEcIU44Z5useIfLtneZR4C5V9uWas2xaDBjw9xIIZrvRHRoUco507257z+cO+8fVz68a9zM1qyaPtlA1VYLSsaHyPEwsS5irqPNpp4yYj5kqzuGTCbnjA82WFHpCOYu2htjotvp3bTQ9rnd06Q42PR4CJkGmAqM7x9VSjEQfc0GTjkXzfRm1DO9K8FDiJ79ccI7z3p/ohVoriDe2AY7Z7TevGIAezcjQDG+L7vJ0RrkrFxyJ4bvIsa/ePxFevzczeJ0s+PD/e56aHsRUkysJbMsKz99fU9TMaRmmki7wCHBUQrr6cLXj3VkGQm8m+n9NefLo013pFNq4/XbzLuHlfvzGbwVVY+PhVpBpXN72HF/EoI78/mbC953dvuJ7caopVgOpDO08ngQdjvh5mB6pygTKXnmOaLpgVKVIInoHcRI087ds3Q1S3He4Z1ye9iZPXxrBA93L/esl8x83Jtldjd+pk+w331HY+gDd3e3FM1jMxBubRvGd3j/2gq/5x8dcCJcuhUfoXu0GQUtTRZcGqaIeNPD0MzlTVWHs55nNyUU5RgDGgPn88LnX574oz/4MRKSOSXmjOudOO/QZllZnUprgrjI+VJNDD+MQcQ5Hu63rX805N9prqx/U6MlxsQ0RaMmVxPR92FPXZsZ9YgI8zyB2Pu3LDpPrpUUEzBdp4sxmV138IHT44lS2qA55fH+xkYvEGLk+fPnLJeLUXmdFebPnt0aYr1cbBIcHNrh5vaGsq7cPzzSWscP2iZsiKTHt0EpHFM+r2KT3N54dXPDzc0tP/wrv8IPvnfDR8MsJmunqHLJSl4quXRev33P/enMw/lCb51Z4MUu8fLDO+5e7Ti9+RyajhCSMA6m0WCrmUb4kaflxPK8LOOo4Zpj+daoJa1XmjiyC/z7n4N05dF/TsAmvClOrLlyyY/4XSPtQesF1+HVr7zh0xcrn37g+cf/8JG752l8Z0bvEedJwaZnm7187+0av2AFwZWEysietqJ6rJHtob3THEbLHNSkaws3mnTRnz1AatMrtWqbPKNPE2CjE21Fm4z+S64orU18NoOiUZz5zIcvPmQfI45sIIILvJhv8M5cZfGOQsepNZe7myO3xz1S1wENW+HUzSaS3s2IyXl7j2GsqdbblYL9H//un9nE6mZnDaBuukX70rxy1R/2ap/LqHOCjjytUuD9t2/YTJes6NrouQJd8dExHQ+8enlgks+hezqOrpnj/C2/+8Pf4MOPPuJPHv8YQsM1NW0xghZzVt1Hx3/3d/4Oogs//MHf5tVxT7mcED8h3Rw9fR/uk63SZ4/4AOsZrZ1/+4efUMt7/qf//p+yOwTy+Z63Xz0QUzbkXoWQOofQ+Ct//W+i6YbT45/wzTeV1iL+8S0igVKFpha50HulNUPr18uZWtomqbZ1NMywHh5PPD7cMx12zPsDh/0NIsLSLkSxJjWEza3aWayHXQXcyNKbQyRvRfYASmqxWKMwGghqMwqYuGuqU20rrfbxnGpaYu9xKbFPibu7Z1Zsa0eeH9CRoWcFtLDHIeVizJ1sU7gujn/333qNOSLbFKKWTuuei1ocRh+SgVobqo7cVkptOI14Z8ZOcwoGeg1mhhfHmldaF9QFy0lVaCWSksN7R87CZWl0FR6XTrsUSrlQS+HZsyNzciyn99fpRWlnaJCrR1XozYpMXRam6Hl2d2Q3TXx894HpeJ1FlYgKp4eVx8cL36LkVrl/vFimr4yIBIT9bkZbJqbIumbe5csAKQOrON5+/QDfnI0NU6zJsuZdzJCnw+PlkZpXghfm/cy837E/7gx0Knnof42wV0plThPRm4GbiDKnhPeBUkzje3cTAeXmuDOjK4M1AMaU0ltjJTB56wiT2PnnQrhOtcyVHKJ4vPfM0Zg9tRizpTUDS/ow7FIG0yMEcs44sSmrDsZHH2CTqDFdnI9Ms6cqlGZgQ2+del8tK3RM7kB5dnek60QZuaEuxGvEjkgZIK/gojWj2yTQpAJCCJFlzfgty7F1go/UNaO14ENEiPRmWdnRO5RAU4fTZntJURskiOJwJDETHgPwEzHMVLUIiv3OPmMdAOOGtdW20TaFcl5ATB8sYJpK7bjo8RKudYAC65phgKZOYE7KPO1pNRit+GQa+MM8s1Q7n/7S91+Zz8Lpkfv7BRfdADmWcc8rrQM5W152CAa4oiNCqY5zLlJa5XQ+oR2maTIAPgRatwGAdkhRWdeV1ipdK845op9Go2u6fucUccHuxQFg0cxHwoVILdX2folMMZBbZ83NorNqR3tlv4t89skNIQjaHWWF08jG/vr8aPmjPnArkNuKnyZrfsXAgtPjyu1hjwSr41KCeIHO8q/sN37x+P//8XM3i7/6a684nR6tgQkJL57eHWuNLDkQzolc4PXbC+dl5fL+nuPs+OzGQ1u5u92zm16wm2aePYs458h1wWE5N7Vk3j9Ulux4XN/zeF44PV748x+95bIKYZr59MNn/OD7rwjiuH84s9snPnp1GJMuE6Z3BDdF4uTZHbBDMTtKrrjJGxc7eHou0NW45QpR1bQ3s+krpimOYF0lzuZolbohsRoj9GaHrQv4OIFAyaZt8MHQy9a7OQSuQssNnNC9IWpziIT9TOuNmhtTSnhVtFvD7SZ7bjfs+HtpFj7c5YpmGiIY8epoTch0o3Dcn3j45i2f//SB4M3YRRhUSlXypVJ6Q4cxjKgVod6bAxrd+Oq2bxryK0CvY6MZh6CO8Oh5N+O9sCwPxo0XIQTHsiyczic2F0hxQh7h6N4PqkkfbnljCimCmdhsk6CBCJopiLdGvNukybkxKeiNr7/6ylC81q+ObL2bkU4desZtsvD2zRu27L5Ns+i93RrWCNtrehG2Mt8RB9rX0VI4zpHf/Wu/zfc+jITLA643Hi8PlN5xNwFqIMTE6eOZ+9Mja81McyKfVo7HO24/eIU8f87ueKDXgnpBUsDRkWoUX69WgPZhJjBFO7i0gValSTemtEJdKq5ZcLXIcMBzlSY2BQse8uXedBGhUy8ZtOCa5/LmS9Z331Dev+WxwpGA883MIIZx0Dada03xTofVvbECtp9vk7KNXrod1tvjZ3rA68jxKj69XvPt5yrbc+qQ2BqAouoG7fXp920COvSJfDcXc3ux7WXG//eB11/f86d/9AUHXdl9asXwP/k//tCoR0Go2jhXoxnePdvzwasX/KXf+U32L27IGF3YIbg2EPM2Jo2tXTU6gpiLwugk/vZnP7YidB2TUBuNPn1R0aIIVBUfIk3bMDVS1BtAsubMN199NVD6cY+yof1GhY0hMs075mAhzXRvMJUTWN7xG7/6PV6+vONP/7gj3YqIbb8TZVA1lZzPfP7FN7x5dLx6+Qoef4IER6fjkEExtoaobdNLFTqeY6z87g9/nb/z3/6v/Hhd+ZXf+lX+9b/x27x87qkinE9nXv/Jn3L/5sTf/V/+HnvZ8/b+Pcejo7sJT8SL0bbF2KLDOK3SVYjThA+Nulq32LDJf3KRgnJez8yHI9P+yO7mjhgCcnmPrut1OuqcUNeKd8F0aKp0H9A1MM8TZTVdpPiAH/lvqmqUe+/QppSyUGslRtPUN+2sywoYjVBESSmyLOugBGa7b3on7W5YcuNUOoi3IssLpdta7+uKiMenHcqed+/e8s23J9amrPmMqOf2LvB4KcTpht1uYgqZye9oOiHeMaXI4bgzoK1XnIzYjRjxCNPQlZ9PF85LBp+o2rjenTZ8IIYJmRUJkMQMR252O6ZkjXOplcvFZBhtnAPegcPOylbM82DNZZwjSnCJNDniFOgdfIObeeKzD5/hnZCXhcvjij8mylppTUlTYJoiOS/0puzmHdNsuZpiQ2J6N0ro+/sTTiLrulJK4bKcWXLhi7cHWllpdeXu+R0vXjzn408+NBZDsWYmryahKbkaSDrOTJU+mCzC6bSgOKbdwfY7LagK9+9PLDlbPJBEalPWeqZrI0kgOsccI4I5VVqkFOSiOK9MyRgy3onVIli+cRp5deYGbo3oPE+E2HHbftzacCZ3Yx9VJFrkgbpOjObMnPC8eP7MJDzVzNxaadzfP7Jmy8S8nFebuDkDxXpnAIfjTHZ+sMPK0MxZeaDdjHfcaLBhq2csAkZVcT0izlF7o9ZC8xZD1LoZ+REhyHAt7da8pBQJTsgahimU0LI1VpN3Bqp1Y4pZLmS4Or33rlTMRbU2G0bYQMByS/W6D9s+FrzDNKudphU/GagstZiLuZusEnLesrG1IlhWpSRn4GEzsDB3a9xbMS2kqLBeiplBpTb0yubN0FqlNBt8LOcL2hqfffYJzgnLcjIjqWIuvmEXSHMwEKQFgovk3GitXM8Hk2FYVmQctOFcO3XQwRVjZtQOa67UXlhKxflAd5Y9jBP0vQEwwU1450A62guTT6yXytoKH376jJ1zRDlQtbHoSutKWUF6ozcDQYIPeF1Q93O3JL94/L/8+LmvzPndiXXNJgxPRmcTMX1N9IGb2dGiMA93sPMlEUTYTx4ne1xK7NLMnCL7ozfheU2WaaZKLkIvFa+Nw7zndvY8JuGLz7/l1Bvv3q0IhX2C3RSZoh30+XTCDRqpSwwufUJiIjKZFiUKs0Q0Kn7yTFPi0gt02zBNc9fNrbNa0VWpeGdTnXzKTxS42g3pqo3iioHZ1TZu23yMotq005qwaIWq1GxTRLVANZo4ow+MyUSufaCfo5jtDqU9IYkKrsmgQQpkm5KAR7FxP4NvLwJ+mrj0SlG5mmc0ge7MLMCpoabKU1G9UbTsD0+Tmi3QXqSbpEqN4hEGcl5ypXlnWXfjrwu28TncoPk+tQ3iZDhQ2jTlqTG01+u9U3IedvNtNHOOEBIuCKZDXX+GVlZbvVJJZUytcl7te7nSKE1wv2kyRSyzD30yUbEsxdGoqJXh9t106wWc8HBZ+em3b/lHf/Ajvvg2Ma8rUSCXhaadeUokF5imhoqytGjGMj0aXbQZtUp0GNM0AzlsmqzbwMiui7Nr5VCoOpqq7X0NzQIO2QUzIgieP58zvXZ+LR/GFXZo9KR6xPWKTDtCnMEJXh1Lb0hLRA1UvdgEfUzCZUh3r/PlcZja8T9oVd5E+FoH4itPy4jr7z01/4Ms+p1f+E5LqVvbub0C18bPsi37IBA+sYvkO/8ZldTPPudYc5vrKmP2uJ9mdtFMgv7D/7GaV0UXmpq1u9Fw7OneffOWXYo0AXEByXl8tzC8bRA1bRF1y9gc76mNKarI0BJi2VzDXGJkYNjk2G8j03GFR/M5ZqUgjrIUvvnyy+uUvxv/+PpdP/3T9FA02OjIEHFt5TjfsZ8nUoy43hg4+9PlEIb+qfPtV9/w5k1Bv/8BriuusLPrNQAAIABJREFUN7pP0G0/6JgmVjEDHucc6hpeMh8eJv7Zi/+U2jr/3m/+Hh+9PHCcbIKWD44P/Geczpmbrx94+9MLp3ViN0+4VqFWavK4ZOCFjFwQ1YS5BQbEd0SX8XkrvQttAF8xBqaYmEJEMJ15FEcLgd7sO3POMSVH/840whqcTq8GvlwNP8Y+uukXtVvW4DVj0I09XT1pFsAjoaAYUyUM1sb1sAAkBhIg3uhhpRWTwCoDSMyAw2VlQXhfO18ulVytuJSunN5Xains2kptnWnq/PT+Qi6mfXTecXOzR7UxJzOGidGPCRj48X6WdWFdK13MadVMzrC4hLUiLjDNjpCE3XD3nuOEc45dSgB0NU1TV6U2o2GnZDTq4AOtjolYb8QUSbESlkCMntah5LHWRyMkIoR9NFfG8RoiRhWe4myNhY/W8DSjDotaYxS98Or5M0JMPD4+0ruSy47aGs9egtNGzwvHmz03N0cOxx2X5YKQEA3jNrTNrGQzCpLhpm5GHUq5MxlAnCbTuQ6H7fNtZM2VXBsSLHJhLQu9N9y2J2p/Ol/GemrFDOu6WPMSg8eL/W7vlVKf9k4/mAylFFpvJO8puRiI7cXoo4OVsq23lgtd6hMzCaEN+2Q3ZCghCjIkOIdDetqjt16wmcRk8FbtfW7O46qos3vB9nqLk+rakTHZnaYdFq9h6845TIIzKPZejaCrwVlkDqa7N7d5ux+rjuZj5P45oG5C+dHcOm86vO081Y03azzz8Z2OCbt3bK6uqhtoHGw/9zqo6uO7HKClSVTEnPG92DoYrrJFOl6NPh/VEVRoKHXsOehgEHXFB2sWTRdqCgWVRnCBlHb03rgU0z0vq32vazbGzfL+wnRJbGaB3o3zt1fbI8d1FOlX53kEUrT3vh2iJvsRojNm0DwJdXhitN1kbtHDc2LBooBqqUAzHWxplNr44t2ZFDq3yRsjZtRiTsxAir5Fs3UQ85j/xeMv5uPnbhb/ye9/yWVZ8T5enS9jMC2gjIZHEMKEFbZqh/apCj7ApJWeT1zEcf/gkKDc7A8IK6qNUirv7s+8e3fmUh2XNXP/eOHPvn3k4X7h/X3GfRn586/e8uw48YPvPzfaQh1hoJMjuALa2E8zU0o8uzkyTR4vHT8ylZwTYvTsdsnoGmKREOt5IfjI7UcH6lrIWFyBB9JhZzQaOrV0fIqo81xyHSHgFSfmWBcItNpp3aidpSo+BXwyvaNDR7bZ4I+L4HY71DkmP4rEoXncpmqmnXqihopzSO1GLfACYo5qM4FerfA4HAOHO0f5qSMCqqZRxCXc4LXXoQ8RtlBlvTqIqlqR670Vz9aYbdTPoeEZp8eaC5tJjqqNAObpwOGwt0nDQNFVzRlMnIyN1uG8os1oXVvxsk0GN/OZLTJBdcUTrwX/tYmgX6ewiBXY6o1CAaP4G/borhvNDGyfdH0LEx5GA6pGC3WWEbYJ+e37L6a/Ac658JM//zGn15F99OxTYkpGXbx4cF7hdLH3ILA/7LnkTiBSmtAeTrQkSClIh+4FTQEI1GFs87gW2nemTqJmEy9OMGdMZRcnJJn7nhMhCfwXn/4hqPLf/NFvmb7R27Wb9olUQGdFdiBhh5PA5O948eHnfPbpj9F/8Q95f/J8/2BOqudB+xU/qFwVa66lUeWCDGfUOdh1KHlovvqTa+l3ziS2WZj3Y6A2cj115DPKGA247fqII4i50F61steVZ457Im4UdsOhL4bhgNfRPu4jNbR7azIvpfHBiyN7D+tjZimWz5Ym084EEZxL7J0xKc7nM9Mu4YJDsYgCy7lQWKtRs7yj0VBn97hzpo8pvY/o0c7f/fwzHMLf+qWfmFHOADs2+yXNo9gBWltwKNa3ezPwcMKldn789RdYq2luc4KY1NbZeu21Us8XtAB+Bs6odHBmsjDHzm9871M+/+gTfvz5Twwp3gx4nKeJ6abm4Pn8z/6IP/j9z/nVX/5VPk0TUhoyzeDrlcbeuhqGOMAa5x20ykE7uv8ML8Lf+MGPoWSoxdbR7PilX/4ABH73tz0/+fKet+fCs33EXy6oc2iwZtF3MZdYJ3iZjXarG90/mZ7JWYPbmnKzn8ApKc3McyKlQO0WB9AH+0B7v7ptllpx1dgfNikxAwbvZ9xkuYEiYgVgN5OIba+qCuK8ZcXVMgAyE6hNKYKLti7FFn0de5tptcCHQOwGNKxrodTGdJgBT80YMwDY3b5kfrnj+PFCrebAiFYm55G+okumVaXWjnOZXDqXpZiLcR45nGlGu3K5NKOJOaH1FfGYjsvvzY0xzXiFXgv18YGGktczlwukmDjvzVFY+wlxtgc4gd3Oc7jZk8uF+/sHahPU7cmlUtUmNrRMFHjx7MCSzRQsOjNyup2NklkMcyE3M/pIPnF7nIheyXUBInOYTD/Z1SI/1oUgnfuLlaPew/G4pwF5yfRqmsKO+S8E55mC583jI+7LB3ZzotTCcT+PwtzoxIfj/kpl780YQ7VZ7ROnSPDKUdTqggEs3BxnDr2TSyHGMFgZFuvknRv6czdo0wbK+G71QUOoW52A4H2g0sceabWVU0/wNuGvtY091hGc4r3pGts4y+l1GG+5AT63MUUsLFs+7sgT9s5C1J330Btp0IRFddAZhVomA5ScUFu3fOkQaP2Jsg2ONsDi2gZAO87tzd21bkZ0veNioBUD6o2ab3T4Xi2PMTg/6OJGt/ZqE9RWK8E7PLDWtsHf9pw63MyHXMH2f7EJrLbra/c+dP39qVFEsZxmAbxRpHu1n3dsGGDN/vYrid4dpRk1U9ti39eoNWI39hox0rqx2TamUG15lDQGQ82zjLXikIPt8qVaTmGc9qYfVI/0RmmNfKn0jjnA906cjKpai+ml7Z/WFG9O8GkKBO/xT9MBa+I2Nl10lGJU1/08I9qYgyNIMLMy56k1Ubt5jOx3wlqUH335htYaKa0DoNfhdQDQeHY8mNmaV1rp13X3i8dfvMfP3SyecLy7rEivJLFYAHO9HMYetaEqw8imEJxnTpHjIeKkcbNLxBAIzhvlcBLmcKDmSik2ql6ycKnC568v3D9ceP944ZQN4djvdoQ0EdMM4jktQGuoRKbJMeHxsuIxFKiWjraF3T5ZmDxi4/la0Vb59Puf4oKjaGXNmbIuzN7z0auPTFTOaLBKZnd7wHsz9im9IyHQCkjOBG9mA9Cvjp6tDwqPd3SnVDc0Kx1kTI9EHbU2mhPTY0WPi9Gs2ps1ZYRh2iAwuB02wXAB3DhOXLfGAYfIcLESxVO5PYxg4W6ZYa4P97JeRtg6WINonHwLXN82D7n+TMcuanoPQ7Zq7VjML1eUcit+Ba6xGZazOA4Lxmbc+jADkcHAk5+ZMFoIsVXMIShrM5S7ZzUh+giojzGMSeRoMgTT9I0JrRPTbmyTkn7NZxwTSMYWqVybre1zO3UYrGn0MAmCc9BK4/Zw5OMPXvA7v/IBd1FhgjkmdnOC4FhnTwbyUjjfP4Ka8ZD6xi4I+ynhxKNrpwWG0x8mAuepeDidTjbhGqYCMUykyVth1QpS26AGGYKKKpxAc7Yp1vt7UIheICZ6XagjgNw5wfcJxLF/YaL1/XFP8o6ldObdhKOxPmRqU1wc07DNBU8dqsXWLdaEP80btxWk12tuTb1FkuDsELHGXmxys/1dsYN5o5huk8jamxlJjfsS9KofUjVUtGvHi+l3ai3WKDIA8FGYIGay01rn8OyGihl/5CbskqMnR0BIIeJjRFzjZp45PD/w7OUdXqC3cgU9tEGvHTz4aDTl7d6yNTyanG6f9b/+Z38ZEeHf+cFXBtB0/Q62OgrBLduzDbt/FZw0pDUUoXtPFSWKo47MVXOcUWSYY7XWkaXaFDvM9H4xUGnQisLs+fTDD/nw1Qf8yeefM4mOie3wZxxPuZs9y/LAl2/e8O1p5eMPZvzjaoWfMz1QLeN+m01f1en4ZmZo18gOQB/O10KSUfj2JaNLZp4nfvD9O5gTuhb6t+/w846ezEmaNtaEDqnooIwqw5ofMcMnD75VUoqUlm0OPQXC7oDvI2t1nAFVLTdNxQp4l2zVmVNvA+8G3crMhQTGPliva1PEKFxmKGLASpSN2lYMBAuDXqaCVjNtM+pdw3ujGJ9bZZt4N+1M0Vs278jQTGni5X7Hy+d7fuWjRimd+9MZT+eDZ0fqsnB+/8j5Unl8yNx+sKPrFvfQ6d1Rc8dH98SkEUdIARfMJChOeyCyirmP+jBT1zPt8R1JHHldWS8V8ZG4m1lPF5zaVKiU1YrEmEj7PefzA+/2idYDlxp4WBfePlxsx82BIKaLLbmxlkILyjwFWh1GK06ovXN//8iyZHbTgZozzlVyyaT5lnMv7A8JdUKuhbJc8Np5fxHQgtB5+/Ydp1wI4ilL4XGpZIX3i4J6drs9jPbss49eEJ3j7mgGUq/fvmNZC8ebnQGzYnFf67Li3Mx+n4jJNHzPDnHQli+mV44e7Y28FmJwV2AaESY/2XTzeMQH2zsUJXQYqJJ5AAy5zDQlNDk2Gr5Xh/SAFzMEzFWtcaugKRFGc7quq4Ee1w3ZzGI8pn3TvtUNln2srQ+TGmNAaGmsvUN0BmCrvU4Su6+3ydFmWdPpI85kMzIzaM45samcnTwsS6Zpv9Jka7Fw+VyVUtug4Q9dvHMWS5Y8TpQpGAeiNiNstHGuMMBuVaA/RUuIG1OzbjR1a+bNe2BjHXUFrf0KlNttbp4OwXvM/X2bBFtTvun2EUWafWi7ryree2pVnBvMi95Nw4wMZ/vBZBJz9y51ODKPCBcn5pavoxpwCHOItG4Dgq6dUtSAQucotVFb47JYjExW29PW1ii5cTpZbEUdDAgnpiG2iC291l5dlYb5TOxTNFA0CA1Ba+X5cWYOkeN+IgaH99Huuyo0CZQG37y553RWHi9GN1/yivfCYT+BVnzck6IQOpSl8uR08IvHX7THz90s/hu//QrnPiY4R/A2ag+umJYLoRez6Z7CAfEORbjkyldfPnA+FVoX0jwRoqP1jATP519/g4gyhxHsGxwfvTjw6cd3hOgJzqZ0r9+953TKHA5H9tNEa42iwxp4cO0Fz6sX38OJFS5ePGmOuAC9V/JamI47glWLaC4WUtoix5R49BM5r/zzP/0Jh/3Efj8DZgH9+e9/wWVZeTivJgwfM5Ob3cyUIsf9TBiNynI+4bDmNsbI/ePJqJ8hwKDGxBCIztN6salYCDbh7IaglWL01mm22IkYHTc3O1KK5lrmHSkYLaLkFdVKmndWfIrn2YfPicfIs9sDU3ScHs2QwwTWeUzSbGLZ6VA7Lz54iQi8fffW7MO95V9N0zQyjWBZCujmhOlR9VdevPMBJxYk60SopZDX5Yo0bjTClCK9P7mqoZjDa+9mUsD2/FjjuYnRxeyzY5ywo3W4xPU+KIajwf7Oa9lzAJj9tRsUkevIaztQxu/reGYZkzMnWAi0bm1QwknlxatXHG5u+Cd/9FOKQhdlColyfmRdV1QdPiaO+5mbvcOLcrxd0doJofHRpx/x8Q9u6C9v6XREjYKj7UJvlWU/cVkL7ZOPieI4iiOKEGUgnXTWnmjiKPFAq42EEPxoaOOXIMLy/BUCFGcHVVgndAZ8QjVYaHNq1H/xj/n9//P3+Ht//5/x6598xJs3F96fV+bk+PjjF6R5x/vzG2KeORyPhOiJk0Pbgdor33x9QbHMPpv2/D/3Dxl6NgMVuLJE7XDSjZdlh/S4MN5HYpzw0ZD4OI+9prXr9e3NzLFKMfpVCLCumx24ARal2PqKmMi/lcqzG89/8J/8Z+yPK4f0Lf/5D34PFc9/9cc/JOBJI7ybvpoZgXjSvEf381h7GcpYRPtByXKRzUzCcgxtiuGTOdzhxPI6Vak+ohLHge2Go2anBYcfTS5xoqKIBAOv6gXE0990qJXW87UY3Jz/WhkOlE44tcLnDw8c7gpJMq57XIu4HOCF4+Pf/B6f/uT7yD/+RzgJtLoYHYt6ncJ6L0gVvvzia370pz/mr37vSHs8m/PuZpgwiqy+dXLKQLANCNso6D66633XByjlQoTbZBOP+5X+7owIhGk2yq6qNeeB4S5rlLGmnZJXWq3XzM7erAl0znFqyuvXb7i9e0HQCPWe3hspmb59Y9z3apEhDqHmMsCVSHKeIFasAled9KZZlzFpjCFydGbiVWu7UlK9czA1VKBtzsgNcitIDExp4pASNReLexp0Ps3NCvZLoT4sJBn5db3zB//X7/P67T0XIlWVr7/5GiExJWWadzgsVijGRv1psuw6jI522M/EFMlLtqnDmGT2boPeXJW3DxfOy0IIypzgMM0453n+Ys+z5wfOD0Z3Fefw6Wy1gAj1fB4AipCmjuaMl8AHzz80wzl/IYUDh52nq+OUK5daOC/VGunWjU7oHF/f3xOnGZcbLVc+eDYZaEqySAA1hocTx855lnbBxcCLmyPPpg+oTfnrHz0zWlyurKvpwWs1WUnrxaYluxv7HrQxJaPBBtcGQGzAqPhP8UG4f/9I10AtFfF2bS+XdUxfuhmWDEqlzkfEOdZloaly2O047A/EmGi9UGrmki325lQyrhnQ1hSKCF08WjJBKinOvHt3z7v7nxB3E7VbM2WyG6PP65js7ObJogq0s9/P5tIePT5GvFrTKVKZ92ZCt+RsDvAoXU3T70fMVel13Psmq2lijeFG0T/L0JwWY/SEEMmrTSvrlc7ZCAJOzbmV4NkyeedkJnetVnMKnQxQS7GZeZY4cjZdr4hDtbEsC60pS7iQYqTWQoiJKSXqmAyGjWUywNZSbC/IJV8ppn3QZEOcDMRWxTtHG+wbpV3PKNc70KA1Nmdt78wIR1FjaRnKTmkXfLLhSW+Z3c60pXnsKcEHO7u0Imq5xdtQLQ1NpfMy6p/RCFczBrxSYlGcmOdDqRXtlnk8hcDOB25SQHScp4DIRPAeF+0a9r6dmeaSjwqldAOUuvk9nJYLtXe6d5zPQimNdVFciPz49YWmC+7/Zu9NniTJs/u+z/tt7h4RuVRVV3dP9yw9CwYQAYkEBNB4kCiJoBmNN+mAo0wH3HTWVaaT/gfJZDxJOstEHWmiThQpAw00ggaJmMEAA8zaS22ZGRHu/lueDu/nkdVj0qVPfegwm+mqyszICA/35+9933fxx84eMsprdI0UbRn07vMRbQPTtOPh4czr1yYTG6bAmhV8ZVmV+7mCL4zDV9EZX9bHFx4Wf/Cnv+zOjDAlo53sxokYZ0IwB8Cmwl15MGGwmH4wRpj2Fm/hEyANXRttbQhW0GpHzZp3RrPbbIZbpmRHK3aTF1HWvNBq5XpvOUrUjPNGCXlyk8xFsDW8c+z2EyF5QrAtHoMnjYkxjZznE6WBxJGmwvHhRFkL+5QIo8NkMQ1XGsUF5iVzPi627g+R4BzTzWjIVx+eWy3ks+lnPJbN824zWoL3vm8S2oW6qyZAIAbbCBaMLlCKbUd9jGirtFqI0TH0bRoi3Q3ZsTRDEZMmyIZ0DeVMffWGP//zV6zFEcdgyOFajcrYHQOtdnjwhkJab9Zpl9BpgWq8d3F4Xy6ZhHSUXzbr7os2cPtfP+FC4OJKqXpxIXNd79iaUVClb4QQc5F0IpdYC+1uaJs0a8ukgr55clvukz2v94aeo3bjcr5n8vWtK52qYpQ2MJe87jy5vXDtmXN9APXNtGM+BP7qpz/nL6uyT5HJcgeIITKfzBTg6ubAMDXmtvL6XqEJ/sWJmlfuHu559pefIRr46IOnTNHCuYNzhGC0vp98fOT1g2VpxgBjyARp7ELA4ykVPr6bebMsyHQgOsfV4BmjZxw9x+dHVJUf/Ot/S0yep/sdrTUG3yzDMwzENOBa47Rknn14y/MPnvG9X/8m//rf/BUIPJwW1kXJeUHcEdVs+iwNyKL4UyP6PSq2xVmLcDyebdu9IbR6mccfH28hvoi+db5s28Vt27h9nkYLbZo5nSp0uruT7cbaB8vo2aI2bFPZaKs587bPaWNtE73mmf/hv/9H7MbKBzdnfvFfXlFU+J//ux+wCwPXgwVc146Crud7vvb8Gf/Bf/x7vPvhc0rNSDG9jXTabS2gudB6MyhaaFpNZysJxCFb8PbD0QZC58wYhs1gJlnnWBWcolSjOIkidQWJ9hzFUPzmTc1Yq+l6+tGl9za8ep1Zv+b7IK99SrdrVlwwlgLGLKgbyNLNeLS1yzW4ns8c749ovTZGRy4gkEIgef/4mfcsztYBgLekyliAPZ3L1UE9iulYOm3ZbbpBLY9OSfQmrlqjRDGS8kbdbRuVDdsR1U5PE63MxzszWGl7Wm3Mx2bxAN4APBt0G85HUt/8ONcpoj6aI6OIhRwol62x1UftTWijqlHOYqfTWy/VwZ1eT+u6GHipdt64Vi3GaF2ZTzOCuZOupRAdtJZ5OD3gY2S/PxAOI/U0c39vIdvjNGHsjMBa4HQ+USqEQTifMk4Lh0NimibOa+Hu4ch8XhhT4snTG1Kyjeh6zngf+OYHe0pbeXL9lBAspul4XjidM1SYQiTjKa1w9+aeWho5N9ZaWGul1cZSKj4m8loZnWe3HzhMkfNp5nDl2E17wBr46BNehLWsrGU1cMcHxJk7pfOOCQjek1UZezTE2pTShPO5DznBs6yNX5yONqTHbJTTVTmeF3BGZx1S4HAYcSK8fPUKFUhDZDmfUVV240BtC9OYEAmdBtkYYqQ1x5QcIXmG5FmXiAtCXoxRFYbYgUihFUXbwUCRptTS9bYaaOpo7gbpx9G7hq8rTSsFGMKeitFhtcFuuOb5s6kDosZWaa0yTYPVPPptzUEQYxANg1Gt12XF+0BrmVXMsd3SQz1FK65Vaq5oawQfKNhWtGqjtmpZwWLmMrSN+QGIOczihFqq5R461zd8dn9ttVp/1fWwPUzk0hdsxnJNe3awCGmMlx5iSAZUiHSq974zZ/rPt1L6e++7PuVisLPRXodk0WYpJmN7dZMcYzlV85AohWVtb8V6dbaTWJSL9KiNzTDP9axeq40GXmwRSk6EFEKPPrO6FKLlChpDzPKyN4dVM0IzpoH1OVZXSjWHbDMGjCjaNbnGRnDem5FWa2SbZfsm1Hiy5mWx5SuaHKqp42JM2LemCAYCONNeqyhXhxHnbLFR9xZH1qS7CJeEamMudhy1+cs2dZ4rJzVHAW3K0t26VYEC57KaOVKCkitlqcQhouWtG8RXjy/V4wsPi88OO87rA2uBT+6E0hY+/fQF9w9Hlly4ubnm+Xu3fOuJp1ThzYMyL5njvABdZ9BsCJrGieRNiF4aFBdRL8TBMSWPd8rpnM1NTAutenbTNXI8Eyh4ET55czKzFXeilkCtiSnYULDf7cnLwjKfGXoIK6qEXljQRhhTp1JYQZxPZ5ZlZh8S0xCZUsIHRxw8afD4EBh3k20v9B5Q7n4qXRdgsQy7aSQNkVIrczEdXxwSs5oTlqqShokgZtzy8OoOVLm5umEYR84oIY40X82yPVvo+TgEbp/fMA0DNc9UhClaq3TVi1Vzlp8EyvD0lhImUkpoPuNCxEvEi6FOzjujz1VLtvTiKDnjgiPE0HN1DNVbl5V2nq3AaP8Z1UtRBS4UQmvijUrhqxX9pl0z0L+3dMqFNvuz0ZdtiFxXo0huw/WyrlhR7XoDNVv6zWDCCqFpH2svft519Ky1izZJGrTmLnmLmzHOptXYcqxU5bKh8SGAVmrL9hqah2Z0nCgru93Ab373m1wfEsELzSvHZYbm2MWBNc8s2YKmqcVCfIfI+7fP+Oijb/A7f/dv8u433kXaTDvbMdmMeZ59v1yom6BoyRaGvGZrkMXxXe9ptVFPZ4KAuAoeXApcXZ2gwfe+/ZysRm9yzZNPd4g2pgBTFFQC0/XE4fYDxvQLXPa8efOSp89G/vrn5jr43Am6nmAUkkTyOdMoiBTSMOFc5eYw4tzKm9OJyStzlb7JfuuhmzEPvSHw0B0XzdFW+2JYu8arEUNkvxvM8AEAZ26YzSjP9lx2Y/dA02oOtkpHgi3/b3NcNZ2KBY3H5PjTf/XP2O0H/mqaePHqu+Rc+N/+8R9TRNmPMCXQweIs3kkjf+d3fwt/vKP99IH8cGeaT2caNnvW7mTapGtkTa8bkgc9gTbKuiIizK/fmK4SOx9rafgYmN1CaEJQIcZolN0wmI5MMy4OUIO53VXMAMmZi6E4sfPfmRlTAz75+DPWbzyhygMqRkOvWkjLSwanTGHEqSKu4kT7Ne621g5VG2A+/sXP+OEPf0D7B9+gSiUmh6iD1WJb7AjES611mx0sb7ncttIpa9agbLr2GsTMYSrgrVHKYtdvxPSPTSBOnRlg1oFGKRXbdhjPTC+/s9aZ3bMbEEcaRlIcaNlon7jH19Vq7SkojnKaaWULRTdKclN7xS4F00Ut2czUunuy84p3dOMNe5tb+xO7M+pGwfdxIsUJVaOE5VI4TAd0L6Rrq6s1r6RWOd6/wfuBm/1IrY1zyQwxMHhHW2ZKFYY0cX0z8MMf/5h1hevbK3a7wHpS3n8vcporp+ORnFcO+8HcSw8HvBN2Y+Bw2NlZ+8x3QKai7YpPX35GxTGfV5wKyQdO+cxaC0+uD9wMe25uJmotpBDw3vHOs6fEGExvOQ08PJw4n84IwqvXrzmeA7gDS5lx2pjSjnyGT4/3toFXx/F4xEu0prgfo2WdqaWivnA7TlwfJsYx8ObNkZv9U6bDwDBNHVAsRgv10nXuSgqBeV148+qeaRw536+sa0adcrje8Zc/+ZTzcSbnxm/8+rdY84y4xajqq7kdx2T+Asv5SEyR3W6HF2F/CByuJrQJx7uFXFZ2u70BL90gS1sl19KHrgDiiTJzPj8wpoCPiblBaRZFJlGQap+HIpS6kKIj9PgNNw3kllGp5lugnVasyipWE0/Hl4iCd4HMYudkiCCO+36vSQEzzxkGFGGtBZdXRGFMCXG+D5I9R/YtBkgrkLX0ei6X+ptGPr9EAAAgAElEQVTzihlPCc4bEFVKNhppN9GJKQK29dukIuYybm6827WzgVTibWDzztzIa80oEL3lTgsCrfahyuiwYxJUfb9HNKI31g0aKLXY4CRmDOjEQTRZTc6ZzQPYfKis/q2lYLFdmfxwMif33kd5Z0yvoP6yQUxDpLTCsi4XN/ucF4zeb87r0pkmznlGscHPXHwbotkGf8DufNaneHH4ZFmL61rsvQ62edWmFq0k9l5QLDdWer/TavdRc2jPEzcAq/YzzQbYc6m0tpBzxfvuj9HlHb73HIfo+pbUWC9xuO6simJ9inN4v8c4Gwb1PZwWTuczSw42bAf45Yt7cn7DV48v5+MLD4u3z/ekszloXjdPrpZvt3/YcTobB/00Z+7O1nSncWTcDTxxe6DhpPZCAmMa8cHhsaFpLoXSGvO84qh4iaw6o8vCp6+OrAV8nBk9PL0amMZEihYrkHyiiaM5SN6oioNrpMEzRaOCem+xDHGQC0fbqJXG8S61MrqRugu2LXWeXYq2jQoCWokpkWJvELr7aRi6XgrjoocgZnJCoLVoRS56Cnpx4QzRhOkqME5m1hJHh09KyI0gRuHwNLbsHZoFzmZdKGumAoHE5islzuHVnFwlONZT4W6+M9oo2yC0beKUUi0YXTdjDOmU1GbUzkcKp3JZRiC9ue0l+SJMNsm4bfv6lqh/xfSEj46liBXODV28/O72qBUAOl2im3+8/Rz9Z7g8/zZQ2et4e3tkL78PKN1cZHNfe/uhv/Ln7f1q3cK3O3rbN12qSmnKORd+9slrPn0lXO0Si2ay2rl7iAXVwlKyDeEIks0EadiPvL6buX/xmie3E14ydek6TxF728tsuoLQdX4bza7HvIBAqAa4BHutfouN0Nibcce4OzC0CvOCC57AjtoSKQ24OKCaicHTfGNpM+flnt00sksjzs84cYwp4hPM1QY17wpiEwxDcDT14Bsxmmtr6cd307/96kPe+h/y+Q9A3lqMoZBSYDcmarVtIA5ya1Btm7NFnKh2EySF5vQRCdbW2Z/dBU638x1yE55f73jn6cjVYeQvBs8QHX/r++9TRRmiNRjHOkODpz7w3ju3jIcD6ithOiAh2muuRlmuTXDNTDBsTdZNUaIZWamI6VNVCUO0BrdTofAWuZNI5n6r9KB5zP34cnICMSIx2QCtj8BJ98XC9+8VUdp6RusTazIERDwNDyXz3rMnvP/+u2bI0K9B0/l2IEi36045PRy5e/UGmiCtwmrUJRNKFaAZy0A8iNpmUbf/bRf25crs/y50u0OrP1XN8EigiIWLN4RQ7dyuG41cN8OtbsqwsRzoJj8Cqh5HYskV50y3uzEkTFu9XfMdSGhC85EmnuYczXuk2XmEQuwWlttwuT2BOfT23725PG/v0dBM+0BE8L1BFnH44JEhdNMxo7NqU5oD1YrooT+r3QnGlpmSuUj66E1njiMNntfXB0p1pL01zOucefn6RKmV08NCazMNYUiRtVSCd7x6M3P3sHZmRyAmYRgC0pTg7dwcp5HoHSkEVB1zLuSm1PPCeV5Y1pXdOHZq75vOQBbG3URZFmpdLQMwem7TAfE7cgbRRvIDXDn2J6M6Bh9YFjOaytUa21obx/NsAfVNEQx4cYNn2I34BMdl5pOf3ONFuL0acIvj5evc5TKBFCI409jlrJyXlWVdGcbI3f3CmitxSOz2Hq2F4Iyl1JowO6VWM3GpdQFvDfJ5WQiSUCkoxiKYjzNNSzeXEXJeOtvGzm0nWGxUq7hkJmsqPe+wYSZV64qjsyA6GBajXDY0iqOosXEajtDZ7149WuqF3dNNsyllZckLTe18od8XVBvUQoixa3K3TblRbKPadt0uU9P40TdjiBAHo2maZMOkD65vB9c1A1CbDTvnOWNG845aC3Je8Z22baZUDu8tX7h26npXmtv11CNFNmOW2kGo1jWPbhuqyqZ/0z6ivC1HeewZuj/0pfcI0fo0kRXvU39+uFio9dbENMbN/CW6GZjh4NafbUZquVRYLF6l1UqIrvcv3RhQtrLYLr9LpHUzPrvxiXNsR6H1z6Z/5dEkSLa7qKdp7YNb3xz3Xo7W5Tpa7TPHPrMNUgPLAmdzaAYDy9iMK821lQ5c16r9XNAOLoHgzHyn52xC607yxT43K8wEp+zGwJiGToOPpOippfKDv37JV48v3+OLbxbfO3BdE97bBWYV0GIiWm3M88yaK8kHasukwRCWaRyxU8roj0YJtOeUwQq05kLNjfu7zFoUwXN888CbN0faDz7l55+85OH1meHqwO1wxfUu8u77t4zDQHKNMBgnOqWJmrOh5N62ei7YJsI5o6vYskYpPb+KaiYNZtkNh+uDYUvedYfz1hEiE/Q6wQKEO9VSxNwXcabTE+/wPTtm06+44LuxjBpC48wZdX+wC3vcTyDCrsXO0EpW8kQuW7zoBWjmvtYriHZapapRuRyWe/Qnf/Ij/sUf/Tk/+vELPJHqVrrWvG/0tlB7c7cS1xvL1gzFpZvMsL1HK5y1F07f+fNgBWor2K1yOSZbPwh9+NK+xdN+TJy7IHsNo3Bctn5Nqapmfd3RNrbPBHsPrjsu2g2hPZrWXIZH6SihvZbW+qDZzJp8G4Afy2cfYrYf7++1yeP79EHIpZBS4vqw451ne+b5xDR69GzOhyk6iyXJA3FwHN69ZRpGag9AfvLslt1uR14r+c2Z5mZQM4mpWIPUSqEJOOLlM0GcOQNiw7TLNmAkDwUhq0er5Ynm0hCnLEuxDVVtED3jzY6yZHN4jImCNag6n6j5gSz3pCExDnvefepY1sa5VsbJMb9qnN3Css7ghJAcvp45H1f214EUlOgc56L9HHgcEsQWpr3R387D7eudfthPmmbALoJwcxh57/k1enqNAw77nVEOiwUXB2+NnA3USllrD3Y2LaQTTxrC5fxYl7U3Wonz6YHf//u/x699+wlPrxx/+mzBieO/+M9+B+88SaKFhJfFtsyr8v7Xv8bu+gpJgtdrGwQAOgWSko1+NVtkS6uKa94aSy+Id5dzPMQeNVC7uUGwGjL0/DCcs2vfrjhcq0hWLORuQOJkDRxmSe+w+tINQ3GqeJT1dE+pHokT0iq+eggDcpr59V/7Pq9PMI475uP9pW6JNnO0FqvFAA8PJ16/urOYBYG2rDYP0XDyaJ4gtO5A81gA/t7Xf2bXL/2f4WLKs7kk+m2g7zUntNJps9j761E+9g/9sG/xJpdC02ljHWBq1WiCjsIYrU1sWsztWFs3yNmaQkVdpIoy54KWSgxWi0SV4ozR4tUibkoxSuxG7qr0euy622YzGnvVhnamRMxdJygYndV7lpIfi6baXOnUkcYBVFgfTjiUq8Hz89b47GHmk1cLXhzBN/wpMA47nAQKlVxWgqucj45SlHltrGvmeH6FA9Jo24LjqXA6L7YN10Zwwu2VOcdO4568rEzTFjHgiTGZbqx090wsH6/VyjSOpNhpy2oB7sH5HsZu9XUYR2J4TQiwi57Fz+x2e26uDgaeROGdpyPH85GqyloqFgt1TVHYyQAUqmayNtx7nt2ozHPlxWcZL453no1UhM9OlcFDCuClwQLvvfeUVoQXb04U51juV07nOw77Pft95Opq4OoqkuJEdDagrbWxVMiL0X6dd+Y3ECOt0PWPdgaNoyOGHbUVWmmUbBswcXYPqwLaNa3HIsQUuZtn03MNA9KU1w/3ZkyFJ0Zv4NEYTVe6ZEp9YM1GT61NCKPrAKjFVtXV7pHn+WxzYTQ/AC8mmwkOUjDq4DEvaCnkJaPVPrPNMMqyio3tE7csSOcIPW6hdQq5iCNFi+dIySiKBRs8olNopVN6bVCKIZFLYRgSoQaW1SLJjHVvLIsNMNS+dWytm8rUTWO7eRRsvYcBibUZ1XfzMXBipkKtVpzf3GgLm2Ggk80kzSJGcjemiX0hIE4opRKDMAShNLHrzPU4Du1xKs3uM7na8Fqbkjd3ePHdd8fjXOggwowP1tsYdbcDUO6xZ9r6nbcfmwGN1aqGug2MB9RdBmwDGDxF6O60mJM7Sl6r5dVujKVe/2wg75teFQT7TDego/a+w296UPuUO0inPULDarOqgwq5mvxjAxGkNKLzSLREAucCT/d2d/tqWPxyPr7wsOgEhgFDrr01++NosQQpRHbDFd7ZoDPnM6f11OkLFSGwnB5tyXtJQp2iUhlcYhw8417M3a/B4Tpxdb3jXh1hUF6/nHl6c827z/ZMyfHesxt2ux2lzYz7wLRPjNOhu5EuNCoSTZey5TpJ6eYBrZI1I60S40iIAWQ1m2YaBI/6zrN3Rnkw7ZuSUmAcBnPd7M3iOI0455jnE7UVnLOCW0vewLyeSwjB+e6e6oza6ExvJc4Tgg01W/5itanNim1H6YMEpHlKyZYRiCHRrVXEeSQqP/3lZ/zxv/kRr+7OPDns0IB1NJctm6GLdhOQS/GDjgR1VM+9tfq5bLQu20D7bvowqe3yBB09s8FMtu2PPGYZWg/YBeftV+mKHbHcsLW3J07oOsLHIRX4nO7Rnl9/9cc6NcZdJl7tg4od1u2b39pUeg+5ov0z1BRR5ygKt+PAN99/h7/7u7/BebljHweOdw5JBZ8a80Ph+KoRxsbzrx2YdjtyKRyXE0+e3XBz85QPv/UBu5sd5PvuJNopzU0Zx8H0lN50M2XOxpyrC9BpwE0ZschTj3AUIVclz2f+qx++h3eNtSxUNcMhdX2r3ipCtZDmEC278dwYY+Dm9orBnxnHyIfvJz57eeJhLYQwgD9TtFG9GajEEKl1YcmFK8wgYkgecj/Jtg91O6ZvnR66QZtKPyE3EOetcwoYhsB+SpyOlSTK9WEChVozvoM18zpbU96EU5uJQyDnTHDCOCTGcSTnxSidNHKutE7jfO/r7/H1733AO4fAtPsxKLz3tSu880yMeBpucKRhYs1CGhKoNRUVoyYrHhdNA4xf0VLsfWzGKQJaHai5mdp2ky0U7KIFcsFozs3Z1pbNqQ6xIag50C3bzNyaN4pRvypNw7ht4Pt5vK6Z1oQqYllluWfF1cJuDNzcToy7A8eHB5IzHZWxFR7rvr1XbDvRbfVbB17oTdHWyDnnOsvz8ar+w9/8s146jHolanpi08P0eIC+wW/9mEQgghVPbPhlYy30Fyeh2ib3sg2gO+06yyeUSgiWs+uDvV6PoME2p62YccXF2qpZnEYt2Y6vs+ZV2EyiHdFHQGm51xhnoGItxZqqrl80x9M+kPZhcWgGCuVWUQfNCYM3PerajcJiE4I6VGa8BB7e3CFamYbIX//0BX/6w5/zFz9+baYmO2EaRtKgeEndidJCx59cHbg7z5xLYAqe+zdnHu7vuX5yAHHcPSyc5wLekVuhritv7jyH2x05z9Q1M/himjR1DOPIMh+ZJtjvR25vnhBS4uH+Dl0zzifEOfKSWZezORfLYBTa/AB4QrRr8mYfiUHYj5n9/tocyjFn0zyfALhbZsQn0rCnVWU4OG73A0EGzmsm7Qae3CopjPDRFdIUiTPFOT5oe7Sc0bJwfrjH3zd2T29Y54aEyDjPvPzkNY5KDGZEknPh6sqYSOYlUEgCkwrnEAl+JOeVYRg4HPasy0LJSoyxMxwiIXge7h8oAkmDnWMIuZuc2N+U85rJuXGaj6TkGb1RANfaOM2K08rUHAMga8P5wJwX5tnAOsGRc0VPpsOvxXRuWg2QWpYZ54XD9URIkRAjV0MyV1ZnXZkfHbUJZyeU1Rr+Jc+suTCfVnOTR9jtJqtjDkKyOn46902vM+2cONgddjhn9P6QHLsUCKrs91ed/ggxJVprpDhSW8OfTuRSLM6k9uunDyCtWixZpZl8RIVWLHc6BHcBs504SlkvwLZDTJNM11OWRpJuhtddm61GSHc0tWziVhsi1dyB+wBeSrNjZko8xp0tPmqrOHEsYqZbKUXmUjoqKuRgQvbaNd92G3TQDETyEvDB4zuQ0Lx9j1b7HFbNG1aNowOp0nuzbbDu71VaM48QbLFg/abR6Ln0Vu4y3NbaFyc9Csvyqf3FAEgJ0AwE9L7/3m6Ok2KilYwFvAgqtoX0fess8mg+17qzO918sspCKUptR5wLhJAoebnU8q8eX77HFx4W11LQbML1UhecU+7vVuZlRhvEYY93kXGo1jiOqRuEGfqindJTirIuJi4OYaSsGc3ZnETXTK7wsNDNFxwyjHzj29/k6bsLIST8NOKD4FyltQfUwd3dmRefZcrZBq3bdw6Mu4gfPflsmjPvE2HanO2UfbymW/n1iWJkzdn6GW/ukwDVCzH3JsVHcMKyzOAdErGIg/mhO21K17IIMSSCH62wuJ7JI0JuzVAoOpWzCfMp03TFq3He0bXbOvcFbtcliSoZRb0whETJnW/ujDPfslLENk2twtV+wasj2yGw3B+UIgmabYRzy0aL3Sinvei0ahSRGIy62VQ7UmQm2V23broANcG7RRNYNIptDttlKGvV/hzi9hlwoZHZ0OfQ1imnfRjcMpBMfL5RIazgO2daGe1Wz7ZR6IYCddNE2GcozmjHMUQI2h31LMPpkulIN03pYEappuEK0QY38ZVGRr1nXgOrDrz30beIqTL4E/kEYxwQ77h7WFlPSvQVdSutCVdxoDbl4XTPtfdcD5XBzdSmuJbYqNqDd7ZtakBZoDXs3iNc76YLdYWEbX+qEkW4bSbIR254r2bz4Xhqw3bNDZqH84mwCzRxVArhtCDq8E+v+L3f+U2+99HX+cFv/FuGSTm8f8Wf/D8veX10/Pu//S3uf/QTXj9kFmfuxTuf+OmLTziujjF6lnlmrT/k0z//DM/btOHt/zrlZhsI+y3HrM83AEF7450JKRCkoPmOaTB9cV7OBB8YUrhQgiV7zueF43nu2U+2dXTO9wy5yprNFGIcI+MYePX6TK0DH79I/Jbf8dE3Dux3v6Dh+PB7v0YUIXhPccY6aCjXzSGTaQhZSt+wdXOUamh3FUUGT9hdYTdKj+/65A02/h//4F8C4OUZtRhF0nX0VsRCvQWj/1S2a8FMmnARnCMchV0y0wYzm3Ab+9OUJxtaL/DwxgAsDQ2VhriMd4mSwddf8M33A3//H/w+//h/+V/xrXbzv+6c3DanYneh0dauBXYScL5dBlfnBoK34Vad78Y79rZbH5K3v4OzAG6E4ALqoLRmOisFRNCYDCAr1Ya3DvZslvqq27ahWRSPGIXOeQ8U8nxivrvHDxFppo+1w2qjtSfge/TO1oA5ZyHaV+FgyH/ujYwaJRZt+OA6fTh0m6A+uPbPd6PHolg8jvZ60893p6bVrtUayYg1sgGjpbXSyCqWY5qVNV6Dh3WA0+ljRjzv3EwUJ3z28hV3P/sYrY5pCsynxloqLnp8/KUZtIVAGgY+fO9rfP2j73IYE0MQxmR7uzR49vsRxFxbFeX4kHHBM7fKUht3s4XLtwpU07vXWlERbt+9IQCuFRqNsNsz7RxFM2MyE7yrlGhNGQ6wLrbZHVLk6c2EuBXUZButCau7odbGbbqhVDN9iaPnVDPL3UL0gf3hwFIcxzeFJSzmkixQTydahv2h2SZtHJBx4JvP30G7S+f3nViNOR25v5/55MVr25qFyM3hmiFF5tMRGFA8VZXkS9+KzdSWWc533dQF1mW2bZXzPBzNCXs37Tnpwrr02CEXKVUpZQVxhJg4n06E6QoRz2evFhxw++Qp3/noGhFFa7ba3zdQ8/XerjPxHYCqFzfgh9MDuRQzMqExju8QQ7jQ77VkljVzPhfWtTGNOwYGRCpxFMrYaAyUeUephfHDxH4YWXWhlIxXyyEtFaLzEBpVhdOc+dkvX/Lm9QPx/mhApDZS8KRk0Rzrmo3q6RxrNoOW07ISQ2KYJlJ0XE+2IS3F5D4xxs40apZR2cwp3aeI9Ptf6zeNUgt+iJfab7ETZrJSG0xD7PRIxzSEixZy22RC17cGM1Jc5k6fbGIMgBnoFFh/WljySggB71wHRGBZCzjbzNVmQ6v3kLp2UjdAS4RhPFCb6edbd513/T4hwdGibeFLrZe+B6y2KTZM2i3UwK2ylot54pLNYG9pWwZk38z2BUEIk/VRmzu1h32047bhtrWWbnhjtGZtXRIhjVzPVMRuLM3qlXdi+tUG2utjrY2QgoF/WoFqMivncfLEBs1mLuEXJtdXjy/d4wsPi1Ubx/sTd28WXn4ym46omIDYi5BC65s4Q8KGcepocsV55ek7B0IwN9X9PuB9YAyRsgpFEk1hXMyyfTyeqc5s2Zd7E4hPYjd21/NqcB6JDpeUYfAMbWQJti6PY0SCo5aVvGamcUJaQYvRuwQouYcU9w1Ww2ze67YB86G//I7uY02P1m6sgNCWbiMuDkrrw5LRoCrNQlR9wGnoxakj/o7L792C4Guz4PPW3WBswOmuqWLGMqjlI0EjV72gMtbjNTZziePdzMsXRxojTQLqjerWilp/64GedejkMaB+G962h+tUOLZcIDWto5nKcOHBI5ZbdNn+6SO1c9OKbc+70Uc2G2cnj26GmwGNIfRGfTFqQ+v/fVwXbk6qG2+lbM5fG6VLhNA3wtqHw6o9w1L43GvaNllgRVJtvduLsgmqrMe1TKOgK21dGaSyC4HkIvHgkW4tPj4ZGd4foGRyaajzhMFWZ7dlZJoOOBxmBCloFLtxidDEqIuuu4o2aRDM5ZZWjY6nwIJtocRs/0UrFBPvmz6ko4xi2XPGn+432RSsqY49JDwmHk5nfvnzX/J//NFf0KTx/HDLJ6/uOXrlvJyIxzte3BVOa2U/JG52E0s58/qcuZkGUhCudoPpTFp3v+1HWDYgYltEvXWHcM5AAlsciZmsiG3gRYzOleJICJ5hN1FK5eX9Pa00Yoo8HBfWnHFuJKWB169fGgBQCjEGrm9u8MGRuzV4rZUXd0de3s9UXlDzFe1N4T9f96h45M0rcIJ27VGqK4oShr0Nh+OA9s3/du4gRs9ypXYP/E7LEWfUizX3SJ/WN39i/65qoI1IHxBt2+QKSDM0u4niMVOWDSgIzvH05roDMtUmrGbI9KMfgqJimzDnHFotI1VDQtxok1w+s4t7vvnRt7h5csNnP/85wbn+Wiyz8RGttlOI4HEp4WUwpwsqHjNNoOeuNhNA08ThgL98cwUK3765s+ewt20nYy0GxjjsmlPTdxsf09kdq/SmzgoItqERe9utIT1wXpwdVydCEs9UPSoO7weci1yy0fpAavlvipZq29BtjVgqTUwHfjlPu8mSFgPonPHAPr8m122B6h//LoITayqlGm0z167riYGqoLY2sNrsFHWu31uVtXbNZPDgI1kCpyzglMk7WtrxcFxYzkpuUJqyPpxY28yyNO5moYrj//7RpwTvSM6Rkm3sQzAGyW7a901Hw9PYBUeMgdvbkWk3ECQxH1eWlrkeA9MYQR1LyVxdX3EYBrul1ELtUQ5VbfMk4lnmxUyAZk9rzobAuVD1iOApuTDnDD6g0q+DbqDiMKMoXD+3KXD/QC6V6MzRdy1HRApDytQi7AalFaGtcD4bZe44Z9ay2EDrHIMEcs3My8m2vinwG98Tbm/21GXBY/END6cTLoDzidZzd1tTWnGEKKRkur8UHFPaAEyhZbDwd9OArqWw1r7hWQo6JIrCvKz84pOXzOtC+vgzdvuBpSh3xwVKYz8kxmmgdc39zfWe/TTgtXB12BPDyPWNxUkJNiwNcUBEWNdCzis1CIfDNeKEeT6RkvVb87JyujPg5bAbkdRQGcx13QVzvi2V0TkqFu3gnaeupk+uTfnasxu7nLyn5Mq6LIiYHIYOZC9rZs2Z85xpCud5pdRGCBEfAtIqy7Iwz5U1L4isl/NoGGJnaJsefplXc49HLkZ4x9MZ54WUDGCSVhHviDESvO/aug4E917gYpnWGVQhBssWVXDNiJZaDUT2nbWUW8WFxJorrWZaW9lyKp2zoS34SGvK2tbuwN6ZH33wq+0xRxIxmnvJhRBCB5f68lN6d1jtvoHvvgbBd/Ae1LkOcAnSPC6Ffu9/1DBqa51t15jnctkgClt27Oe18w27f3scThrqTB+LCinaAFjK1gfb8QxYHJR2/XMLylpy79EMkKeKgRPRMtq36LMtVumrx5fv8YWHRWtmlRDswgJhHCIiSvAQ3bYGty2Ti53KGBzewzgGfDC3vuBtAzd5T01QxExipsnsp6fJ27ClyqmZZX4cB1xwxCCk4Bh2Dh8dPoGow6mz/gu1bDDoGyh3WW+YK50VsLZdlV34rUiHwu1vG7XJgmGtGaT1QuO5IDtyaRjoy4NmAwD96emZhm8dSXoj7ejMhc1+WTYufn8V25/789oTtv5762Nz1bcRzluTkZeVXPtUuF2MfeAxKsNGEzQUjO139JFJ4KL9QfWiA9C3Xrd9o/1g//HH99dR9u3PbaOFqm3+Nnrdrw6o23ayth7PIZsi6PHXaeNzm8RtALfntX/Y9HB6eY39M64bwtff5XYM6EYkYpqU1p34RK3pvnx6DYK3zMur3QjzYhtHV22QXAu5NdIumsNsW6nZNJKtu/8aGpjJa740q61lQ5M7oOBQxOBNnPRGCcwdt4vOBTs+6guPa1477v/o3Z/RauMPP/vQNjLO6IkeuUxsTQzNbTRcTGgcqWHitGQKUO5fc84Lr6ncnwrfee4sG6kIxVWW1WjkgjIvK0ESV/uRFB1lqW+dH/0zupxdl9Pxc5/rplXZ9jQhBIbQM7eakrDgdJ88jgNaC3EYmEZzi4wxMQwjV9cjTWsfID2H/UTwZnxi2Gwl7SJPnioffviMwzSgDf6TF1eIC+DMKIemhuIj9sFrPxVVLzpWwIZ3HLX/v3S2gKI0KaAOp7XXo+155XKZ9BP6cfAQ33+fUbEv15pK1/I2hhh4791nBqwoZgrTa9B2XPupTYrJKFudht5o+AYinracSSHy4YfP+eDDb/LxT356CaHfrkqP75tDwXt5pEGsWjAAACAASURBVJhKe/xF0v9+AXPs3zs5l//m//xdUPif/uE/7V9/6/scl+uxcxHYYAbZpi/fdcAXamvXwfSmSoMBbtq/F+dweNKoNHGEFC/Amj33ZsTVQcCOFrnPfSbtcjbar3z7s9DL56Xby9etCm4qT7mcMm/XS3GC602XOOnzqVFzm6u46mheCH6iBqG57nIbHe998A7NJ67enVEadTkxr42H80qtsJRGLpYpfFweWGblfjYa8XY/FCzKaUwe5/qmOIzgvRm2tGI0XRcMPBFPcrbl9zFwtYsWL+U9a/Y8f37NLiUcxsZoapELpTesTYX7Hk5eiuDVXRwiC3beZ4S1bya206tmA/9CCJ0D3CyLU6HOhVyraQe7uYffcvLUGQZWDLQQHxE16iIiNEpnvgQiAUYzV4nB4UNCXMSF7ZqpSIy2tXee4GIHT0GDsw82RtOPRXut25YnDAMumLGOjwFfAr6/fu9WXAqUrATXLA95DTZgidV85xzqoeE4L5nzuqIKx7kypojWlWm34F3EB3MQd1iWZPLHXmOUmjNZlcO+kqKntUzsqTTzvBjDwPle4qoNTD37NQDqlKxKrhXNineNtla2SCm8Ad4hOLQaq8yJObei3XdgczMf3aUPqa0SXMR5x7oaY+mwt3xIRFjOJ9suioHmTaTTxu26ucheeh3Z7vUGFFZjaIijVe0GM4/fC9JvmXbBhxBQOnvjUiFMdkSvfVutM9250UmNlNb7KVvI0TBDulrtv74DMtR6cXTfapE5zbbOlqrW02Ff3/qa3nSxsa30rbfN1r52jZG7AGCCZzPicY/XeavUKh0oM+rt5o5aisWjPA62cjl2LvjH+992H3d8/rX046+9n5aup9/8B7R1RtrlhT+W0q8eX87HFx4Wj28qw+jZvzvwzjPLSgkh4aQ3//0M8q4PUF3L4YJ/y9rdLpKSS7dl7v1Cz1ITteyu3X6wrVFTds+Eu6DsDxPilZSEOESG0VseYdlawEIaQhc1F+PvO0903igj5m8OGB3TRRM9b5bnIXj8dgU4yHTb6Kak6LZlFKiyFrUiFkyrk5vdju399UGpD1u5Uwr9Bqljg1hTvehxGnYM3VsxEoqSc70Yt2jfKOVsZhpNCkKhdbOgVqC0iAeevbvnO7/+Lv/Xv/gr4g4027pB+naJUi8XtYoh8lp78exDl78gPjZQbGOM2xpb+tvZAuu7Qtv48jYA24+43hx2XZHJ8nBButmFDcQ+dAto1UdUrfUGkF4kpTd17vF1CGZvH3ueY9kst8XMaEQMEQvem+Ab+rDdC3YfwJ0zQ4Hoow3mfUtXsSErqA2yyTvef/4OH3ztHQ6TI+pKK0IJsG7C/9I4vTrS1JxTIcNqG1NfK36orD7C1cgwDWhe7fLx5vwGK1UfBzvXTDtVa9kuGHANtBCyARK2mHQ4HP90/wKAP3zzoRlptAatUE8zpAECaMk2YE+J433hXBJ1eMLf/BtfJ3rPp6/foLrjRz+550c/u+e3P/qAh+UeXTzeQWkLD6cza2ncLY1lVxlD4Ol+5OenTOhb3k1RFyhG0+yDbumNdmsNj+vXjm1IQ/B8+P5T/r3vfwMo/OyzV7Qys/NXPLk+8PRmAszaO0SgKcelslYlXX9IGHzXxoEvhVZWwiWqQ5Ew0ELg7/1Hf4fbqeIl4yv4bQDQ2sX7oQ/anRbsHynZrim+3+lqv7ZFm63M1NnkIV3bI9YAqcB//c9+G0H4b//DP+5RFfrWjbhfIFT7efUIlaLSLfIN8T7sIt/9/ndoKaBzJrZAbo0mlSZKEPBqbInpdm+IdwXVCnVBC9a8rzOuKr/1/ef8wR/8p/zLf/7P7VJ1egGnHDZ874aJ290B1kxbVjP1UkFb7rUBwLiwpp3rqPgFOLLxaWvynAhmBtP6YNmPRQfpfBM7Dv3QPEJGj8PXxYk0usfF01vflKZkg2Yf+B2AeNtqXp4dCBtE0b/QHRe117nNHOzx0VGr7XVs76d/T90Mj/pr3ShzrYOXY3L9sHQwUAS7KB811GFjmfRBVoH3nz/hb/277uKeubTWpQjW7J3mbFRR58m5kUu1jVB3BhU2l0trhjcdudHibNhDK1oXvPOs2aKLXK7UOTME1xkxarr71nh2e9VN0baYBXpebsVHuyfmUmkIS7bRuXbAUx19y6K0udLWQq7mDHl6OFukhjftl8uNJRtTY6lKw+NTYl0WhsGTYiQEo91dDRPbYfXe5BtjTIh3ZGkUrSyloFW5q5UpBXYx8fXrK4RKawmcUJrjttrA5YNHTHZmWrTeuGsn1MRkjboHA7ai0Q+RTuFWLoyZemMvrpRCcI5/J3zItvla18Xuyd7OntyU87xwd7R7wmkpnNfMmgunU+NUe7wyzqjgtRFrIzoheg/BM88Ln3x6ById+F7I6wz9uAQnfHx3R+laXaNZgu9047vjAw0lRds6jkk4zysWK9E4nU7c3BwQKsMY8OK6yZb1Brk7tUqPm7k/PiCIbcmQnt2s3N5cMUTr4U5BLc6iNqPe9yiOlIYOGhug7JzDh/3jZWuUrI5jm4NnXnPf6DWj5/rNzMc+uzVX0JNFlwh962eGda0ZmIuYRKDlwhCiaZfHLqERh7ZsV8aGHvnOngoJRC3yrGTEWS9i22f7PXEYqSVT+ntSjOpPB7AFaMWKy7qsl/eNvcVeq7nU1c2E5wLCiUWvTOPAxUSsA/OqVgNid20FyzVFhLW4Lo+w/y5nAyxCDN2wsSISbNjt28nS2SXOmXOqF4/4gEuPwP9W4/7/HNO/enw5Hl94WHzz6cLh1jHtFG0mgqZ0a2qBorbJ2kxXnPeGfmrrRcEcs5xzFgNRV6I3Ye0wjAhwf3dHUwguob3QFC0s2YwapCr7YWBXPDUbMuNFKGSyLt1BLJjtb0dzcs6oE3zzm2UdzTUS/c/ZCnhygZRGlvlMbg0XHRI80uhOV3S1cR94xNyhbDNUu7mDXoLl7WHGEK5tCFBv/vtgtVHvfKdxFl27A9hWcTYkydCizSEMB3Mu+AChO7RKnm2InQ68/433+d53PuWf/JM/I6au18Ec6qRTSuxhxUJwRlurjxfuVkS2orRt3z5vIqOXr4ENceJg0xuaa6kNMb4zs2C7aTa8dyQfO4VUmZdzR5NtWL68StFOWaM77EHrW5xtcSjeGn1xZpghYnozkQ4EBE9d82VQdx1ZrLVS1SiwVSv3ZwubnuLUF0kF1KMkmoP5WLh7caZ+vfLht5+y3J0JkhAP2jIOJbqBnM10J06GFpdTJbdGwFjAK5VyOjMddrTNotp7nChrviNKpLnYoxYcrjT8LoGaE22JM0KBOdJQvFjXUvvXnQT8dIBB4HTC18ZyOhmaK54mFQ2OMFzx4ke/5JOf/ZKPf/kJ+zQxJYff2ab+eGy8/OyM1MQyZ/76F/cEr+xS47NXM7lm4m7itkykAgcfsFAc26huKKMFe/ZzGcyQop/bKq27xTlaUaIXvvbegb/xa+8yH0+oK7x48ZKbcc/1OBAls5Zsjc+8sq6Fu/uVeS3kT16yu54YR08Q4SpEBu8IMXLOynmpvMkLi2RCakhyeDfwv4+fQC38/sM7iCQUM42iBXAJ7zJQyVVpEohNDKERBbGAa1oyNF8sLsXqQ6OuYpoW1/jxm70NCrUgztu2t9EHTE/VdUOlTEeHRelsMLLD6Dy+U8mQbj3fjOK14XYO+7NLFuhNq3htiNoGCembv7LwfNf423/7N3CuXUxgBKEK9vq1McbEYRxpbBTbvgG9uN56zJKmAflxC/tWL1A3O2XeNuPpDI7tm7YfENis77fB62368taV+e3vskWGdLp+B5Zq7XrHbnphBXljm9jDfW7rwOf+8rjN/P9+XL7+9vf19+0+951qBke9qbJv08/VVLB7iijouuC3CtiLXJ0zORdOiw1x0XnaeUa051rmbEu4ELiaRjKFu7JSPExxxxgSo/Od3pdsG+VtsLFCCS4a0NGcATqC4HODtUAuLMeFsjZqNp2S6AaWmD6uVHMjn65Gdrc7q+fJstUM2bHPyDIuG261exul0IqJ6zVX3tw/kGtjXStrLbQKp/PKXHLXUCpzNUZOXgvURgnCeZ05n3MPi8+c7h9wAkmCmb+oRXPMKrTSuF9X3n1yzfvPbvnaGAmuYsQpZ/KRJlQVO7Nroaq5DPthy+y10yklZ6B5VVLw3FfLEPYxIi50Lb1DVYxlEhxr3pp+o3SWovj9zuitPdEArzzzVwz7kXEckMEorSJCXaA1oYVgevXa8KqMzlxAH84nlrxyOq384uNXHM8LD6eZXBqfvTIAbJ4LJS/kpVGzcF5mzvMJQYnV7qnn05Hrmz3f+sY1u2Fg3EXELeTaaEtmGJTkI/tpT4pCa5YBGIPda89zY83ZsgZVqLlQ1kprBiZYRFjldDzh+3Z2WTKqjrnU3nt5cL4PcJXojYnkveN8PCPYhjJ4x2GXugTF+rJ1zX1rrThnoPG6Zpx3jNNALRlthXMpRhnv/ZaoI+eVJqad9EApGYrig8MFh0Px3uQkqLOBNkZyXazd7Pc4Hz0+qHlxYCyJ2vut2IEF8eFCHRW5VETrtTqIVko29of6R3LKVsf64CrizHCrM99839Bum0ptSmmlHw938W1ALOkg9h7WeXPOXVbT/MdkA/swuLeWCRYXkkvpm0vTpS7LYhh1n1l3uz0into2uZC9zq+GxS/v4wsPi3/0r/6MpzfPGFJgNwkxwdXtSDkeTSBcDRU+L32Y6PbGOReqVoIzq8+mhrZb9qFHtSHeImj/X/be5Ve2/Lrv+6zfY+9dVed17+1m8ylKIimbkmOIcgzLhhTYDjxJRv4DMgiSv8MIDMSjzILM4gAJgvwLgTIIjBh2YgGhjQiKZFmUmq9ms5vd93HOqdp7/x4rg7V21blsOgOOOOAGyL73nkdV7cfvt9b6vpaymoYjBJZlZS2VtXRefXxkl4SUR65udoxjYD8mppyJPRKSZRwO4wjAOCRSFIYxkVOm9Mq0mxhyotXmrlBWMKVs+q1aXnE8HglBOOz3HkgK0zTQW7emLGZAHMHsPNZ7W0TcTao3D1x1t6kYI7U1Wq+kbNPP3hv38yM5DV4Q2YIVJDOOo5+Tdm7ErMH0QFwAMWrP3W6iiTl20RTRTNBCrEe+9OLAb/3mN7i5/jbDkFE1UwbVjlbIMZr7njefrXZ7XYHuFY7ROm0jjNH+sdR6bh6B85+3xcb0ZxGCZXNVXzjO4fLOuBtyssJ+KfS+ME0DosIQsxVQTjWtqrRSiZLcLhtzs9WGCbbNTKe1RnlczQEMC9puzX+PWvCsdCWpl8ENowt687y2Ql36ORYmT9DXxRa0mFFtSH3NGIU1NEr6HI9F+N5ffMLj/YnbuytqWdlPIylnNMyElAhd0CWBBvaxo9GstMMwkK53LgZvZ6fbLpUQIxM7erCCQlTpdbVGs2IUR1Xamojxirbr6NIoHUMFwmraptb442+/z/F+5nd+99cJvTJ+/h2aCGusSBSyNOAl/90//Z/4znc+4YMf3XPzfKDNSgqZz7+44/mzPVd3A0sYee+dGya54wvv3vEbX32Hd3/7GcfXnTcfPRC1MfPID1/f80/+m38GKRK10nuFXjbyCnlI7IbEcMh8/OmJVAWVSm2L4SgKX3n+FdbHxL/4v77DfJp58ezAi6t3mYdHXlPZH+54Z7wCAu9/72NaT3zlKy94fpX5+OGRl68Wvvu9T6B1rq8mN4mAHs2hshWjEw8/+A7sEnOK/Pd/7bsowu/+eSVmIY8TWRNr+ZAUMy0MhDiQxx0kaGkmMqIEM3aIkaaJHiANA7SK1mK0w3GHaAG9INr2aLcnO71PlVwjstFbkWRUZJQYRqIIY5x599078hApa3c3QBtW1eqsDmcj7K7vCFloKdD7QGKkR288h1uEwnU+snv2Oe7efY96/4iWGdFuupRdpqlQezWkSTBdmWud+5lp4I0v9lwZSm5N7plyPESeHgExfePTw5HWJ+RzHPoievP4mQZONw3QNjQSyrqgquTRqMwdN+iK4YnRjr+CvP0e4rbGOcvhM8eT198019v3ichnoMi4/Q6bUBHDkybwKYUc0Gi6ee3h0vQDqsJpns3+P5jsQLsyDCOnunoMRzXL+qVxevmK07wwPyq0wEe1WB6iFGMiyJZ3K6DRz09DeidW0xaFkDmVhY9ev2KthTwMvHj+HilllvWESGcaItM0MI4jvTWOj7N/nMhpbdTWzkgEWB7x4I1qCsJhSAxDJk0mQZncmErzYPdpFiYJjEPn3ZSs6PVYrN4aIQ9muubnL0ajTVfXhwasoXv1eDQdlRfUn376huPDiXi153q352o3ck0l0a3x7sJSC6WuRCy7Nqa9zSxdZ1ZbJaVsGcvOeNko5YcBVqm0biwsNNAWu35pGIg5k2pBtRElMojpPNuy0BTeVGepiOUTLstCV2u6ajPapsmjO7U2ex9FGYbM4zxTUE4xcKwrEgd2hytSTDy7vWU/jXzzq++Rg3A3TmZIYwpLiDAHKHTuT515bdyvJgkqa0VaY1qLmci1ypvHI8vpxLwUTk358NNHWu2svbO0wjTusCzAgfl4RHtjvx8Z9onrw0RKgSlZpu00mHZ0HDK7ccD5CRyPR5a1IpJ4uD9SW6e2zjwvzMvM85uRZVnZj8EiiCRSWqXUYjKcwYaxvXWaQCmGzFICq8++rq9vbX0OBm4Mw8Dx+EiKnZv9RIimcR33B0SEeZlZ5gUUympGNTEGa/IcYS+t0/ojW8yaKqTQngzezVzmzfENIsGyJ30Idh4kxQt9XkLg2fN3KGWllBXgvH5qV2ptrLV646ekwQx9SutnXaYtX0rzuqw50yD468zzzKM7uA55dCqruf6qr7O1WLQXYoDOOA6klFFNZ8qv3Fw5W6H7QEW8LttijQxk+Znr6y+PX4jj524Wf/f3vk5XoyvGGBhz5HAlDr9DYAIZUJ09/NkjMnxjTc5hB28uVL3YD/TaoXVatdiMVpV1XZmXlR98/Jr6WHn2/Bn7/cA7dwOHXeTF8xtrPOLAWgvFN7ON0hiw359SIE/X5qTnn0WCwd/qFJgQzRRkXa9JMTJMw5lfnnI0QPHMMlKMNAXMiZSTZfM4hWijplporDJOGTT6QqQMOZKGPSC0Wn0KXY2a14I7/Xn23EY1cGqoOB+dAHM3q2aVaBpNNTRH4mjxb1q9gTPRdFM7tyKw5eM47mMLxQZm+qZrYbkmYG7Ngl2Dx3hcDGxkKw9tEnUuUpXiOWSb89bZC8f1opY1aVbeVkDLBYXYKIy9ITHZMEKs0Cjr4n92t0s1CiYRJIXzgta702uB0grNBdpdO12eTPQdgumASHeUz4o/o/UFhE42rIiusFJ4qI989OkrTvcLS5lR7TzmgSCRZV0orZppRB4JmEYIlF0QdtPA9Ytb8n4kXtn0XcUNCR5mdocDIZjLb0fYT4khey7llKB1lpczfT2ym0biIIYIIJR1pRcTUDx/NnF3MGv43sU7ZyU1QCI9GCXvH/7D3+Nf/eGf86+//V3Kw4KOhdOqHJeZ3SyMWfiz9z/k1z5/YNg17k8P/NvvFR53FZpRiWIUOiNXUZkydHdH1KBePNr9MoyJZ3cHnr+44vX9j6jNitcYzI22Nbi5siidkANJ4YNXRw7TxDe/+IyIcP/yyNENbxbJvHlcOP3wni9/7oo07gkR7m6uLPOqwMNcIHTCEKk98PrlI70rHzzA56/3pLG6NtWfj9ZZ59nvy0BZO/lmgBzQaO5uvcu5+EdXu8FDAQKtRDOPUjUDGzeYOiNXAuIOxN3bInsEDXkKak1VYAuetqard7vGkZXbwy3TONKPJxsSdHNnNHqR2cJLCLzzbMeUO/jvsKff6MBNXIuijSEHfv/3/h7/z7/5Nh99/3171ptHPzRlPOzY3d1iW0hwdAgzYvKBFtIuo+6zxnpDHrnEhZyfcUffzujcpUnc+qmzIZA/yxsV/a1DsebJT24Ao4kpZyOa7mgmb73e203nUwOtJ5fqZ38PF+Tzp79+/hl9+w9Ogr/INJ98AN0opz4xiWw0Lzmf07YsNnx0F+fqVPLqXEhRIYdNZ6QcDgNyZWtybeNZ3oC4DGBrsrp63IeZ1cmWszcMlNqYrgdWz8RLg4JUfw9WwJdSicG0auaCaWHfebTQ7bUWqlNRe4N1PqKqRIFXboqRptH2wmLZmWkc7D5FyTEiKTCk7AYeViiPOROjOW/i46hhyFR8SN0aOWQkCG/m2RhC7j9wOj4SmnL/8IY67ai7PWW74GK60hA6rS5QDWFt9QEJgZBsEKyqnE73tu/HZGYjT2751rqZyMyzDS8Fz+pdWU9HDtPefBTEn12/S0QNrQpidUwTyGl0Jsxo1y8IMZsmstVm2r/jQsqZ6WQSlqV1Wh5oBPp6snv2CCswtwIoH7lubRiy0S1jQKaMBBtuSwiMajXbrkIicDVFOoHShOs8UK8vxiz13Wt6t0ddwQb+tVFK5f5BOc6mt61t5uWrk+3XzRyfb68mxiEx5YHrqz1bvmPv9vmidMZ9YkTY7/asZWUtC61jucXFZi/OdmVdLdpBCaylsJwWzLBGkJjMYOi0WPPY4QyV49EdKZBj4n5p52c2BGH0oX/KZtATc8OZqsTY0ai+Ltozooaj2B3q9Z72LTqsX+ig2D1iJAGrX7a6Vbudi9mzHYchkaI5xVpEhw2QosupJNh+H0Kke33XxdB8gDQawllqdf3h5rx9qTtba9A6pVlueasVCe5gDed1aJuNnR31z4tnOOuygwgS4znPk2i05A0x/eXxi3f83M3il758zTgIIkqriknxIiLGIQ8ajb+fdq7fNTRpy8PrtZ2pRSmZ3qW01aaCrjMLyRZ/swe2Te3LxxvefP1L7IYbxl1kuguEpIzJNp6cM+oT6t7bRh5CcNG3mFOo+mZqN3U3UbNaFg9qPP1xsMBdAt6Ydbc3jkSJhnp2m063VhivD0jYrNS3os8pncXs1oecAKOKIiYmt00aWsy+Wai5RHld0JsvcipnjV+KEVHT9eUgRpMroL1avZ6FBJyWxr/4V3/JP/+XfwxsNu1WrIr/zt4u8RRPg3CDiLPHLgWQcfvts20N1mYNb6jBxbxn+/r2PeLX+y0kQE3c3p+In5FuDoCesxQJphlZKkZvCy7At4YtxUQMVgxq90BhaWy5SfbZjDPP+b3hU7Lu78sNcvy9WRi2WvSARPpmLx0qkUgIe1SUZSn8/t/+G/ydv/2b/JVfibTHmRDNFKH3Ah4TUBdDYWNvoOYQbE6nGFV6HBHXCdSy2n26wOlBmSYgdf7yz37CssJXv/EOX/jynr/49gek/Z7d9YHbu5FleeTVy4Xru8SoRvVaWmNZF0LIHO4O7A8j6/2J8uaB3AshJ+KwQ6RCVvSx8Nu/9nXiMXIbB8abO3ToaBHm4wMhLez3E//bP/8h6zTwlXeuKWXluJ745EdvLIdKBhrK3d3Ib/3KF/i1r73Hj370isf7R6LfA8XRi8P1ni985XN87Vff48+/9ymvT+rnx1aMJvAf/Z1v8O5t4nj/iuOpsLu65fXLlcdPC3EINrWuyu60MobE5+4s/+onb2bGodFaoCDmbCtCioHdFHh2vSOnjPzK56jayO++IO0DOY5IHAiqjNONBba7XjTLCEPmR/dWSD2/HdkPATkdadF0KllNI6kiSDcLdHIwlKtW+lpwA2E2UwUzwAFPV7YWQiGEYqvX2TDF7svuNH8Qcj/y/OZd3nv3C3y0VuY3L908yZfdAAFz5vv1L+64kteEVl0vWuhBiBqRYJu13ZOdv/E3/yY//OD7fPDD75jU1aWxscO7736OL331K9aYeYNJ39xSlSj1UiV3Q2+CV5Db+hLfqgv0p/7L+bMi2xjK4h02beG2joTzKv/054OdHRckxuhMictCdqbmbpasT1/5KWIZFH4a8Hz79bf3Z58tqBnsfObYLmH314q2CWxsbHs/m4v2k/PSL/m0EKyAF3EUK9KCOYQvvSIxosVQqeNpNZ1ih4d5NcpkTIZCrQtjjrz77JqcAmM0jV1OiWnce4W3NdFWaLZmzWopz1nmmXleKZvx3KPp1VLKpJTPg4HdNCJYAHruFRmE3f6W2qE2kycYHdmjiroNSsdxR06Jjz99SSkVLVasp+zazdItvgsoYsODpXdSEPYtM8TIlAKcFg5Xe7oYa8nc0yGmjORwng8M776DAK/WRwtjj8IQIq3D4+mRtlRyNIZMjOZ4qlqxOKlIKc0a2WQmLWkYfH+x/btrp2sgDsL+arLXjfbv9/dHooIkQVdzsG3aaWpSBYuZMoSyLgspJx+cRqadOZ0u68oopuWroRmyvs9M48j1zY6IMMpgUS2xUbaswdWihVpthJQojkbVtSBiiN1xLrR1RWrH4mKs5dpMsu5Xa6Z7F5a502rj9vYK7ZW42xoci7WYhr3p27RTlltCTNRatuUDsIG3arOMytY8X9polq8eZ29CTBZQzT6YlE7O3hIOhx3kRlaLZxi2yJq9+WQYBXUEDuymCQWvLcxRtTWrTVu3hqt3BytOFUU4LQu1FCQZUmeyYPv8bTbKs6TkjHwzwFnX2SquMCBqcirtnZWtwTJWVx5Hjo9H2w+e+C1sfhbz6URTM3Bb58LjwyPjOLLfH6yp64Ut57pWG0fllBnGfKZ8hmiuy6a9tSazdrxesnVv83wAM8CqpdkgVyENhp6vSyVimmntF0aZ1LdNGUMMxBDoDlj4V3xdj74GNFLiMwO7Xx6/OMfP3SzWtVkmuBd/ALXVizFFr6g2Qks+PbiYlcQQ7GFz9KlnMyYpdQt/tsmKlkaMBl0j1qS0KoSYTfzbISyCVKhxRZuQVgujR9J50mohquK0AIv9IAYSxuWvtVrRKME2clWWbhObabenaUOi2/g75SOl7Bxsm8S1Ws6UU+wjAJZdA1CqP/zNFqXkWjsJNvHvHsAKX4UINAAAIABJREFUxn8vpbC5lG4xFMEbKBFB3aK7a6PXAFSjfKqY/ksSKQihVY4PR169nkG6N97GtXji53emzCiXImlDWs+0CbZFYJsaXQqKt/SL2//00nxtzl0/vRRsi8r5N3mRFvACaft55KwJwcEFMCrGhnA+/eL2vdt0a/Mw3RDajWZhToiXe/j8ns5FsP2QaWbFNkh3J+vufBaI5DSy22fXHkRCynQNkDtDDLC3SXeslj1FSiZGd+G6xOjmM2q6WRFizEx7c88MGri6nsiraWCogZCNrpMC5BRQHal715WqApZ/FzfbclGnrCmkSF8aItE6Y6uaCSFQTgttWaBVYugstUMLpBjYTxO7vOPukDjOlXwXCZJYqtBOlTQJGpV1rRxzQ+SOZ3d7Pv3kgeN2fyhvNT92fS8bDGLxKCJCyoHDlMnJRP11VRfoC/Oysh9Gnt3uQU0HGzeUWsw1kNAs9FiM2i5BCEMypDIYmpcFpt2Ou3ffZez30NbLexksAFq8YQmt0RA++PEr0jBxdZjYT5d2RQk0EaIKqkblagrHx8rSGjfTQJZtkKVPGghbo7pPm2W7N1U4WwF4PhhvnzqCdnZT5vr6lk+Hn7D2l+RoRY5oR1SsOB4z4xAJvSEk707CpbFRdfdWYDnyuWdX3OwG72nUJs50Gspw2HN1ew1s69Q2TvaWrV+eI/Xr+/9/nMN1fmqB+Fl/2Zq4bQC5nQf1q7BxPeT8Y6qO2W60X9Enmtm3X/On0cHuY6mf+qaf+rHL6/2MvvJnfIS3f6fZ1D/RS/70sRXS/tYBd2q2jNnQM5ot3iWXbuyLuFizqBB2k5nH+FCMkhlTYrfbkYKQxEx4cspmHnJe5+11QxCqWCOXYianyJAHqth9uht3pqkV0431ZoVqihHBkL1aViQE9oedUZmbu0j2fn6NQDe1a8iEFFn73r5nNbRSg9UZNEM/y9bse5avOS7a+h5zQlp3LeZmsGET2CmY8cfG+ulYgz8NAzGIGYDkwQ2BJmP9IMSQyNFpqNFdbBF6tCZ0yEY/DB6vADYraW4Yl8QaC9uXTB8W9nuLjsgjNRmdGDGUJ7qmdYulWmVgSJHWTIuuujU0hiw3XC+m9qT21skpksSyTlGx2iJs96utpyGbjCPmTG6NkiLQKN2or70pSSKlFjsnKBpMQrRqJXRoTTitxaIfjivmQ+CMHjFUrfX+lvuz0uxeVcjZ4iIkBJRIbxNlyyDVQEcYhs1bOECPLIvp4tw+wgbcrXp+YqdpZekXo5kQrbFG9fw6IoKuNugb9zszRpJusvJgTv8pBCLF81eVPibi1iz6ngO4Rq8SU/Zn1YbRQcyBuVYb+uRuGsVGpG/METGPjZIrAvSkjiDaPmjGM55fHdylFvUMb2cJOOVtQyF761aXF1B1qqlTQde1kLuiyQbspmm0Z95aYLu/trpzy7y2++6p7Kj5+ZVzXbXd+613b5rlvGaeGTSKvUd31+/r+lYd9svjF+v4uZvFDz848pOPH9EeGHeZnEG1krPRW0LqhviRzgXDdnPE6DbKwY0GwpY5aDox0/wJZS7W/ITAWmaWtfDqvrLMlf3NnpwgB5uMPHuefe5qS9kmsEcsONYMVhoxBaa9bWyrbk2Ta8eCb+BqwmGbtpXL5Elt4YxBaMloJbVW1KfXvdlmOA7ZH2QsoBQopVFr4Rgsr2ccB6z0ssVzc5gKITBNibkWjyI4l41G7VNfbNum/RNEMhoMEUjBDG9KL+wD/PijT/jg+z8iqHJzyJ7149dEI12NG5+yTUFbM3qpOg1po+SajfRPFVV8trACzq6B5uYVrQBI0S2kNyRvq5G3AO3kTnxGc8hqDWDv2KChGSWqdPWJsjeR2ukkuqPHrW+W0SbGrr1eePM+vd6akVovEz2bVPZzg7ldk9Kgl2pGTap0GawY6Y3QOvtd4N/86z9mPZ641r8C62yTSYJRVkNgGKIZLNZmxjQSkJ0v7qWwywNX11fEnKygWxUNkA/CMEYohX7MfOPrLyAoOivl0xNf+OKVmRTR4AF2IbPbF6NEerV+PWT+KjeUWhl7ITwUxhzJL66t8UKR0RoBWkDSNVo+otQTj29e8vHHr1hLY14705C42u9A3zDpwvvvP3JwJOT1w4m0KGkQDoeBY1lZP+i8fP2GQ+jsYuTenWVLba6d7aynwqtP3vDxLlLrakVddyMsiVztEv/2T/8CofPy9YnaYPyu0R3feTEwToHb/Jz9MJAyrP1IcQ3EFKGSqA242XFaIvM8M+QIMfJ6UUqZkZcP3D1/weff+1X6q/dpx4+cOg+a7Q8BoAlNA/NSCLGRxk5gQWslujaO0AErZoNOhFhZeuCTlydevWn8+pcOPC73JDopu8kKdp9HgSg+KNuaLtfXgXpwffOw+kATW0GSwjgFvvYb3+DVm9f84EcfcDdGQjFHyqjKkBLD7oocE1DRPAJCpZLwhrEvKBZVUz/5Ht/67b/GH/3fX+aPvj0xl5WwC+SQWGbl+vaWd959h8CRvmF9ZxdsDEU5L/wXi/cu8I//9h/6Z/vMksJnWy190vxtaN4T6qZ+9icvTdjFFqu7o6k7O1x6Ox/8bG/wKeth08/0bYC1/RxPEMSnxc0TR9Sf6msv/7b9XcR0/dv0Hs6Duaf6dMFs79XXtd4VfN+KKZ3ZD4iwk3juJJ9S/nvvlqPqn9PWW2NoGGvFDEV67wQ8VsVBTPX+XwLE7IVgCIhMXgxvA0I/FzHSN8nB0/PazPhCtfnQQS7Op+6GiuDxCALd6Keff+/GhrjNTuBaLA4kCuYG3Ls7TAt5GoyiqJUYYIjJ2E0bUisW0SkiaNVzhNHalFdv7oldGHYjeRyYhpHdYGv01XiDEM1gp9UzNT2K71mtMoxWf+SUba3o7dxwR7nsO+LZwb01eotIHLi5u6b2jtDoOtBaNZqijihmwNLdxCbmdEa7rJEyh/QyDq6Js3zmzTxkXVbmeYFg+Zqtm+5fvLiv3TP43NMo+JNlBBgb0O/2A7vDyIBQS6Do5hdQ0dAYxwYSaA2SDJTqMQkYHXTLz0tp4PRg+sDereFpzfaD07wgTuelW+RHSltDJJyOJyRa02axPRZnMkRrCi2Oy/63HlfLFuwGNCzVnpfWjR785v6RUqo10p7zd1pmFGUcR2uW1BogGzx6vEerTGNivxsRhFIe7dwXM8obp8kM9zRSFxs4puS5i8kN8npB6H5PRIYYvV6yeqN3Ze9eG4YUmoTD3Loi4801IsLD/SM3+wl5dmtpAr35mrCjlr4tu0ZtVbtfuzeLQYDeyXFjfHWPl3uSbd27uxmLgTXBzZcUM8tBiTn7UMju9bjF0bTt3hfPILcaLoifS+2IxPOaISEQNFLXclmnfnn8wh0/d7P4/R+85vhwNCrMg/HuVSK73UTOkcMeUoJZbYqWo9Ey8gBDjhcusyiqK1s2UghCjgHRAM3oCzlGM+yo5m4Z6Dy/HunSWdZGk8o0XJkjkxpZfNsodzujs5RSWJcTKUcO13uWdeXYGzmZkYy2zf3EKJ/DcADXsu3GA2VdKOtC743dNAKBulZK6fRum0UvtkGmlM4NV1mLWZDXQozN+rTQydncD41uUNgfrhiGkRgMNVnXmTRNTsu9FCFnaqX/W5TImAZWEq1W1G32W114UOWPvvN9/s9v/znrCZ7dHogxny3YVRPahU9fvdzqJnehFEd9N6dD3DBAQPDso+Ab2mWR3uqjt+mpVrCFEKwoqA2ieINmzbaEiBkvGnWuVRP5m/26NfnVzSjWZohRc2pPCoFUGylGB0pcJ+kT69IKm0jcdDRy0TwGN6MISmuddV0B4/VP00gj0rpQS2cauzFB3GgGhb4K07Tj5atXfP/7P+TlT75MTiMhNXpfbdHW4Pl8nV4Kui6m6jqIOQbPhXeubtnJCNmcM8GBj0cFsc/V9EhsbuFeZsq6EkpnTQFSZEgHe5jLkZ4zYR8JUdDa+MffeY+SDS3o3ZComCpMgTYryEDIgVrNne7q5hm3z6548WyifdK4uRuoD7b5vnl44NWpMLfO8f7I9z60AcbjcUXrkbUk7nbXdOmc2iOfvnzF8WhB9jFGo4pjCFttRrE6ziunefVGX80kBbuOWZTvf//7zKVxqp08RKY4sZwg5xvaIhzf/Jg4DDy/u+L5M4tqqFVZ1pUmkdpW0i6RCTyeVoiC9pUh7djlgSimM+4dCAkZ0mWjDQFCRHpHpcNg2qVv/YdfI6RAf5jpx0LMI1Gd/qUNeiGGA+jAGJTnNyP7SdkPjb94/zXX15mrw8Tf+/KH5yJ5o0OK2ETWxremv7LcQmu2ojqyHaxIj80avS9+6Yv8u393Z4W4+o9jUoGncTsEs8RXBFUzHEF189ynSaOvj7z7tfd454vvkfIIayNH4Wo3UZuyTwPXwwQ8etdnxudGXffK3NFq2WJfEKIqX7u9t4Viq2jOf+YCm/mh/UmOK/6hXFO8/fz29TMa7P/SUazqxc12LK9PwZB8p6T188r104ji1qS64EqcG72hv9vUa3tf57d5QRnf/jBvv8p5Dzy/f28ouAzhtoLq0sSqv/ylUe3dp/RqkVDbEE99vUaVcjLzjUEiiWgcYOPpAuqa9EQkotWulwxqzXRp57MhPhPpPnzTbs6RxtyLhKZOScPW92bO1lECWSwuJqRgZl61X1gr2mlbrAzW4G25zNZ8OpKnkVIK0757tIqibkIccz4PAsHyjHv1zN1g98PjMiMElmUxRo9EpHUONVjuYVTysGPaT8YKoNJLc9S6o2L1hUq3WBKsAdyerdYiBMtL3GoQkWB3qdg1jjGiKdGasQ+q2rilV3PprKWgXUkxW4RUM5peiOZcG0Kg1MowZcbxypq+UpiLG4eFBCEYgtaqNS5Yc1KbrSutWcTX6/s3zGVhWxpOy2qUw6UjmrDom2C6NGd7bGZW27J0NY42UJsiei2U1pw5EXh4nOkKQ9iRg0UPLWWltGpNkgSOS+Pl6zec5kpvYr5eIXJ8WNjtJkJIvHp9sjohW4O+1k7tdm6HceB4/wiYWdT11YRgTqjjMDAyUEvjtK4MOXJ1taMs5dww1lJ4WMz1vj+eTO9YGzFmy2L2IdD9wz13dzfcXt+wlsJHn3zKMOw4LjNlbex3e3b7PTEmss4MWbi5vWIcJxZ36x+mBHREGwEhx4vBVQgOpNAt2qMZarfMC007w2AmP6aNPdKapQlY3QLDOHoN2XxvVaMqH/ZuwLSapjcF26PQ8/Be1Ybw0SnuNjgX1sI5ymRzxK/FapQYkteF7bKwqWtm/T7NeTC2lUvJWlU3eExnZo/VXGYmuDlD//L4xTt+7mZxECHuMykHhkGAgVaUVleyDgxMaGnMdaG3xkmP5ggpsC6FaTecJyJBbINSqhuOGHH0tDbm2ZyWVAyq/tGHDzzcF8ar90nBtBYxBq4ON+zGgUDFypVuNJlWLKhZO+OYOVztCVlYlhUtlkMXxOh8qBWjMQab8Dvqp1p59uyOMQ+03qnurLc1HDEOtnGqPRBRPKoD22xFhJwzKUXEuY3z43J+oLoqpzf31PKpndshEVPgtL46F0eCME6ZcchoN4FxzplpF1nqkdZWm8AWE4jHmEg58ivPv8hf//qv8uHHnzJOmePjjITMWhrH00LrgRRBm0WKSEhUn8Q9Da/d9Cy1eRCuCto3ZJbzpm8TMrCFqEEyqkTvlSFHhmFnFJHeicPANN36MCEaX757RmIQ0mD5SDkJQ85M03g2xZimkevrA2VZCMEoWTFGsmc1DVO0CZjTdrcgXTNwsCl2TEIIyRY/sUl39yllV0c5y2qGQj2RNNB0pmmAmImxM6WBL79zxc1h4NWHf0EvnZvrA9AYJqNcr13MBj2YsU0SLJQ5Z8LNSNeVT8pLdnlPaN3dOiNIRVplrY9EEtKLOf/FgWk8oLlByHYPhkrVBR2iufedLC/NppqRqVjYdVVhEDM4YnkgCjy+fMWyVCYS4/4RrgfeeeeOr//W1zh9+33aXvjkk8bpWKjMHEb4wu0LSMKPfvLIF54d+O1vfJ4ff/qG+2NHykIKGeTz9Fq5+5Lw+v4jXj0cyQPk65HleE+pym46QIdXn97TiiLFNKY9dlIeeOfmi7y46yy9U+ZORhheRAKRIBNhnxlvEr3Cx69f8d2PT9QVllUIoxLqp2hbud1dMYw7psMLJMDpOPN4/MQL2kRZf8hP/uh/Zx/NSKm9t5IkkI8ny+Msi9Ex444QR8qH90hU0jASxx2tLRZkrgnRSIwDRSr0SuiBm2nH3Q4oJ37jd75ijVnv/Od3f2Iow9m8yhqtmAY6QuxYk4r6RtpQtXvcJYuwdtr9R3z1i895/uwAoSMhEWMyqlPvVJkYd88RHYgVhtqNYRASTZSqkFr0qAZF1orSWdPIQ0k8PDbSUri+GYlT5E+/82fc/eEtv/Nrf90QAhdIBrKtndJAjBrfSU+awAv66KFgb28s/rWzK57Tu7a15aJZvCB9lwJjQw23rixAtpD1tTtdbDeapfzmkOwShe3oGJVZz68Jobtpzxkt+/dui0+GZmfo0tZCOBvqnO1yttBvvfxS8Yaqb+gemL7S18ct27O1hrQth82Qz7l5U9iVdV2o1T7zUgoP89HWx5yMd6OGagcv1lKy87QZz4AaCtXN5r60ajo5VbR2aLa2kKNLGjspCTnK5XVCcC2XnQtJ9oFWNxGZhvHsG2Af09kdwXCp3hRd/WpHobZKqytRIseKSVmwvWmZT+x2uzO1VEJkGDOqlegAt2H+HTpMcaSWylKbXfNgCOBhN7Aub/jxm49BI2tX8jCaYVRXUnQDO7Hms7ZCWY3WKU4tDiGSBnNKP63GNloWMwTa3h8i5NgQOktRUOHq4PmA1aiG0hTccGQVYT3NcLLao7kerRRjQMWcSNFkNLhceJiMJptGq3Fir2YaFiCMmb1kbm93jDkzn44+YAiYf4QNY0tvnNZqOkqMcfUwP/oAdECJvH6zRSN0Si2sZSWHzpgzo2sCH+pLSl+gC2M2R+L74yNlaUy7PVE6OZbzPV7o1F6oPTMNA1/80nscHx/Pa8FxMaYZBOppZsgWibVpIFs153Wwf085s5ZO7asZHwbLlN4frskx8SXtiCppiIQUGKfhjBiGEB0xg7UsrIvlC+b4VRDh5evXtGr6ztaMBjy3O9ai/PgnJ9b1wZA6LFM8BmE3ADRKDcwnm3YY1bSxGzPjNHB1MOAlpxHRwJuHE28e3pCzreun9cTNzTVhmDidThxf33N//wBqrrjLWkGN4QWYuSKOh7icZhsUESzCZJPvpGR1hRkOKnnI5wFW8NzFeZ4Riabx9yESaiaQvXXbe9zRNQ9mEhlTJ8pw1jc+Pabd+Nn94JfHL8zxczeLX/3Vu7NeL/iE2xCb4BOS4O5tnHnS4BNTtWK/tXZ+wIToGgzLwWndnMqib5I2ael87SuNWtQQHhFaXaF37p7dkaIQpZt2KQSz79aOtnamO+YhM+1Go1+sphdMMbJFbW21RtfORgFVlN00uRGPNTTNDXrMnUs9D/EyUdmcX21jMLQxucah90YMwR86zlQgETO1EYf8dyTbDKMFswpm4IPHi5jRTiAT6GGw6aVTenpXhhDZTZl5XfneR6/Yj4lWVlSjuZMFMwxIOdGqFUghXs55ddtlC7K3bMJpGp2a5A5hQXxCr071te/PyfN5fMobYiDnRNoCr72uG1xn0/uGZgYzeej9rEdMQyalxLqsZrHuKOLp4URvttAuYbHfiQXZh2Ewdy7FqM7qPHzVc66jl2cWZK3qVA01i215GxuwWA/15jf4UKNChfvX7/LVz7/gt752x2N98ABmIUd736mrZZYFozCLwJgybSseQmJ/uOVwew1imkIN/r47ZB0v+ImYtuEt3ELEzaUcbeiKtm4kthDojlyZgYZNDQmAT4RjTIyTDW1IkfDsilc/+DF//pef8i/+8H1OVMoa/NoWYhTy1QP3pxMffDjz+HBkXlc+/OBjaomktE0RzUXv+gbm+ZEmlVJhXTq1RGrvJBFqV5ajFSc1Nuim1VNVKis//mSmaiVEYQyB5ZPkqNHMj3+iRkeP5lS3GTI8HGeGUYGR1ipt/ZQYAoddJGVhvxs8J8rMXJrCUrx47sp/+4df4TBGTnK0Z8AHR2W+R3lEUiAPHp8jjdDNiv3SKFjWnQ0fCiKFJtjvOc5uBgOo0cdRdeMaWx83lzo8skZRUDNY6MF1t2qIhhKItfC1r32Rz33uzpzqmmkVu5i7otZKPS4UhBZ3tP5ghUAKEA4EKugR1YY0iDJSl3s4vUGbZYsh8PBwolXlgx98jz/7sxuQb/nnreZG1DtbDIO1Yv61C57GP/3jbxKA/+Kbf3K+p7fTZlP8TTf99C7XJ/9vDVd7ssae159tj7HN5vy9p7Ly8vVrpt2Oq92e62kHzjwQ2do31z57Q7ZRXs9MCXnSVm5DNJG3/8Ebze4NwRalwdYA6gWFVEcJzqYPtoD53eN/VxtudV/XNhpX9wJwLpXjUuki9ByhG31PYnLkRxl65/b6BkQZghCDkIkmW1M1O/7oKY5q56+L0IIVk8NGS9uo0WrLet/YOHRaXa0hEfv9MUZDtdUobkb/tX1xGk13h1p+pzFK7PmKKZ4pmzRl9a5dJRBFsPCLgIZODRdabIpb3ZGsoVWl1+KIojXB2u2z9t6Z62zrejXK4hAzswin1+bwGENmP40M3iQplsPYe+XhdAKEqtaQtNa8ObbQ8YgNXlJO7AfTrqUwXmYYHWJO5GSDqclZBNr7Ge2vrdFLI2bLZ42tM2BmIa2DDtlqqtZIOdjgeJxotXNUa/zezCfzW1iFFCI5JOiw1Gq5ggjTNJqsplotlwfTY9a6WkTPuc6w/agPid1uMMTdb09dBkofaKqsbTWXTN+/8fu/hYR2yBrI3nwNaaC2E4oaXTN4SLzvYcPVgehNCzRy3iiMgZs0obvRkFLEG6INaXekrBTX2dnzO42DG+ucjLkROiqN07qwnkzyNE7W+NRP3pwpvjHGs0P8si60rg4o2F4eg5LHiDahdQFN5N01tSnHow3f9/vJ31s/3wTmbwCn1YwT11JMcoN5YByPJ1tDQkRUjL7rLvQ5RZMbvbbmsHWbEExDImXTpg+T1QFWT1rOpYEa6bw2l1psDQhitGgbIVmtC5a/qI1eCq2pr63GXqi1EYJFgAXBavIg50ZQVc8uwa2W88q9oaPb+dhML9d1/UwD+cvjF+f4uZvFm1ujYm7UmS0oNKaLoU3XRvSg1FabU6usIRly9n2wm+CawDTt0G7ujdoaeRiY8sBFX9YtF4kNHBLWZaG1yu3dlemvxN2XYmAaJ9s0Wr1MMgR2+53RXkrFglyNGmBNnuneNhqFqtG4YtzcoWwtq549I1jo7PYQBDf4UZwiszmHhmgOal1pXZ5QVU0YDkrOG6fbCpSUR6etGNWvt+Z251sIqmtZiJZbqYIG35ibNUpWqHSPzzAOfa0eGBzcJRQ5W3VvNARKcz3ppjeIRvlMkd4M7QwxX+ilQO/uNKZmwjJNA73XcwEY/bqEc7bc5pBqjqgheCGwbfatot00lq01Hu5PZgXt9IczPQKLIdnui64Qkm2mdsl9Gt/rtg9Zro8XPzaw6F4QKbUUe58hmAYUYV5ny7PKk20SvdKkcjwV7q7e4cvxwLPPPSPdB65vb9DemcbBI0E8hsGLLzBL91YqRKOWjrs90zChwbQFBC82O4jYRPBnZsqdjy0KQMx4Z8MvovCf/eqfIAL/8/u/yQbCBDALbac9h5SdFm50ropwWmHtkbV28pQJKEE31BPurq/54MOZtcBcAkOeGGIkjIrSqKsQUuLmKtI08/GxsBwXQjGAuHvBX3tnLa4pE0MKtg0+hs5paVRtpMEm3/Fk09IujbU2hMY0JA7TSO/KaS6UVskakZRpGrkvJ2iV49KJofHs7pqb2ysEYRCBMCAh4yfc7qUgpstpFkvTQ2RdLd9slJ3FhGhHS/XmdTu5ag5+rSO9nQcTIvZ8CsXQMVX+8s01AL9283im8bxNaeTcM+nWRmzGSj0YAiNCbzNf+MJz7m6vydHYB6qOSgr0WlkfH/jo5SuuXyQmMSQnEgiYVCBoR1y3hmTK8ZHy+ACtEMWGZ/OxEBVeffoJP/7wx+ZLH/RMI++9EN0ShrOGrp/NZwD+2Q++hCj8l3/ljz9zFwcvoj4zYdbzp2fTam/nStX1Nf78bj9rJgoec7CuzCc7x2OMaMrgDZClt9n5tcbQ88f8HIat0dm09VyAUrMX8cnSk/e8IYyd7d/lbAu//berI17bXrLdd08aVLwZbk9//TZQoHOaF14dV4oKNUZbq10vr2a8bNfWEfOIUfczZmgjIoQs9Oz6bczaXqNAMrTN1srL+a/NzTlK9QGVWrOBudWaOYyYb3WrRlAO4YxYIrZvbayP3r12iMH69M3L4Dwv8dd306gmpuNki78SQTxf0TTcRm9vvZ2NR3qwKAJbjw2lDCEQxJxHS63e7JifwLSbyDljTq7m+GqaM1jdgMzOiaPQLn/Y4GMRDKUPYnubn7vuJlUSrCgXwU1B9IxyarL1VatRKZMb5nQ1nWrrVge10jyzU0k5254SO6VmGp1TXX2Q7ZusNwi9C+vixXuw+ylIwjxtfL3ozQfv2c+pM4aSeENha7yvyDQClUZqA6oZquWy1t6o3WLLVAOjXgYzeUikbjT7KEZDFH+OeuuEafTrbvVBTOL6WTMaAkheExX/rOYJaK+QklhjvcmVk2lFj726FjExpsjcqt9HnNeQZS1uLmO5ldGZYqVtTanQ1BG7IZ7fU/Qac7+zYc2URlQTt9dXxpzyYYzlfioNMd1tbczzYvIaicynmTcPJ//8RqmWINAvRoGigbJepEI2eDF2WIgRrUrTxjhmYvRINo0YP0NA27kGjT4U0GY6zRT8CitY9NLF2GbblIxefRmP+jTrzJ7wtzZyAAAgAElEQVTb6jt7jeY/E88XZGOjWdli4MQ2kPrl8Yt3/NzN4sP94jeGOZ6ZlqqSs28AanRF0725ha5P/lpTHpajPbDRivFWC0a9VtMsZsso2iYfIQSiGFdfRRmHTIiwO0w2WanVYHWfkq1LYz0ZwhFiOG/mIrC+frDNvFsRILLSWiVmm2raFN+alpSyNaChnhetlJJPYX1Z8k09xuwuXK7Tk2CblQjalNIuyFurTomBc7GyLla4GPwPp3X2JuPpjL1cLoJcCqcUrElQbJodtVE08HA8cbjK/NVvfI7buKf2wMNpZS0zD4+PzHPj08eZYRSS6yNq7zSne4aQXOfXWbtyOhn9o/VOzCPabTpljaZl9bReQR7tfcXormHVFxqfpjff/B0dMPMDOz+7afSm2xdKVXC3rrUYfWw7I9u5GkabQHa3pR+cY78s1Sfgdo1y9jgUtv1TkWgGI2tZ0K6Mw2gTVqdeqZpWLJEQLYjCEAIhjixZ+e3f/Ra/97d+k2/+RqIeO0N0BNqLwLAVkj4lR7YNzSjLvdrnbsvJNVb+fmM4f3b8frPR/7/noRRDEqNyXnSlmDsuQJ/dGMNgH6P0SSdtv39bpz98ya8+u+Pu736L3//732LMA1oWSmlosXzLpIH91TP+lz/4VyzHzs1wzT/4T75JK4UiM1GUpFaI7YcdP35zwz/6r/8H/vRP/hQNC0PoNDqhLvSitDEyAlfJ0Nhj79zeXvOtb/0HvPelHW09WcB4HojSUUlIGszqH0VbNfqLIxwvbm84HCZS3lEqnGaj1M3ziePJUOjoNM6AofOf+/x7RGa0rzZw0EroRpluBHqItGxxBaKK5GxodCvgxbciNG9ktm10WwO2h7g1M2zSrvyjf/m3APgf/+7/atRgn6znnJAQqI4WG2pja2LXdtH2yNaSzAwJxjgwyh7hNRFjEBCh9kdOpyN/8Af/B//wP/1t7q46bOZc8RWxczZcCaJAo8yR06mRaUwiSIvULQN2LhwfZj45CVMUpscIsUISGtERgGZOssjlHt6eXOGsfbt8xYdqqueiW33AuBUX23TpaZSFbGjeWw2meFNtf9uLcP3sDoDkTAPVwJTHc4G4sV624Osz/dSfrTOKuBVMT17vLWMcvAkLblzW7XmOwZr7rQk8Y5Jbg8E2L3Bjs+3aRjN76uoRByKkmFhqYRgiuxoJVXm8X329hNNp5jRbCHxpF11mxRD8bFNKxtGaLKKjwK2zGwdiFMZow7LR7/MmVtie5pVaTdcVYnQjEr+/a2E3DTy72pNiMJ12bybj9XVIMUdyH1OyNZBDHmyt8sufQiQmYV4Xpzlz1qIngg/fnFKvncP1wWKjuumWp2l39g7IA+eft49ue/g4ZFpTXr1+ZF5XlrL62qn0yWQzSQwhbQApcThcGTKj6iZ3jS3/b7u3m0ATZV5Wems8nmZqrZTWQc2Rs1QFIj1FSIE+5LNBWxQIvXO1RIsjSlZkT4Szu2uMA9eTexD0Tq0niMLt8ytCiNzNe+g2HEAwbaR2tEVOhwNdKwRDRneD07O3hmC3c1ZUsMFPhyCd3lZHwKB2Q6MDM5bd2Zk9f7N7rNfmtJy9FurravFJQdiPIzEk07Vi5nw2zO2oGo25dmv8gwR6EEQty7o2WGqzeg84cyBFLW+2d8ZsTdw2kIghoh1eeOPWeiengb5v8K6xt5Z5PSNim0Sond1Vjd6s6kHyalTtdUPNQkTUqMH3r95YOZrMbOjTVw/UZlrL1o3JEsV03jnZcGSMgQZMhz1c73nvxSaHMVZdEGNsgbAuFoVzXn/8/XQ1V/7alMd5tgF5Bnrg4WHZ2jy0KU2rIYje4A05U9aCSHWJvNWCANmlUdt+E+NW17Zzc15bJbhkStyV1c6Z3T8+GQPB1gbt58ZSVcgxYtqKXx6/iMfP3SxaQ2gLb7nsp2yC1e5uSjHZApC8YNr0cNFdj9S/TyTSilEXNIC4aENEaKWRolGvWrXGqtcV8dxEtulHL/4w+fvD/v088Qejmzp1VhxqtwKBi67tjGrZoh+inOt0qxfcrl227/cGsJfza1t/YJq5M2Tjv3sr+Ow8WINLv0xu6cFtwNULju3H5fL7fbAegrlZ9RgIkgwlQMkhcjVMPL6wPKz/988/5MtX7zKXlddvTlZLRVuIqnR6FetDu4XzSjB31uYxAjG4b6MYZbT1wOlhpqNkpxS3ZTVkSqzpTBLQpN5gR6cYbNQhm2CbsZHlQ6mas9w4ZOQ8h45nKoxtzGbPbkihNVkxBHZ7y7dS11mGnEzLEVczdQmeSZkT42D3yDbJyjmzxXX05gG3ycXXxqShdyWFhGSfqqqdrv0w8NX3bvjyswlev0Kr0B0NfzpMiGErGm3BXas1nZIHoxrit0HwKloc6XlqGfnvQxXVUZGth+79EofwpKgOemmy8YHGVq2b06JtDO20chgS0+EGDpGI604l0mN0kykhhGt+83e+zve/98jHHzS++te/SetvaHpP6IHURzQ3Qim8wzf5wpf/gO9+/31e3y/kKqQk7DQw1gBRuJaITIlxjLRT4/b5C/7GP/j7/Me//4w831NqoE835PFEZSBy443vA9oWQvPJZVkIywOSzIxGyWi6QyXRQ4TsQfK92+a1rPQCw67RZqMq/1e/8j4C/JMf/LoNlIIZVwy7HYTI6c1rQ/ux9SEGQ+e6D7UuPb2eEavLtRAkmgZqa4rCYAMG6xUFSbZebrLEcyexXTPU1xDLc4xp4vH1I8fHN5T1nl3ypqTjhZQNVY6PK7UKsVUTr4RIC4YWbk6hTSu0E7t3X7B/8ZweEqUvDFEpQekB1tPMTz56xXd/+IZfeydxYLUC2lE5CabJ7N6MP50Xb3fxll369GtRsXzK4FRQ2RZ4u3/FP87Tn9mazQsldHshcZdXWysSON3R7t+urjFyZCE6OnR2VvTB3saWOR+yMTYuDeQ5POVnPaOq58FecIQKkbOsQb2ZfDp43Ng62wRfSXRvImx4Crspk8fMzbOMSuArFHumvXBvJCtgoyMJwci7aCW0TujdzndvZ1qrdGuOCJGQnMq6NeNixkml275a1nKmzrW+0eosMN32YaWsq7lOhuQDU8uC7KqcVsvM627eNHeILaLNBy/REEmNkb7aezRpi3CcV9sjRWjaqa1wWmZSjOQYfQ+xzFpjPGVi8tgBjBlkzqt2XQ/T6ChjofXCvHQe60hyN+LaGqfjiV7VmRUWbVFrc0d0q12GcSQKjDkyjqMhc8PAhOkW+zJzOq08Phz5+KGxdGFeZkIILKtpKOdlQ8m65zInVA11fX635/Zqx+115mo3cnezJwZrtJIjRXFspFGQ0KjzzOlYqRWOpbE2NR8IjwOqzaiaY5oJEti5Ti7FCGr7ZMLM3VIOhHFHbCO1dgZAJHB6OBJUGGOmYrmUPXoeJnav5mBa/eM6Q0rkkBlipiwr11dXzPNM9L28qNBaYz6d7BqH/4+9d4u1JcvSs755iYi11r6dS57MyszqzMrKrHJVl7vaxu1qWza23NiWDW0J7AdANsbIQuIBxBtPSLaMbIHECxI8AAIbCwuLm61WG4s2YAss1O1ud9t1y+6u6s66ZGVWZp773nutiJiXwcMYM2Ltk9kIlYRUD7lKleecvdeKFTFjxpzj/8c//uHp+24hzfouIr4nZbH6xEyy5xmBk+0J0zgxT6OBKI0bgpUMxdApcJsTfa9x5ib2iGjrj0a0Dl1vZk3aWoXokZKIaIuUfqP9RMX3zFNWCSqOw6GSfdEYx9RqtQrDyYZQKuM0I0mJFl8EKouDfBUYrzWu8i0rbuuQLgcqi91aPBE7dc8tJv+vYrWbruP0ZEuaJzY7R62Bk34LFFxUkmKcJjN2E8ZpVLmq7uykaSaXSipKXsz7ydZZQcGiX1SBztR52k9RXe91K2ngMpuTqs6j4AM+arw/zeq1EYJn6DbWA/zj1w/j6wcGi/OsdYAxqNSoVg2yc1pbOhTrcZRSxg+BGNRQpL3aQ9D1HbXmFTBUbVfR/h17rw1BU0Wy10UfdbgLlnYTEesP5TTA987AgoEua9mhm5NOeW8GA41ZdlYP12IObRSqfXZaiONMbtFMWTxah9HAiNbrqUFLzkVthPELU9UYGajWrNdaiFg7Ef2SNWDQf7pFoqT1hHa+Ns4OT3EdTiY6dwDnqZwz+8Dzn3qR115/mV95822+8d132W5Ut1+kUFLB4diEnTpqScH3geaGFZwVM2O9grwWvwsFj1jPI0FKwlVh23dmu1xIUui7QBe0L5VQCN4Mg3TU7dp0QTwkrdUL0fphWr2iWEAntZLmWcFiUPtwUNe02AU18dBmViqlIikpj2gERGWxxqiqTYloA2AqSK4mhVEDiBijBZra+7OOBZFEDVoc/+T6ikdXBz7/+m/jk5++w8Xzgfq+9u7sqjcCxLIgpZInzZDXWklVa06riBmYoO6MivQWcFGdY7G1hJtgsQWpDYSINo1RA5B69J7jzwBOgxD1nHA3jutNGpVPLnj0wXs8+uD7dA5ympgmZZgTkETr4PwsfOqzd/kgvc0/+aWv8eX/8Yr3PviAd99XJzfnZiodM9f89p/4cYJ7wnbTM106Lk47Nruel168YNMFvve9BzzZZE7KBkTofOXBd77LX/sr/wnjH/4xTnaZd96/zwePMtvTM0KZuL0RPv3pV/jsG6+Q50QIcH31WAkaP5Cq48H773N9tefy6ppUK2OplATDZuDOrS27beTu8+fE7Y47f+B3W8+pyHc22otLB0qBlTjdIIM3R2S3mpaUWlXSrimuNtg6/AYesfpl/Y+Czybo8sb+Oxq7r3Ol+ibttkw/zoKIFRi5UqiHkd1px9nmgvPdC4zzOwSXcHWDj5VCIteeL37pn2F3e8tcEj54XAx4SRSyOmHSnPBG6tO3+eLn3+C5T3yCb/3mN5E+ESYAoZQ9999/l7/zc7/Cn/lX/ji3y9+nsqGGE73yPIAb9b2tjUBbz+y8VxHT+hIDQ60fW+vltfrjmLTJfRiYfchIz9u3eX0+VrkoVKf9CQtCR2/npfLGItVqSu058l6BWlm/IFj93XIP2vMoa5sN1+5/OznLnCzPsa3jxRQIYERmA46y1k3KsieaxBStS0/jzNP9Y20EPqsr9JQSaa7MUyaVQpozj5481RZUwZERLk629D5w5/yMLno664e3G9TMZc6FsdRljpaq2UnBMZdKStZWqQPxLKTd4CIhe4aie7OrWjbhYsBtAzAYKVLZ3j6FKuSkJngxRnyvIJOihmBXh2tAndS1TYVmfbahW/ZAF3XvzNn6tLX7JlBkh/fBgKYG4LVUHj16SLfZEoMnl0LcRPrNCSenzWTDcb7Z6fNpMryzYae3zeqJZ1MzqcGOZqTybGSrc6RpRmIliXAYJzNUgjt3b/Hcc3d4eR6pNdE7x9BHzeY4x5iEVDTCeXq5Z58Lj65Hpmni0ZNLvv3gPg8fX+l5EagVUi7Wu9dTq86hWyennG577j1/Tn/SMzx3gt92sMmcODjrOl453aiItAuMaebqcM2cMofxCsmZ6CPTmNiPB6p4htBzdXWtBCwd0QWG25HgPL01fw9hByZjFMsA55wYxwOnd28xHUbGnHC5kqXy/vvvqmvmPKucOfQ457i+3rPdDnR9JF1dM8/Jsn6ezUYz4pdPr3SsS2EYdmy2W9598IBaISeV//YW0/WbDb5Wrg57claiI8isyqgrrbftfCT6jsN44MnTa6acwHm6GLh7cRvnIiknpjyzH1XlpF21KqBS2ZNhoHZxAXg5Z+7du4V3nsePHnG6HTh7/ox+GLg+7LUWsVT2h5GeSLcZmNPMnJSImMpkxBI8ffSUmgs+dIzjyDxlcxjuFLCJZz6o0ZD3mdh7pAZOT07BW/sVF/GoKmG6POAcXOw2OA/d6SkgzNMEeOKwU2J8njUmwpHSbM+iOhhfH/bkWui7Xuv6XdbSr1wRNxNCJB0OSlg5Dy5Ri1h9cm+lWRZXfJxZ/KF9/cBgsessZdxAjNX2Ob9af/ugbHaIR/KGdfdcjqXFudUkNGVh/4pJRtrLeU/stUVE7K2upG0YWq2v5nlOFyrn7f8OTSRgxgDG+Kw1iJqRUAaoLpnBVZ0PqvPWRsCuyTWqbpTgjrJXQs7VCn31vWoxbtIbS8tXKctQlCJmjuMsPlXWM3hjQluyyXv9zqMms87r+UVRUY8wgAS8y7iQuDrMHA6Jvjtlu8vEIDhfSMVBUWCY0ohYgbhDpTfF/r30vzLTomJStcaYNbbLe1mMCmoLkJ1mFIpUc9fXfKFq8PV+hbA6DYK2AlGJsCxBdrsPwQfEpBp2S6midRG91euICCUVxEWVSlXsnXpHc9Esn7buQDezqnWqueg5uSKIqAECokC5Fm1S3PeaQxhCx+1NwNeOciVwrZIK4ajRtOjYSRVSSqt0w7kls65GTiYVdUYQLLVvHwH2WsD7oadILAgTmywc/UaWtyyfab9fGE2rbUFAZk42HfHuHa1Fc0Iualne3BLnOlPnwq2zW7z00sRnf/Ql3t533Ln7PCcXhblWCoWaYBq3PHdxwhc/+wnGR4/5p0+eIEEDzP3VROlUbtZ5DZjwjmETKdlxSHt+7h9/m9dfuc3Ldy44z9eMVGLs6HYDNXge7Z8wTnuGvucwz+Ssz2nJBWLPyUWH2wxkEbYlkItm5aSPTHjeevdADDO/O/cKh81wCgelFU4ZsFGFYr1xW6zN6mLKsjKwOnahgfKFKGn3sckpHdXmg7f3VVsr/QI6103UN6lBw7K+4Eth2FSee/6CV19/lS9/9R36ADiTxqHtbm7f2rALRV1KvSP4gqvgiSafVYAmccNweMgnP3GXe/du887b0WRGDl8dMQieka9/9ev8o3/0Oq986UXIe5wUnOvXLHmDhHLTdVSHyOq92nDdGMc1ewdWwts+U9uY35zXXkn6Nctnz/MyZm3RlOZ6rNmVJWe4/L42Kkr/a2vVb5XYv3FZbm3l8eHfWYbUspfF5JIti9rOYdVfsJyv8+pSuxpAyLL3bYYe6cFv7Bizp9/CTuryzO4utF1VFlXcbGKki5HooNakmSsXdT0tResC+w6s7UUQrRHPRVULXdB9O41thIVExpGIIZBjUs4za3ZJDn6pm7ftk/1SW6lZaO+dmoPZd3mn9ZHZgtJS69L2qJEGoDJUMdI1hkDXdwpcYxOGr+uhDrM+e6VkcBERmOZMKhmo9F1H11lPYtF5pR9rrph6xdFKZQQ12HI0dYboXlcr0UoShqFf1gfNcMIwnOh+mRJ4j1jdfQxiGV+4FTecV+HOhWbzxufPKVXY70cET65CStl6LkZShas5kUxh03nPeDiwH0cODx6TRLM5zJkheO6enyvZ2nkKlVLU+ObObsMmBC42A7fOzzi5qMzB43xPPagz9KZWfK64oC2wcI5ZCnNJuGylRyEas1PV8M154nZLcI4hKklzMZ0SQyTauiq2719dX+GCtzZabpG/qv+EGbqYjDK30hTnONkoAMmdGfUlVa8d9ntK0TpQcUqSuVIZfCDNmVwK2Rf6HpKRVcHIbC+OQ55xeOYxk9JMsFKZlp1upE+8zsyWIWsS2KdXEzF2XF1dKxCetPeptnOLlv3XmGSekoJ/WOKf1m9yO2yRTkmPLkTGqOVgLcgtBeLGanoXMjiS6kwtk60nmk2s13VRxV2Os60rqvJq/RNDuMKHYIkXNffL2boWGEGq8leYcyI4dZ4lBIud9NkLcbDYSIF1NRPKpjTEOSrl48ziD/HrBwaLLXAA1HQkapapBcG1qgyxNQEt5kgaY2NGW2bMHEPBegChMg+BkhQWTFMidpHgA5fXe+ZpZrvZEKLq10PQYmYXGr7QFVwzVFh0rC5azvThWu/QpK6aSarVTF0cNN16eykI1qDeNznLsayPZottANfkX77p8FUTa0BSbN9Zd7CysNYtG+GY53z0HiyTcQwErJhYCsFvNAsnleoSwwCbsuHy/hVPHlyy3UZevH2LkjWrWEVdY11wvP/BfRA1A9JCf+1PKbT7Vig5M82F4DuTIMB0UPlM6DttL1BWoC1FCL0ndg4fC+PBFteWCW5leE574o3J3PRiXGR2uhGAVDUiKrUgVKvd0iBit9sg1t/O+8DQ99S4GtvUmi3IKAybztgxrSfUmlRHzhM+OLZR2cDYRfpOayClFuac8HEDOVNQyVrfRYKHUmaVJrmgDqaCtRSRZc5oX83WV0jZY+8U3KoTsE6QigbBUitOPN6kSG1aaNDT5lyLYNq88GYwovJX5NkaKzRLygpIkDXwavJo5bMrJ9vIyXDOIRec71QWI0KHztssSr6EMPD5H3mJW7fP+Js/8xY//c/9GD/xko37MFDniTLD9pOf4/EVPHnvIW9+/dtEr5nfkiqhj9w62/LgMjP0jj7CZrslhh7fdXzlrW9xmUZ+8sd/F5/95Kv82lsP6HeOT736EudnF3SbgZwmQvCIeCjK/ldXkJQIvjndOpzrCJ1nHK+RIuAjh5w1myyivdNaNtehsmyTVnrJS51UG9fSGBPn8cv9aOOu69mS6b2BHNQUpmXMwsKXHIEbWmtrA0zrU6/E1jIfPFUCm7MrfvxLr1GGP8mv/Hu/QHXggm7kLgyc7s741HNnnNcP8GgQJ5JAtLbH15bt75EYSdff4tV7X+D3/N4vMefCr7/5TRIjdap0Jmf8yi//ItdPr/mD/+xf4sJ/m37/PZU+uoo6OKuRl/ZafAYsrsO4XGWTmq2/lGfepXLr0ED0M0cKR8OsLROaMYSSB8EcGhtg8+vjZUHXCkz9MSPQ7s3RuS2ZzRvbgCz36thsR0Fuc7u148hRj8d2Tvb8NgdVBKRaKQIqtdOaIK2RC3iG2NlRTAbqBlWEeCWqilTOb19oZkuE6LxmlQVy0YyYKpWdmXpp/Vqr31eSUs1URIzgsssuNseVuNC9zy9k3Crl1ctu+7uOTSOFnQF//Wyw/XP93JzVBVjbWK2GM00pNJmLYhfVLMVhtezBL59x3huJzSLva/ddosP5gJtAKHR9VNlji2VKpn1YpCJG6gUbHy23aeZ0usY7K6oNQffzGFcHSJFWcqLkjHSdKhaiAgsfdM5KbQuM+gnX0lwmsdYilXFe6+nSVEmlcCjqjpuTupJSwbnAVIVxThxmk8TWSpoukTrw6FFiP2Ue7ydwnouzE3pf6FB/CBcj718eOMyVq3Fk10devthy1keev7jQFirBskq10KXMEAOng1d5N55ShcPlSNdpVvh7jx5rBjurTPFkoxLY7aDqsZPTC5r1VNd5nGzpYnNeV1Kl5GputFZXm/Mihy4lMxfhkMsyp9ThNFJLJeWtxoQOxnmtJ21zpdai7VcQpAjj5SVTgeuDSY9BS2NKJaesreF8MOmz2POkSYir6xHnA7MZPSb7s6nXQlDH05oL46zxw3bTqfpNNOaplr33wVu84xFTIqlHiGPoIl0nVrYTyElwbgQn5uZbwAW875jmcXHuby3CYlQF3GazwyGUlIy0dosjbJ7VZTh2Hd5VuqjyU+fV4Zxc7flrPhKFXLNKpYP6VfS9Xs8qNBScr1a3+PHrh/H1A4PFNGfLmgHo5L0RColTowBZYib7uT5EbSNQUqRlyuxovtUzrourHkeYDolpngmho0PrPkRUxunDuhkLYgAWcM04xiQ+FhS04/vmBOqcsUUgTqzWRgFliwicP7ZDd8v5edbN4NnamRYvuKPMgm/5B7s2ZxuMvqdZHB/FnVgm0zUZ1rpRIQXvtOWA1Ep1lVoCZXaQPNvOc3riOO0C4+SMIVUpYQWryensWzI1C12vjqMxeEQCE1ofMPTR2NVCHjVr7LwjoMyoa8x9cAwxEDqHD0LyunE284aWmXROazEG60vlnBnt1GpZR3Cim0cMjlyg61ptYrVWHgXv1CxmGJr1d9tYCzmDi55hE43prdoqpGqz5hidHV8D2812o2DRqdPtNAe6foDcc3241CxkDLgIp6c9cWjZN5W2ar2gLBPLYbWpNo+cBXFtZh3f6EY2qP07C1h0WAqkBa1ipIjNqxvB6jLh7FlrE7FanaIzEHsU7Dqbi9Wku+I0MyTqRgCS9D6bpCegLph5Es63W/rzE55cfYWrKTBstnQuUfqttueoFTm7zeGQGfdapxSDp1bNFg8xLL3lXNDnbtN1arG/O+Gkf5cA3Lpzhzdev804FuIWXvnkPba7U4IbNIhrhJDVIxafydOkC1ApIFpbGHvPNPaUVMFHJIhKxzFzoQZE7NnUiWpuiscAAstmiSxN35cBtWN4juSM7bjOWbbq6FVllVoaWbV8xIBDW2G17q2tSRp0ShyY0sjF7XM+/6PPsd1uISdroaLmDtvthpOhJ4ix0TgNJsVq0xqolYgjUutMV6743Ofe4L0HT3nn7fd5cDWBU5c8J555vOK73/4G3/j2E167FXnRRSgjJTic+GUOLnMT+NT55c1rtznd7E4+DAFv8GMf8Y7f+rW6ddcl2HcuaGZSfovPLKe0LNzrD41gOX52jq+t/eC4prKRMcveJKu5zfF3NIDY9qV17jmr1fNHg2H7g/cG8tfJ00gqLNQ2WYqBcVU7BK9tqoLv1aUbWVQOreeiw4x/nH4+3nCy5kj5IGuyvTkPiVun7pLVrXZZSmA0sC2u7fM6p0QwZY0nl8xQWxslzcA2HwAlh9BAV2SRm5ZsZJ3de1X4cKQkWq9Ba70dXVDVQMXaWvk2E616vt0723ecqYjaPJGWFWtzxFpBlXZPZTVRWlqxqDXmUioTg6f6o2t1UEuhqISGRrRWW6MEizVicxRN9DWw8UHPz2o/g0Pd0rtIyppBbZna6+sRXOTpdeZyP/HgaiKj7aq8FNI8qewwZ54+veZ6nLk8jBy6yCCFtB3o/YYwdGQP1+lAyjPbkjndbJChWK9AJXtzVdVHzsLjq2vmWcEiwH4z03eRTaelPNyJNrUZDSMAACAASURBVPpCl9QARczPwQczI6x52U/wDlna3hRq9mQRdqwxZqmapdb7qI7+VYQp6c9b79c+aF1rm86lCOMexqKZ32LOprlUxjExh0SI2js5zR6qPoPV9rWY1WyqZDXkGbP2Y6wV8jjrw1MV5B4OWgKx3XV00Vz0i/6uOcor0NKWb7CSMmebSDfoHC85mtN+1pYxRcGtD1ruk+ZqirBKmpVw7ytAQdyExzPntY2ZJnGEedZ64a54HEoiRR9M9adNHFsZURf1XOd5Mkd9PQftFVmsNEDXkVYq9vHrh/P1A4NFT1wkpzlVSFrQ67Ms+6pzZemr533btFvmDm7s1q7QmTzDB2fF54EqhZO4scUVa4hb2WyD2kr3mh3qBjV2mc3yGLHePWJBqHdIVebWB3Xra72dXAgcDgdijBTfAnDNjjSmUrP8el6HwwFE64zUiMY2hkOzB9YRKLXVMqrEzwdtyBu9Z14smFmCubQOjJ6DVwcwzXAp69nae6jxijGTtWs0uroP1kC+Fh7VSzabyO/58de4fT7wy29+nyfX1+xOOxDYXxbSXEDUoaoLHZuzM60hkUyqia7riaHjbGtyms2GedaeQH1UEDcfJoLVK1arHfShox+0zYX3Hn8SCS4yJ5U7SOv3B3TbgXvbQRmoVPAeck0468/WbQNdH9nGgbl0CnZFF53oYNhsGAZdyLzTvoFj0l6YKUc2m47zi1OmaTRZbcT1PSWrlErbZBhr78xCuipLK7UqEC6Zzke2sSflSucC3cbz0t1z5qsnfPCusKUxY0KlSXMghu5onmvbE++DWZ/rLIhdZ4EfJkuzt7vVlOGGBeRxaL08S8doQzNp3sGfe+c5zTy2rGPQYvMGWlpQpYy8MB3U5KRQcF1QmXjQALugIMAXTyVR5hl3esb2/A5vfGrLt773Hu+/8aM8d1I4PHxEF7YMW0+dZ/7+P/yn/OIvvUmtI5vNGWnKdE6zHVf7GZczU0m4uKXrAsPG4Zk58/D8cy9y8dLniOeOey/sCWHAuS01CYFJs5Qmd3Ze6zdLTgz9icq2XSaYymGaEsWdIoMGTCUVhq7TIPNoLAWoQfN4obaAw8CAybSDN7t/KeawaaRScMvt0EyV06C4AdCj+wioDbs7Nkpxtsa0fox6/z16HuEowE4+Uk5u8z//zNeI4YxNf8G9W7d5+OARs1NH5Rhg6Bxd1+OyZkcaqM4UXKkEC0ILCQRidwaX3+eP/YGf4BMvvcj1k8f87M/+XYJUigvU4hhIXD94j//gL/xlfvpP/FH+7B//NKfyPpIqnak8ai1Lls7h+Mtf+nm9ntIaD+irHs3hlVrRV7FprTLPI2DdRvFo3WzkS7sf1eTgPqqUqyA0Z86AX4FAi/bdmslbb5Es7oPewM6Hvtfeu5A4rHOmSesrz4BFVIWjMVPLft2cI81MrlGNzbStiGYamlxSnKMzHJRztt51jpTVbTpEbbydasEFHetGJQVTASGY5b6AyeqrHpw0V90r7OLaeDX3w8Uwq2VFj0gCca2BkJV6eLcQLG2/1tpADf6D12OWmq3mzUgcu1fB7k8DXs4ANWKZuSOw7G9kMvV+tD5yqwuusAkDXfRLGyUrcScs+ll1LdXAHIq5eDbDuVKt7Yx3VK+lFq01i8OvOLr9Rxliren1XqWjaHZJfQHCUnLigvavjMvaJDi6Ze3WuMFqg73WMWrdr4GeqlkzodOvtfENBq42vSp0QufN0K8SRCWGqWqLjKtcmIHruVCzth6rpTA/vuYwJ673iXeeXDE93XMIkToWpg5icGy22kf79HRnGTLhhRduq5IrV9I8mjLMk+ZCqZlpf3Vjfei7qC7t3i2+E6E1lre5oHX3GlfEGAkOuqXsCDSr1kpQzN3UgStF65TBSIdTA//GznhHydlKqnT9b6qFeU6m6sHIAK+lELky50QWbbUyp8yDR08Br26u00yu1uopZdKke/bl9YFpTox5tvUuWKlSNak0OK8qqDQnvAvmrApVPOMo1sNc3U9r9eDUJwEnSM5wmNrqYnPY41xknNW7YZ61DKfoQRuW1TrpXIxYvqYZgikRtrrbt8ne3OoBk7EqAaL9tA0XGKEaj9rTffz64Xv9wGBxs9U6I7fGRAt4qkUYx4lcMrAxuZ0G4nEbTWpiTKmxvuKC1kM4T0l5yUAtLTdctFrDkWHTs91tCV43l5Qmimims9Q1QziPrRA3E0Ik+kCqWZ3aZDVRCKEyzRnvdXHX3kTKooaW8j9KNqSScKie3TfHq1qhqnPa0iQaMRZIluuoRWuEdFPTvoWgVug5zct3C2IW7tV6ArmlsX01CYK3Or1aExAoJTOOlSqeHo/PM7HruDjd8eLLd5m//A4ex3g1aq/Hrmd3uyfQU5JJ27xQ+x3TpIuh1tYooyUOxnE26YUwFw3ILke1W249rISKS2op3Ud1x6ul0PUWONXKnLVGoNZKmAPj5X7Z9PuNgquSZkIIxN1AKTCXivORaUo6ZrHn/oNH7HYbQtA6yz5GtpstD57sVXo69AxDx+GQedxapjgPeGLUFhnjYVSJqwXLl1d7QgP9ohLG5IXBpE1VPLgJnwr/9OGb/Nhrz3HmXuX0ExekKSnYLYWck35XxDT+uoiG4Bm2g97HaI2k24rqzGimBW5tE0KeSagc/cOyRB4Lcp32l2rv+6nHF8pUezV3okmzzOkvWDAoTt0nc52Y5gPjdCBlz5gKcXOOj44uqrNxT2Cqmf3VgXAoDKXyxiv3eOu7l1yVibMqPHz3e/TbE1585fNMk+OdB9d8cD3h+0hHTwJ87wmDJ12ZlXZnZgQhqrueZB4eCnfGa9J8Sc6n5pgsHPYjbAbCRjOLJEfsewqFOR3worby45SgOvo+MHQVfGbYbHExqjlAFwhpgmTZHbP1b6Sna73BLMpb+vyxBhNVKks/M8M8pYFzA6mtdmrtlyn8pZ/8eT1GLZoFcWu1XDVwudb6iWWt1+/XDHRkLLf4W//TP+TqyUNcnbh8fN/a36hUKc2J66cj8zxrc3kXbA1zzN6AYvWWLdJ/Zxko9Skh3edik/nM5z6N/zudzRWrL68dWw+//qtf4Vc+/Wn++T/0BW7d+QTy+D3wmsWjOHW8/NBLPuJnWBal1Yo/i62PCJFl/J/5uA2Pbwv2sbGQHbAF+VofsLLbuCW/upgXLd9XlWBZksIiHz6/Z36wBJvtSwVWcFOX91gezoK7Vo/X1h9tV7Ia+ujzWkSQUpbm4C1Y9T5aNqIgVc3o1UANxHpINHdmt7CpzmrAFVToAQ0cuza/dd1s2bG6gEUjQ2y+O9+AkYX6LaPXQHRgzcSL6DkcjZ/uvU1Sq7NfLc28kcaFYq2RxPZR7DJowaoduwWhVcx13XrEOlrtm97/Ko5SEs7qNouiZpC1TlnbQegYNDJOAXKx9lACXhYTN+2XacDE1oEmJleworXRUr0CAls4atNBBzUWAgXUgsqsZRlvWeZTFSFbyw+HWIWNziMxqToto+q05UFtTdUFLq+f2FF1zkmdtUbWOQqq1nECEuHEG7gPAdf1bF/aLffii2+8zJQy+1TMTVOzVldXB+ZUmQ7CnDNzmnl8tadkYQg9OSe6Xc/QRx2JWuiGHZ1lo4JXsOFrQaQwFa2/nXIim4mgs2tLJVFLNrM6jeOikbje+laqz4UZRgXPoN3f8EFr82spVvZi63aFcZzsGTbzsapDKjZPW7YzBGEIXtvRbDotOwlKQtw97ZHqmOfCPCfL8jrmVHj0+Jo5C+enA1fXIx88fqxrgY8ktCZYE9jOHLgDzun+0DL+1SvNFiTQFUFqxqExuZoC6b0tVVuddRZ7ePQcnT3v/dCjxpXZFIKOaczMU2Ky8rBsEum5ZIt1VU6dzDhRCZW2xmk8qc92XDjrYN4eStp/nFX8YX65j9ps/7+8/sN/91+WxjJ2XaQza9wuat+lGLUOJsva4FwwWUUpbLcbPQGTkzSZSEoKBJpUpG2q3pgwRGUysQurYY0I2urR40xm5M21KgaVaHjvFllqyUX76Pmb8hTQY2B9dBoDKUuTUcxowDdFyMJ8iVSGbruwlK22sAWQ3hYosSCm1ta3p7Er2ENkDWldUHt9Y8jB4EALMgxo1yoEcfZZlRU4j4IaHIdD5stf+xbfefv7PBqBKng5IEUY+lO6fsvXf/03eHI9M6aqtYTOcz4EiqvMc0SysN3AyfmGy6sZfME50WC+VLJMWutVtwqm5yu63nEYJ7qhp+s75smkD0mdsM5PTzk73dGHzOXTa2oflCEjcv/hU+4+f46PcLhOPL5/IJUJ31XOT7bE0JFSYZoSwyaw3W45PdnRec/JNhKD5+GTRBoT2177hucys0+OOelm5ah4SUS3BcSyGsq4qtuuZsV3uy0hBsqUSBLpfCY4TxbPfkqc73r+2O/7ST790j1ubfdcT5ndyQmm3NKaoZwJURdIXTyF5m4ZQiCEQN/3i2Pu+nS2edGyTbIGy+5IDm3vaVmMZqQATgMdA71LVkxUmqQtJDBTIlms6N955x0ePnjA40ePuHV2i5QzV/u99rmcJ1JKiARc3FKnK0pxVOl58S78g199m8/+xO/js598nrvxkhIdl+Or/MzP/h1+4Rd+gSKewoaHj58yjzN3T+9y79YJPjzm//71R9SkrsK+Rnb9ludeuOCP/KHfwSduR/r8iMsnlzw9FDZDx2c/9TJ375xx6/YG0AzEdDVavVDkakyM+ytEKqnM9MNAt9nx9PJaTROS9pva7AZOL074id/7JUo+UOvMX33hPcDxb7z/gj55wtJ6ROV7gVYb7VxjlC1IdG5xxbzxWrIta4bkQ+0e2lsb8+00w1FwlBjpKjjpwFcSEznNlPGaen6bp/4Vvv6Vt/gnv/R1/uP/8m+w7T1dm2v9wPb0gv/lf/jPuSNvMZQ9znUqR3ST1jBiPdv8DC5SsuAlEF3humx4b7rHT/+pP880XdF7nUfZO0JwnMbI1ez4t/6df5t/8U/8AZ4bvk+fHxMKdCqDaNH8mnFqryMAWGyxC8eYsAFve2/LrdR8s5l9M5Ba6hGPvqLVMYFbCL4PPzMsTqTqyi03jn/8DLVraIDv+D46py2iGnl6XJ+HwDxnSi0MfX8jW92+R7TIzI6//m41t9EhU9v7Vd7Y3tuM0tb3HoHMo3MstRi5d3OeeoN1C8ARaem544PeOPYy/0tdgvZj2SVezVhabR6o1F2JVX0mpNalB+wi3G73p9Vb2z0rbvU6WL/LLd8NLK7Xuoc6A4htjPyaNbVzcc6Rp6QkgV/vuY6TSXClgcCVTFzHiuXvTVTd9nydq7KQgu1cBFnW/AYIPxySrfFHA+sfesfRz1oMoj+zrLNd6zIXjRwRYa2VPWZHnD4ziweBsNT5t+tbTqm29mdKeqv3gMY9DiUmQoy0QKYBm2yk0Dhqb8tihGpK2dpk5aUso6myck4LyNc2GuqXUGslhEgIkWmayDnTxR4cZJsbh8OE4Li8vqZkPW7oO3UrL8XuhSqLPLAbBvrB3Hml0vc90dRlJjxWsoJ2PWoYI96Z0/x6P2rlxpzB+ueKb+PucLT9X2PQks0FPmWNIbpeAfesgO0wJzKew5S43B+YSuHJ08w4jaRp5jAlShVSNtKiERW1IKUwzYnQdVQH45TIxXwsnJXSiLr7zjlRkiryggHiWiubftDMcqk6220tolPyRUTrG+eS9V4b6AaNu6NblexOVBqPc1zno17iH7/+f3lJevTRgcf/y+sHziw+fnpJ9Dq5u1xUWoc6hIXkrVFvYJ5Hur5fLL/1P4FaLNjCwFGoR8GygsVFwmmuU23TUECpLFHnW61dq1cyli0nYtTeezmrNbFIY3SFeUq2cawylgbUNAA0bbwTzbi0wkvnFhOcdXPXgOzQJKzLHbEsloMm6dGaL+1bJ5ZdcG01xgIHafb4mpmS2qSwq+WCs3ESjF2O0TaBxvRibluJbV/55CfO2ewTNQtlUnY848l15PbzG27LKTWj0ksfOQ/qBDkVkwa7TNd7njvbqYTNQwdMKUN3wTzPFFHJhGOn/ZLmrIAcKCWoQRaWNfbeWpdETs/PoU640OFDx/7Q40rGidB7x8WtHVIHUp7oY0/fBzZ94GzX0XVbus7TdyrvTLkyTYVCwQ8wV0EmISdHMgmFc9pHsRZHkrbBV5MVOXp6uz+VcVQ79v0+k8qB6JXJG+fMfs7I3VM2p4HTWxEvPSfdhi5GC/RR0xsflvqarsPm3BoEqnymyVvWgHDJQBw/eEdB6WqWsUy3o1f7ueN/v3gMIvzU4wtA2Tz7svU4YFlt4XR3ggdOdyf03WCBpQLK1BoUV8tSjrMW7Ivg+shvO7nH+x/MXH/wLs9dRFIf+JV//L8yPbrPFz/1SZ5cF956/4qStfXM5bRnWzf84Z/6o7zwhcJvvPlVcMKL957nhefv8urrt3ntFlw9fcr77zm2u1PObw/kmplLYZor0wSpCsEL0atR05TUjTEOvZpgzSMhBDof2PaDSsT7HgEOaaQehFJmkIoX+De//8LNLNMy/rI8d22cj0WFDrSGTI4/s96S5S83Avib67bDsgGiINEHzQp4AXxBzFSJriPsTogXd5HtBa5/CYa3eXj9cDl2FQ3+nQhpTqRakRgtR4FmQsSpGy8V7yyT7bKqOZwwz3tcH7hz54Qv/Pbfzre+/S2ePL5P5z2lJASnLVoRvvu9+/zqN9/jd7wRuVcKvnjVwLq1PcSf/nt/BIC/8Yd/bh1D6y1qKZcbQ3VzcBRUCKKue8eT3gIxdfb1S+arZavEr89cex+sgXZ71pYSsWdIlo/697HMVIPjo3rGj7gE5z2xC7hyc9/5rV5u3TRvvNf71X2x/c7fmHCrW6iqPFUOiYExm6msvgMsY7HIaNsBPuocTf7XAv9GfjWjHD2uWJa0AQcrx3CrJNV5QcxOWBZ1xYdrOo8dytug3vAIMNCzAnP7v2sAUqXdrT6yHpEO7Qk8XodbQK/7fWtV4+zcjvZ9GlBmBdZH57nI0hdAsHoNLAZoho7XcV9PYb2W9b6ux715327en5WQWuaP6BnIAmq5CXbrzbVtmQl2Cgs5tqBiWqCBoCYyxTKuea4rOAIYJ43RjKxR0k3PM5eb+5ADSzB0i0oLPOKrKbEaIFbQH5ozp8Vs4Oi7qllkhM4y531Q0up01y3AOYRIrerT0K5RUODe1FR6/4JhHat9diyydNqzZY7+WlGwGjkqQdUIBdHaygY3q1dyU/TYGmvmBfAjICYhFckEL2x62PSBk62uV1UCufYKtKejODmrEVQ5qp9VZZf2l9ZYSOd7SkUdiK0HaiOhctIWGZfXB5v/fpGHb/reYt91g/NAv+kpWcuAqpFuWqPsTf1QLR4vzCmbGVFZ1FBf+c37fPz64Xv9wGDxen9gMwwqsxRlG7x3RLG6sVLpojCPCSFQjVxugFBJP5vYXhuQi2CMfZt8qwy1Wh1BXvTSQt932heQJm/URahkdadSuakGwbVWimusa9G6hNTAohbvSq3MkxYXx6B9axQV2sNj2u2WVVgXw2o1Jdn2kxZU2IbrzKpfhNB1mjVcFnPr4ycrU+pNV19LWgKEtsiWKoTg1k3eqzNt7OLK+gk4c5zKORN95fbFhhI9ea4cnC7bh5qYp5mLWxu2/RYvjulQqCFw7hQsjjh72DMeYecGst4aXC2McyFuNxwmLWz3PhDDBo/2sypWHO/9oMGMnxEg10KuhVS04S6Tum65aFLbXBDJeDp2wwahcjgIoGMXoid6Txd6W3yKOpZlSJNQfSF0avmc5oqSVbpAdl3AuUghL2xglUJOVrPWZBkF1etL5em1khRdDFpjdzgwpsLZyYbtaWR33lOvCn2nkp3aessBsoD/lYTQANZxnBk4Zun1va0GY5Wo6hueeRg11tC6sOOsjQWw//UL7yGwgMXjKLK0gznd7KpAP/TEGDjdnVIFhr5fzHqanLbUTK4zcdL6xklmHqbIa6/e5et/+6s8efcxt+6eMXeRf/B//Tw//iN3+PQrLxLdyG+8e6VuudFzNY/sa+WLP/F7eD1t2foDeM9v+8zrvPbai3zm87fpHvwG3/zGzP37kc3Qc/fOGU8Pe91Ivae6TiVY4tkOg27itdI5TzcEhu2GaeqhqglSCJE5Z/wwIN6TLrWupNSENgRag76wRJI6Ri2M0uGz57XFDKKhljvKeKw3Tj503wT4r772eXDw53/0V2/c4hWLeqplwWq1BuaMiEQKJ5RuB9sLKlv2s/Do8or3HnywEFBSTdZcocyVXEV7i1pdbHXVrlfXMecUXBZzvywOcplwfsvZvQ2vf+YNHlxe8ejJY3x0uKpgUfM+le9+7z1+9Zvf442XP8ULXqBUNZ0gf0RC8XhAZAHYVeQmQfLsS8QCynAUEK8tFEQEH/0CKBxurfG24E+OnrtjZYmDpRXKzVNrmWN3gx5YwCLLodeM19EBlnDfmSTUAM6HgaLNkwWUHGX5js4RVuOWG2oE2jXfHK/jw69/HAXzKw5ZZLbtehxYrffNcRJYnCPLUqtogNS75X1NEdSG3y1XYPm3NhYO2p1flroGthroO76OhmSOH7clgL95PtLea9ewEAXLmCtwa8Y1KxhaFU03x98ydfa/xTTnQ0SBLCDt2dNvY/FsRvHmEi7LfTj+2Uf9/UMHOAJ47RCugeY2n4/2nFZCIs8cu53f0jv0+BrEaVeeVq9mIKxlrVuWP5eMVO1D3Y4jVie7Vkysc0ZlkYHqbJQriDQC34jVRlCImtS0M+v62HCWzQMrb+la7LABlCwK3lGkIEnfV0WzjBrzKDkqKIlQbCyq3av2/C1zS6pmWUWgVZfa+iLOPrPMOyO1qvpMCCr5VIIyW12sXx/CYAo7Vwmd0zjEHId9aI6zjg6/xK21naspO4rkRY4NZgBYQbOYVXu1hqh9si2LnIu2hXvw+FJbTlUQ58g1MwyWLZaWLVefkNPdljTN6gpr91jrkbV1GVKtpjJzPY3MKZNSZkqZKh+DxR/W1w8MFl984c6SXYuxtVMoC8sWLLi8uH2Gd57c6vwCgDBNe1YpXiTUwDyaO9NRsKUOnB21atYljZkqBW+Zy9a4PTpP7DvTPVecKzxNVwvYdM7ZBFU2pT3kzq1ypFaQLlVZeEHbgjQTm2qMiAYNKwu27ElWn9jGZXV6sqACyKJ9qkLblJz+fd20ra2Hq4vdtrYAUdAwNIdQO2aIgRA2BN9RSqXmoku+q4jz7K8r33znMU8uR6YxMKfRZAnQDWoSNB4qj/NjNKIK5DLzNHZUHE8Po8kVBs5OTkhP32F/yOxTZp/VsVSy6vGv9tdMU2LOKpM9OduS55maCpuTM5x37A8H0pw42Z1wfnaKlJHDfk+NoUUlDGHHMET2+wPz+IQ0JwoFwkCyhrQOlcXkNOJDJMYN3kd8DHRd4KTPdH2nzFpRBrxzQTPFjExpYpw0A11qous6okld9nvt2xiDsn4K9mC33bDrd/ggbE8HchV+9LOf4d7F83Ru4MCsduVNbuP9Mq9a/6BgLqBNttM217aptDndiBLNHtQbAS08ExC2DMUzmY6FTVw2Nwv4jnobrQe0uiXnuLq81DHPmaEbGA97Lq9Vhtr16ig7zROlZs7COTOVq5p4en2fE9fzzve+w5tvfoP9ow9IB+Fzn/sU+xT57/6Pr/Lgas9UCrf6AtLThY6BwPzwEU/37xBOT3Fxw8OUGN/9Pt98+oSXzztuXbzEFz4Xub4+sOs2RFf45Ks/wsWdC7bnW23ILQoGxTkolZgLXey0NqvztiFbvS9QxpkqEDef1f3YAkxxjt/cTEitvHqtNdYhHhsNHYEZuRFesTycHKu6DFyyAptWlvT3334ZAf7859+8EbytQLSjSqZSQCK+ZMrp8zydz/mVb8y8/f0HfOu7v8jT99/j7/1vP8dhnCnVsYvBpD1JG83nAHTkyZGiZ1uzklJECioNbHkqTyS6AFkUEHYRXIHxAfFsQxh6XUs9+Ooo1SGhEn3l1772C1Ae8pO/88/w+gsgKVGcgvAlqFqm7bOoTF/hmUzWKr/WELuBo9Lm8VGQfKMeUOw7vF+zcHYODeQsizcsALI5EbdzVJDjlr3NLdk0WZ7DJi9c+vR6v5ALbT/7KHDawJj+3dYC5RcVeBox2t60zJElyF+DViVxis291SSrtZCoFgAfZ8ZKzeuJPPO31uqiSYBbza0CDj14PpImLteNEpjLHGbNnuhFaWDZXE0bsdpIGTmaI/p9KwnaAv/otb5WLJuDc5ScTMFhAM6yTEtLISCY2VjJWQ3r7CGVLNaC4vimrPNigXfu6Ow+BNp0cFy71wsUbaBLwXAb/2MVib1xBXc2R5p50bGk/cPZ6PXZaOe7rlU331uKyR6P4hJYy2VuypLdMh+PiYU2HN4AfhWHC/HI4Kv1T2Yh0GLw+HhMfBxdT/tcIytsXLLk5eqaMmccp/U5beRdI2DsC5ORvkrKqImd9hutixdFKQqORDCFk41DtJo/I2sCEaFSk6rncJFqdeDN4IgG+EVrwT2wnw8We2r8WIosIDTXarWIHsekM0RWqXLoOo33nFK53qmbrbbB0fVmnjRrp89wXYBzpcne6zJvvW/PWLW1LCAE+zyNstHxLAdaGVYthWLOpv0Qma6u2R9Grg/afmOa9DpyzpSSyVIY+o6L7YY0T8w5aau6EKiTttpr5Wl9F7U0ywvb4Nl1PSm5Dz8TH79+aF4/MFi8e+eE1tsuLCxuNRvo1SEpGJ3YVXV7DN6KXaxXXuNkY/QUa2bf5KQ4RymOYeiXzcKfGYDqVleqBuBi163BcjX76CaD8Y6ONVO5SEdwNxbfYCLqYhnMaC0UdJHTJaX1dwKOsj/gYndU5L8urmvwoGPkBGLXajJZGKLG3sag505VzbqP0WQsCsKbDEsXMwAhxl776GTNaBXnuBpnnjy95q1vf8DlPvPkUJA6+2t/hwAAIABJREFUcfv8tgKJ/UStcHV9YBgC3ilDHPrAtz94xDwVSCBFSOLp+yfcuog8vB65uh4JxXFy0muhdhKmuWqx9pTJuRADnJ1u2d7uefJEGaRhc0JOlceXTxnThFCoIuz3id7DEB3bbSCNwvUoTJPKKZx35LlASSoxURqZLkbu3L7Nq6+8zG4zqAV68JzsBq6vDzx8/JiUM05g0w10ncNHzVIfDsL+cEBkY3Ww2oMxdJGTYWPmQTMlZeKwodSMJJVdnMYT+s3Ay8/fYQgZSXuQEec1GBFZi7sb2e6OGG6VrRiTe8TuPrtY6jw9lnosv2GBiy1Q+yhAecR8/pnPf3P53X/z1desoN3x77/2Xb69mSxGcot7nojwU09u8a995zahi7y1nfmPfvQBOExGIjg3KRtbK//S303c+vK3+M633+e7f+oe8x/8LFLhA2+Zg/wcIhDeuuLkL77JPAp3nz/nW//F5/gLuy9z/+k14+9qphL6XL30M095/Ws9v/MLn+T8p2/zt9+YEJmpEhm6+/jwEM2IVv6zX3iezgXwgb/yYw/47m42aRrLcyjA77+/48+9fZeaZt4aJv7K62+BY3FIPIre+eu/9pnlft4IrH7L11HQ/Ww2wK33a/2lW4if5RxlrWUsseJqJVRBmoHX+cu89ZV3+e//5s/yzd98i6vxKWWakSKcnu3oNpGHHzyhZKE4XYtymUlFuP/kmufOenLJuFKJ9BrckgloY2jNUwvZ9QSXCTVAifipcOd0w27o8GgN52imkV0jz2rFVcG5zkx6jDRpjPZy2fZQPAMgW8C8EIY3gIO/OfYWdN5s2tR+bi8fFjOXo4fhxssbOKg37skxZHF2YjclqIuh0fI2JXsWwNjmi18zBMf8wfp8Hp9/vTE/nENdxo+C4SV76o/qHVFi01U7BJBzWvc7H1aTF+fQUgi/SFeP8OfCgDZQrLhsBWHQjuHX7I3dy4L1GXY3c2kLCEOO/u4WALpcvQXADQys9XwNUKzj347TplDLYKy3WG7cblnAjP1Z5ebUE81g6fGPwRnQ6m6tBUv7hRPM6XUdl/WA63MtouhKKov8tB7t4zrQsoK7I1JBbPodv26SBuv5LGSUESTOyJW2tzjrx7vIho+uHeRD33NT8bIa1B2dyUICiCjAcK45v7dxdKrcOTJ0auC2XdqiiHJH66xvhmOreVMIQZ2523m1liR1JVSbI7vLyWqT9ea38osQHH3Xm+lM1nmRtf9fLVXno3fqYoqSMLmIrTRlieVSVgfsWVaw1W68N/JbSqWURB86hsWBF/0uqdQQ1OFUHNINWqs5pcbbAyrtHUsihEjfRRyaDVXSJBCCPqtK4qikc6lP5Og5qS3ru7aucSyWS3in/vM+KGgO4ohZy09C9GxunSEXZ+raW8tCPAloW5CiJpNeWp/LylSy3v+ka78KEJqjstMepuKQAsFF+u5jk5sf1tcPDBZrEmUMsiBWzI+DeW5Olc0ptDGfujBo6rsum0DbLNQaG6RKU0gtC2YraNZ+L8pMtEUp+GANSLV5vEpJtLl7s+heM4nrBtQYRf2adeFq4G3oB7UDl1Ueui78ZWW1bMN3ZvDjHEqV2cKrjlWoLr7p4GsljWkJznH6Ph8USKcyr0yPCNTU/GzUQdI2KQDxkVpUVlUpqm93jpwL4/6ai5OB3/+7P08RONue4H3m6nLPmAvf+f4D3rv/hHFfl8VTZiGK52S7pY+Z8yGw6Xum7LncH7hzfgFs2Q6V7aan6yvjOHF9SHA9IsDzL7/A6W7L6dZTa6KWxG674bAfee72c+y2n0TqxDge6IbIZjOwG7YkKcw1cRjBpcy/8Jkvcna65dHlQ95/eB/cHQ5F7aa9V+nww/sPCV6PEboAVaXP0am5y9nJBu8cp2c7DtOBkiu73QXb7Q7qxLbb0gV1FRu2A32nEg7JCR8dQ68S6Mvra3a7DVWu8D5wenGX7WbDJ25v2SLUvTYgb66/y8uCynYvtZmvaDH3+qYbGZGbGQx/FDi3Q65MqM2C5XNwc8Nt89vCNPs2/VlOefl9Y3alscLGaIfg2WwGtidbbm0nuu6JBjgmpRMaEoYvfelFPrX9Ak9OXuS/PfsqX0/XeAebGCkVZtwCLL93rY53YT8zTolxSqRpJjoFRd5BEMcwXfHmV94mDBf8vuELnGxnRBJEBTYt5ATh3ssvgdV79P1TYqhrFqg9lgjbbmC32cBu4GTbE7onGtitw2kDrWRXtfH1HwE0qq0BuEUsfAQJZPnDQasS1MzR0b0DkFxuyB+bVMuNWp9dHFQZOezvw8HzjV//Bl/51f+TR1eX+HFDnTNdF6jTxFQO+Fw0m0TQLKavVGa+/mu/wUv3PsvdfoOrhdBF010WqAu/rj+LGhD08Qypnnr/ff7sv/onmabC1375n5BTIjghu0rXe+aDQ+Jt2NzDEanzrE3FvQJQbcZ+BJhN3rZmTj48vsuEts8c19fOc9LSh7gCNM3MqzN0KZWatDQgzWkBQbjmJNiOqw9FM2JrMlHXngkLSH1QCXo96pmXs5praEbPDsRq4gGrwqDF8tJq5X37/U2w2/aq4yC67XctmHVOSZ1Ghuj1W01X0M9p83P9+zSNLL3Zgl/6hS4JV5Fl3dH1SP/UJvFa/xkNcOaS7f3FavDV0KeihnALwBPNujtAnJHEHF1Xy2bZwDg80YcbpFnL6LZxaQqbOWe87a04DWjbJrmucwJS6GKkhriMvSCEGG+st1ol4Al9WGqopOYjIlk4gjZrLMBNwNaWaqEZ8x3FOe16muy0gVl3E9QeA8ybUsebr2Mgpzi2Lp9XyaL6DaSUFnCv584yTxfw7Y/JgPW7xY7X9oxGaB+fo7Q2YM6IF4Gry2ukVrq+PzIwtCekPQc2x9s9qCI3MupSldRtGbr2LK0EjRgoret88Z5hGOiqLHVwc9VnVtcLdd2episO44GT3enieuobsVQSXd8z9FozqZk7MyYU0fXUoYt/COR5wlv5iYjgfNC2vmLJjKqqH1UxFY1ZvSeXyrSfKUXn49CpH0a/6dD2G7MaOOqlIlLZH0ZSKqRcKCWzPTlhTsXWAr2/uahqpLf6Ti9oCzu717mqZN85Bc7ByhzmlBGHmQDOJtM1WWvS0iGq0IcITtuS4LAWcZXoDYBT1BhNhK0M+KBOv2r2V6hVGPpez9l7Ss6klPD+qMXYx68futcPDBaF5kSn/9Ji3RZx6cPpRfvNNBZUWwLoh9qCCaszWXvVFrS0Yy/B27HGnmUB0sXMrz88Yuf0C9dFt8lR61Fkti5kbgkoa00fApTOwN1RTkcXGUWD7YRvhPHVDF5WFjBYHdzNDUd8xdeb37UYAZhrmPesMro2JkHHZ7ZMqGubgQjbTU/fd2zt1pwMO6okUp6I4rl375zYaQsQDf49rjoClR1bUqmcDmo1PWfHxcUJt09O2W5GDlNm6ANdJzhOSbny6PIp85zYbE/YbbYMnXAYHePkiKdo4/Og49d3A32M9DEQ+0AXBlxN2ldLCiF6DnPGHSZycWz6EwIDfR/x0dP699we7tHFQNfpz5zAPE7kpOM7RHWJHXxmOOvJ2eohRNuBlKotUxzCOGXm2dN3HV0AtevxdJ0n9wOeSHC9khl5Js8CsjHDOVks/I6ZbUTjZedW1vY4ANL5dzM4OH7KloCgEQvLrLsZQBzj0+OXiPBXv/wqq+T1wzVOf/EbLy7zVgQFEC14BKuLLbxy3fHX33wd2+H1mfERvLX+qCOEp/z4519m+1bH3/1Pf57vvvUbXD18xFjhcq8udalWivc4ER5fTez+9C8TJJMPE6fOUbc9d++ecdZ7QoVHtdJ1nj/45Dn+9a8GDukKPwxU31l2RJ/xmUywzP1f+sZLUKFaQf6SiW/ZAe1lwWvThr/2a29ArURzDlwG1nttyWOjs/bIa0Hj0f12mmFbVoCbb2k3fnlHCzDbV2mWYf11NRBencnsKgiB4DqVWxPIs2OaYOsE36vUqcyFMqm8SiwwLd5ZrlB4+7vf4d23L3j5ExPRVyoHpHpCs8Nf5lKEPCPiyC5ATTAeOHs+8iOvvMjZnTs8uv8eG6ud8agk8LU3XuV3/M7Pc2sXFge8QMGVbKBxveZsgVADD89SIDqv3TIXV0BjGa9GwBjiafLVIsUMTJpuxRmwsLvjeGZtX7+r1c8twPQZcHhT/rd+LoajmnFuPmPr880SFIMaC2HM/43aQXf8pAv8P+y9Xcx123Xf9RtzzrXW/nie9/t8+dg+tuM6dtyvJK2bhIomtFCqtAgKEqWkFG64QUhcVQKJRqJcIlVAW27agioVBBQk2oJIVajbhqhJ0+LYSZzGcY59zvH5Pu/7Pl9777XWnHNwMeacaz3vsYRkVOQLr4tz3ud59l577fkx5hj/8R//UdvusNBMBWv1UEesOtzz7EowtwSpqtbTTVVxKdFa89SPWynDSgFqKECVuKVWP4p5wDnn1iIqFupmDSC7vr+VyVpqyVzbN+s67bXta/Xc5edq/8pTlnmq/863xmyp7WcFHLjybLlt6yWDtQR+qplpKn1TfWhzLjWYLR+y+BRt5bQ5dqt5KFFT+8ZtHmvWsvWkXOz2twsIb9XSCh9ae/X5bl92ztRAERa11RYkru5Vbf6HNl55XROCr+MlsgjztHu49rn18t6RpSyj6um1l2ibi1vsq/IcDcMp5QIWgBWfMZuvWcFNV/j87exMVbTFxt7aSJrS6raz9ms5K6I9rmgJiAqnOTKTcGKUSqNh5kJJrxTPDCrMyT4vFVGdWRMSi81RrA1aKnYbKc9eWnGo9dTVaAHunE1FlWniMFnvwyrYlKINiHOWXRQq3mKnmYrjNEYTnYuW8ex8sCwsmTEvWXCXnIEGGKsgqyUwKiVZs+k5hNAVgMHqG12wUieHY54nNKciemd7PMZEUnPYQ1Hrj1JYJKr0ZU8nb+CbJsumT3Nq49ra1fn84U3wveu75vqOg0Xjn5u1q/WJKeaS8TGDnsmt8aYdXPZeKZmIap/qwW1Bom2uJpGdS/+ygizlZH37pCA9Ug7E0C3KqdVIVlSsGkvnHb4gsmue9vp56jMZYmyNYKsxtXO8ODXtfeVd2bjd7WirGY2CeNXv78SVvgXLAWfG0C11NFSDufxcBYS0cOkLiFTYMQmyGT6rQ1IQh2rgdH3k6U0C3/HB8dp6Bp5qZm5gu1Fe+dgewfrndUGKEmKPD57dpkNQ5jiTcByuZh7c60k5EUqMnDVwcXVFTAPz0CMqBGYenJ/x/pw4zDOuF3Y7yyAOg2M/WN/LpHA4Hbg+ncpp7phvItcp8kun11sACPDOW98ii9CFQHAQJPFbPvUiYb+h703BNHSOfb8x3r94xmkiayJ0nqHfMk2R66tr5ngkzplpmrhz95zgnLUhSIk4BTwmTd/fPcP3PT0TKSoSBkiZ48WRg5640+2J9zISrBC89tUqs9jQVKgCAGVt6jLPqlr659VDvIIIKwdBaVTk5T71U8qLVxHKh50sDAwQmvNXM5TNMSt3i3mhoQbnoRT9i4g1QS6gBFTajrWtkGkkn17n8w/P+dzzH+UHP/vHefXtG/7sf/nnef31dzjFqcVLrji/T68PPL05EWRGRXmae8II0Z247jsGr/zzf/D387u+8HnubC95evEO4c5dHEI4WiY7bzzaGf1FKOqbuwFxjnw4mqR3CShScziE4EJhHhQGQ6q9Dsv4xYyjOExIU1Jsk1OGWytAU+fsQ55XCVlcRehpIjh1fuMqCFjHmYniEDkMgWZgMz/mt33u4/yBP/Qv87VXX+fXf/kXOR1G9vsdcxxhPpCTM0pRttpp7zMhw5OLJ7z73jvMD3rwSsoHnASyBDxq/RNFUWa65EgyW9+3nCAdcU+/xo/9zk/wsz/2Bf7e3/9ZDhfXDAjXV4n7Dx/yR//wj/LP/tjnuRvfZ5qt4bWfbqy9j6s958x5n2P8UDxdaaZag+cC+NUxMdpfVd60dzc6lBW2Lw7Hao5qDdUSxGk7J1pfvKyWCaCqEa432ErEpTznrfMM25+5BiQru1WfsWWUytxbSZHWRn5t/hdQtDiLQqOQVnGNWltfgSS7XybPM3OcP+RzNeZgTKgudF47a7WxaGCpgXQl0+prMFZphCx1uinmNk6qMI/xdgDTgsLUzss1nfRWgC01GJYGbuVUug+WbMg8WdmJ61zzNVRZaIjSlgxdCHgR5tNIrTps49KCDZvHOWbmKbZ7eufoe28U3JWz0qyzAjVIWq0B56QBFOveLRUMsvEuJTSu9n4stFS0ZdvXAEYdpzomS5hP8YsqIL+IOpW4jkqXXi+H3Ox3XXEWqJsaZ/Fp1HpbaqlzrQFZDdyo+63tn9vzutkMt8AVyyRra8sibZ7MbzGK6TLG9XtUwEak9qsuNOfy+wYCerc8qxZ77OxMcE5IYtRTVWVKiU0fCHf3lk1TFoXv9h2XAHbxJ21mY1rEYayli/mUOc6gSswzx6SkZNRMVuJPwdXMuyPnTHCO/WYoAnI2h+N0xOEIO6OcGmPO9sKcTG02Z2MFHKZToxXnaHX4YHXGqfi5PnhC11HbY206oReIWjLxCikK0tlaEsHq5JNyGE3Yb7/bEeeZlCJHibYGxLK0zptfkEsCyYfOerxqJoqCRiIZwbW9GitQIBZwzjHhwrdvC/O967vj+o6DRaTwyQtt1IkHad3qyuFi5+AaZa1nm3hak91yQ8vwSDXgRRyk1DC0jVs+2wCRGoBqe/2a7lOv+vm+HALNLZYl6GsUVVnS9apqC7+etO2U0GbkDO0RKOn6ipa0YLE4qS2rWtzB9njFjiyZ14I6Ko2uVnt/udrjR1fPTS7UU08oqB85ExWOp5mnV0c+uIi4fseVTjj11NY+N8fIzWHi3tmWPjiG4Nj0kMQhGuiGjrP9hoBac/aYuXhyjaD0nTCEjtMYOZ0yHzw98O7FjIrH6cx2E3n44A4xKdOUCCq4IGzv9ux3HfutBS3HGfKoiI94F/Bu4HgIzOMNSWdUTI11miJvvP/YULjkCaIEp+zPNtw53/Dwzp0S7Cq7bcfDe2eIdEg/oAK7YYNTh3cncppJWRjHSOc8D+9s8V1A08ZUWLNyOp3onLDpOvo+wBQhBCR0iEKcrAlzSqYw6TG1r1s5O0MNlrnKeYWoFgf3Gb+2BYrtgFochuqELe9ZFflrzWzLyoEswVP581qwTzFfpgU5eusj7PAvn18dMXNG7GAS58jiS7CoINajKYtHLh/jnPDpj3w/3//bfit/42/9L1xc3vDkyWVx2ARRV8CmEqiWeUYUzZGLqyPxTHnpufv8xO/7ET796Qe4/BbHaWYfPNE79BjtwbOCekq5FioO3faI6yFmfM6EZBSaFCNZcnluIeBwmgrFCHBSZMqtDtKLR1ZMgLXT1qZLl3kDuFWCptXFKHu6bXvl2diT+ro1ap8crjNwaC6Kyhzf4WMfecDv//3/DC/9+utcPv4W77zxLsPQ03eJuROOs0ddJGRP6byBqnDv4UP297a4oIhkavZICmAhxfHMGoEzVGcLFBFS6OHyTV68+yk+/1s/zWvfeoNf/dKv4YJj2J/x8U99is988jlevBOZ37vEifXC1RSR0rBZVfkT3/dlw9e+jWNQ5ehrn9vqZLVsmNSgztZ47ZloVLRlSlrEoDQgxlgZz37mUjPWKK7NLZTVWWM31gxka1beqjsrBdzQwJJ1KE542cftu66+c+1bJ01ooi2CJirjXaFmlueTkhHXLM25aoGKKsklpIi8pLgElKEAlDlZRuI2u8Ac4FSc4BhNWj901sy874qD27Lf5QxyYmUh1FpNC7aXUo9F9Kc6oevgZ/l7OVOdM8CGBVyNsao6ZqvzqtTETAOKNWemufRUlqW+co4RgmeekwmxlM8Vil+RzFm3dgGlhUMqwYU3J9hpOX9rkFf8nnoum6+z7qm6bP5b9Yrtr88CScvvhAo0LdTTxu64dfc65w1CKX7VEixb5tW1x2lVlSXAQldPKvWokuVnEZy6AhRZ5kgrYKD1PfYMrR9jO4u0nXvUxvZWj9SyzreDX+HWl6z+V/2+JeA1YKCMVXl9LMJxIoK1AXKl7lSL6qs9t3PeygnIrQG8OI8P9vnbzVDYERWAsbOuitFVmmsurdSq32bCLpZhnyezQ5KVlMSEbE7ZwJtCP405GjNESt2/KN1mIPgev7G+kDkZdXo7dKiaorz3gZwyx8kA7Tnmtq+zCtnZ/U5zLFoLlvms4LBg4Ldzznqhe+jw+Kq2XnzMeTagbJSZMSr5CGnM9D7gdSWgI1bH6KoQYF1TIgzDQEze1Pwx++ii7fGUTEjN11IwcUtQXbQNvnd9d17ynUbyf+E/+uNqRfc1eCoGZ0UjaRlGtXS990tTeQsKl+ybYEjm+nFWcWQzFrUxPVlb64GUc7u3Yvx2L345uOp9VUtxcq2hXD2rlBqh5pcvhr4FZ+XbuiWebAFguzda6hbM1AW3apNR71ucbXuVttdWI+v9ygCXp6xDYXVIpu4pTow2qh1JDEVSA61ICaZxZpwjsdQNXV8emDQzdcKM472LkevrGclCkEznlPNeGYAnkyeGgNLhUsZrwvXCfEwEF9kE4cF+xzSf8L3nOCV+840nXN6MBO8JXth2wZ5e4TgeTbY/wW47cPd8YzVcPtMPnrPdvoxd5s72nDvbgXv7zpBan5nyBG4ApNTf2FhdXx/JKfLKR1/i7GzL1ZMLUMXlxJwTx0LlEPWcbwObwTNse8AVrEvog6moVjq0K1leVdM9885xPFySfEfSzugUOaJkvATOthuCU2KcAL9C6aUECFqoXdIQ/Vwdp+LM1f5j1Uuo624tQc8aSFgd0MtBvwZKGmxDdRXadqoORXnNuqZSlVsQUn2PamNuFgcce6EvwZdkNvGcWRIalEkd7z+9Ij15l//ui7/M3/2Fb/C1b3xgCGlORPWgaXmGckDvOs+cE5//gd/Bj/7ID/Ov/Gt/gE/276HHJ5Ajc/b8/S+9xuP5yEUEInBMMGdymiE4osJNzGQPd84GHt0/51MvPeSs99zZOjYlK+GdBcyDmFBU50v9m9ge9j6gaZm7OvaIWGawBDwtaC82wa1/1lU8Wf+7Mh4//Y9+LwA//cM/C9QqzGIZVnZHVIkkUujo4wE0IPvnke093rgUfukrv8F/8ef+IldXl8xxJB6U2UVCUsTDSYV57viLf/m/4nOvTNy7/AZkh/TnJHeDVw8Jc4QK/Utih/psg6xCwtPPR2I3MD/3Q7z5RPhPf/pPs72350/8yX+LFz/ycR5uniDjG3QB3Gx1kCQxavlcanCaTaRRPNegRT1Lbo0VbVhKwGR12bcWNHWP1QBkubyU+rvVBymsfl5i/txess7ifHsKanNoy2fXAHf9+zXIYG+quxIqO6eBQ2W+l+Cj2od1cHfrU83urwMSqU9tJ0f9nhZwxiXoq3amrmNdrWkVcqK0fSgO58qAtMCgPI4BAbnRfU2NtgBYQgEiaIGIZTlKfeIzAGvZbjTxEgRKr8Ml5k7UPVJRVcGYSLHQc11RJ085QZPwty8UvNWXazY2gWYxGp4P1JANWRqrl0FESo2VZeur/1DUVQuooYoBJNWeLwbAss+rGrs6L1KDrxVFWLUwN+qgaFkvdjDcArpzVmoDxVxogVR7LqUmsE7fCpm4HbjVgV+C6nX22gLz1ZvbGtAWQCsmoJJSWsDJ8h+nNn5VhdPWeUbUrWr1hfVSr6rGNKRmtfZWjBCjYUr5eSUU51x7znme7AnT4tep0qj6ijLPMykm+mHDHC1LLxhoYnoE0AePL63LvPNWfiBL7Z/R0m2u7Jw1IHROyuXhRE6Zm+PE8XRimmYOp8RpTLz7wTXjlJkj9H3Hbmf9yff73jKhXSh1iAqaTQTOO4ITOlf3AFYW0wgWjqubG+KcGGfrLX46zZymaLWVmL8zbHvLqIvV1gsGmvR9Z7oDgy/1jR5UmMaR0Fs/xZwmVJQ5G0AlpexJUaOoKnhxpkxfwIHj4YAPAS8O7z3eWdCpOfOX/uYv8b3rn+6l8xP5f3/V7es7zixKCM3I1QyFq6hncZQr8keq1M1qi5aCZXMOKgK2UMLWfP1U0iKVh6+l6F51QYGrobb+PgUpkYUSSzuQivNcuO/N+CxgEpUCJGJ8baozUzbAQu2w1zkxtb2ai2mHHRYAL16jlPsuTdobMbEYLydWKWfPW57BV2TZakUquknWkp3NiEtkhbnU6m22WzrfE8aZFI+4AGfdHWYyl5OJEXRkLjyM13DvbscwOHQ+ofPEJx4+IhbFi5yUeFLEOw4yYrUlhpypeFIaiPOR+3d6Htwb2G56NMKDez2X1yNX12ORLLds4PnZlkcP7ljQnc2BfHpxZJwS05z45uEDttueB/fPynfPxJxI2ZyBeZyJMRJnaytxtu8Z+i33TyO7ZtS2uGR1cOMUOR6PXB2FGDNzKgfUnJjSxP5sYOh6drveVL82A6HrCA72Q2AIAzcHZxRgd4N3js0wgA94FwjFSDtX1STrWi+BYTKE33s7ZOwFtvjX2ZWaG6zrWQpIUhtIrwGHBaPQttgsK7RCWJbVdQuwqM+3Fqdc//52epQKyVsAtQpIVSKObE6ABpJPuDyjG8/bbz3mf/rr/zfHyxuee3CXPnTsO0fnjM53FYuiXaX/lDGMIkQVtnfucvbgAcMAH7z5dTqUzWbP9eGaDx6/z0kyhxHilLm6GJnGGZHItgtc3Zz4+rc+IEqAaebOfsfHHt5lvwk892DDft8TfMcLjx7w4M6WT758h/2uI2LPojVAT4rHEPFMNIRaAjlpq5erFDqKymDd5xb416C0HvAW3OTVnP2ZH/kHNi3ZxqMKpYi4luHUqtroOnN03BZRZb58j/T0HR7sH/J7ftdn+PF/7sf4h//4K3zpy1/lYTeAQqJQ433Hvtvx3KMd2/EpPRuyV5I7ItnhxWP9YHP7htSVAAAgAElEQVSpSQMRy8ioJkQFlwPRbTnMiW/96ld5/Y0Dh8OB6Gd+/u9+iZc/8h4vf+IB3WbHdtezldEKdnPE+5lPbBwuHUkuWMCkiSBCUsvg1AzgApjcWrIlY1uEN25zPG+hGi1OXN1PhZb1o/q7UvbaLeEVqW3D7BnKXnOytBa5tV9YXovezpLUrKI9grS9XosVlpYr5RZArXNi9bd1BmYhoK8c4tsvXykqawkeliC5nm3VOfelXZCFKjYgUgMZX35XA8U2J1W2hmZfVCp9LbczmuJw1udPudbe1sjc3r1kcutYLdarBdG3Om9ay/NK6c1UYLoNGIplZ9tYlOeq/kA1c4myyMSZGB+RmlGepth8kVqnWbOfTgQ3l8xVsQO+gIzNTlPqaCvtHyCBOFNsrqBCLu00UkoG2KzsfP1+qYyZVJuT18GpoikDwaZcs5WnsGSX2hqlMl1skVsWuXyeeyb4lBoIG6gXfJVFou0fyyLX+bZfN2Gf5YObZ2T9FpvkbFmwcQGNEKvllVq6VOe5tmgpIysgUvxIKb5mnddVwF3XQtZM8BYGRS0gwApA6MqzW81fUdd3jj6YcE1rg6XW0zkrRqdMcyvRqIyxrJkuhMWnxbJ6HuF8MHX9h+c7VJVZYxF+Ua4OI3NUxrk8t9qaOJwmJjLzfOLyZiLOicPpSE6lLZiDEHqCc+x6zzAYyO6dlRadne3oz7z5iSLWxiMa5XiOpvR6itbS4+L6xOl04vL6gCJ0Q08Qh2gkp9oqA2JSttsN283A/Ts9m96z2Q4MvWPTOwNMNZu+tnM4MnPOTGqasledWUF7lsQ4jszyYZDve9d3z/WdB4vOF70Cc4IUYyu5ooJaavdtozTJYG0HVN2kzpmTvXgFtffRQikN0jWFMmsEWgyiCqEbLMorvPBh2FKbrEsoDVyF5kQ7ZxlH1Qo6rTjwYtzrSqMx522iCyYq0WoBaiuAcoA75+iEpnTVqBZUxBFT0lvgQeI8G7WloKvBe9Tlcv9yuIshSPXocVIk1HUJSkMwZS1NZwQinU/kpFxfXDEz8vTyinc+GMm6IY2PQeDi6WRBrLMC6W++9h7Heebq5sTNzcR2v+E43TCOStft8cGR88jplOkHxzBsEBwXjy9JMRKGjpiUOZeanZzYDh3PPTzn5jRzc5ggRh49vMvv+B2f5d03r/nyr7/DsBm4uLhG6HjwcMM0nbg5XCPScxqVpD1KpvcTu8Fz9fh9Ju24uDlhdIeOFx7dI6bE3/7ZXyUDL7zwkOAyFzczm84zVIqEwBRnPJ6HD+5ydr7l7DxwZzMQLxM5X7MbLNAN/hKNIz44hmFHxvH+00u87xl6O9h3+w3784GzzZ7727v0wRG2IFluHTbAgujKytlTGhBQ/9YoSsUptn8a2vchE1rLZdZvYXU+3/pt8b9XzwFLVsWC0tVNozDNE/M00297UxMs2i+15m/oB3RWOu2IWLeofb5hAvIN7GXgpZef5w3e4dU3XufmeIO6CbwJCL2wGRjnmcM0oskoZrNmBoTtIIyHA4/fveL6InPnle+ji0d2Xc/ZsOOP/dBnLbjKJ/IMKYkVx3shHkZ0GtF4JHcOThvSOPLVf/JNbk4zrhOcjJDgwVnP3btbdvsOJ46NH8wxEYehwdaDU9RqWuaUOB5OOOfoe6vbTWk21kToifNsYIQ6cJ7DeGScDdgIPnB3t2foO4gzEizj552hq//ez/9Rc5JL3fI6U/TvfPZX+ImPvk6aM//g7U/yX//aD9j+r8h9VsR15I//Pv6Nj/w5eOM1/tHbB+RH/gycfdISL77Ddxv+zM99Fh8/w4+/+A3+7c/9Ct5F3r5+jv/4F38UXVanof4oqPDTX/h7vHLnKTpn/uqXP8HfufrtfHA58/iDp7z28LcgCF+7uMfz41P+hcsvcpjgdPmEn5F/H8QRJNN75aX7ZwjKeXfgJz/6VX7fc183FkjogFLjJDO3+LmrhW112kIo0UR1LKxVxxK8PBuCVYe+Zm8q0EIFL0Va8AMr0LDQ2VSVtGwe+38NUMvGqvNVQYL6JKLy4b1LDX9KcLkGcZBb/1e02ZO18wmFAcHiGKdCl/OuK5TQmr2yzM3Szkmt35mYimk9W+zrWfagZhwRsaReXoG4xTnPWe3QV3M0l0bispypavYCcfg+ANLa7thCl6Iwu4xBrVVTMNE3Z9+7joWqkkuQq8UZRWRRaa3TUh7PgGzzAwxgVMbTRBVCMWNsv3cV2OF2natztdUFhK6zNRFXFEgskJCyZlKpnW0Z5VVA37J2dc14m59UVeVZBYwl+KxlL3WBVxZMTrn83YGzOjEpz5RyRopQm63RhZaZMRVfnPWetNpQQaT0Oxa7b/DO2nJpXq3IEuS3QLz4SwXU6rue2nPb5nHZbM4JVYuwnXFejOKoi/0xNeO63gUp/lOKBmDXNqJzqvXFtgZqTaTt21W/SfH2HAKuq76Y1ecjWPCNEjqBqmruS117eV9OVpaA1to+C+bNx1Tm2YIp7x1Rlp6mTsSa3Gcru5hzQktbkxSFlIyWee/umVHaXeY4HgjB04UBxw6XrOQjl2Arqgk35ZSMll0EDo/J6pVjShyPI3OMvPvBE1LKJoKYIWoik9kM1lu6K0KDG688fHHLEO7Q98H2jbe5HU+Jq5sjN8eRi5uZ4xSZkzBNN/zGN99jjolTzIxTZJyNRtt1Pef7Lf2mY7/f4FF6lM4L57ue3nvOBk+393RdIJOLPfre9d14feetM9TkeUUsu2WbKSGFt3L7yLOrHjatz6H9sv29K2IyqRxAFSGqr08pcTxa492uC0a3yCZ7rVBUxnJBIevBVekz1fku9WLtY6X04SqU1RJctia+pS5RS16/SlIDDXmuds+Vg3gxUgu11b7rUh9TADFEqvRrKo4LKAWpqhyg6niIbxmNOnSqJsYjMtNq4TrFRSFPjq7b8vDRHhd6VHpcyjx3Pzb/JKZM33uOUyZGQ6I228DFxWOSCj7sEAfTdGA8TZyOM9vzHT543n6n54P3L3Bd4PLyyJSqOIGCZG4OJ47TzJQSopnLw4lvvPEOx+OB4+GGzgdizPTdwO4scBojNzczjx6eM/SOYbMvBnnGSaLzwjjNSLDM7H675ZMffwHNmZtH90lqLTK8KPez0V5cVoLHQIZ4YNt1nO+2KHA9nYg5c3M05dub44wmOyDPzzb0HWw14bzigh0kfRiISRlH8L3Si+D3HSEIkBqVdF1T0lDWW05hzUCvd8izbqU886+1My9UP1PQW/ep/7yVlXkmq2jL0QCSsmpbkGI1DiWQ1VI/VvyqSgFSUQiQiKgKQT3qS5F+suzx7/zCZ/jE07v8j3/lDaNz1hFRUJ1wkhm8IztIkjCJlcxm09Nz4Ol73+Dn/q7n9/zQQ+4OmbjxiLtGto5NP7AdBgTL1LhkmZvZKa73uG5D1Ex3NhA3QvrEI04xQ/DkPOEyDOrofEZSxIWelCPTPAO+ONNFyKB4J96p0XKcpwsdAU9MJetQaGd939H7joziux1DjMRoku3D0NF5h4pHZekntw4IROseqmujZD5y8Zl1sa/FxKDOMefM03ff4Zff/DLXx7SoBNa5LkGAq/bZ1RrUpTb12UvUFcDKIymgaYbNnnzluDkeubo5WL9FgcNp5MDEyx/9CHfv3Wf64HX+wWu9KW2qYjwUA+aeTlv+6td/kJ948TWAIqwxNzCtruT1M4m98Pa1XuDo8uMzAMqza7+twXaL6l4WSuA6MNRbN7p1n/qn1rS8OabPPHx54S0cB1qgt36+Z2fi1t+0Zu8W2ulC4ZWW0atCE65QflwFpcpnNaGaVQbLsmcLCNmubKyemtXJpb9aFStyGE3bgdVxVipgLp+N2YosWhApaZ/deqBmLaJsyyfXOlDni50rg5pSbmUt6lbMCbWs0Po7OXFkv8wXAtlbEIV35h8o5i+svnQNdK1Be/3dUrbiSu2c77sGFNR5aWvYSQuUGyW9nu/1Gcva02dAw9sLgAa8G5Bly6k9WZ17ofCnPU1WNOdbY7rMaRmfmuNWW4tmjyLLAjYV0foctLVaM8xaTVP7HQppniv23gRnmi8o9bW6BNNVmKesvyzGZpL6nRVqWr9mPIswbwMHKkV10VdmeVa0JAV0+Q5qD1O/Xx2lnOy5UgWDcmWkrcCpBhBZFtrKebWVDllSPS8UbhQt91VswLSxawWclkDJfEeyaQ2IOrRw4o1RRjuvaxsm6YKprOaSfCnnYE6BoQvkrMwxL3XeamJqmWRtbgDEWU2zwhC8MQqKsk8u+hbBKbvBE/zAbrchJfu+cZ45jHtiykxxZk7Z+lqLs/0inUX2oqXvYmaczDcM3rHpQ8nIU3yob3cSfe/6bri+45rFP/+n/5h2XVc4x1aMvvTzKaGiLAa2/hxCMEpPUTlrfwO2uy05Z6aTKTx1XVeyL8FohzFycz3R94Gzsz0xGgodCo/bnFtDiOrP68O4HSBq2SGj55hjOM2WIei8oapzNIXHrrYRKJst5dQypfWeNRDwYSWfrgX51VoHac/QhAWqGNDq0F8LAhiaSTmoKciuJ5T+kRVtVIRpTnQD5ORwXgkBYhIuLiJKYP9gz2YX8LtAmGe608w8J+ttKcq3Prgkac/QbXnh4RnDAO+//Q6hH9AiA328uWIer3jjtSv2984Iu55vvHXB1/7J62Qcb775Pk+ujrgQcE7oeognGJO1ShARcjTXPmsixRFUOd/u2O23vPTicxyPJy6fXvL9n/kk+23P84/uE0LH8RS5Pp3wPnE8XHB5eaAPPednez73mZcZOkfXDyjOgj1xTLuB0zExnka6INw937FJTzjbeJiFD54c+LVvvomGjouLE/3QcX115OLpka7veeWVl9ltes62jt3W4f3IMOzYhR3jMXGMkc225+5mz6defoFh45i4JhAWZb62RioyXZ0xoxfeOsg/tA+lHQyyPmDX6+7Zd3w7x2DlnNU/V/Xhb1uDpaZw2BzIUv/ifKV02/t88FjZYcRnR9AO7YUkCZki7uwu4RMvIvo+/+6/+Rf4lV9/l6cXT3BeSBrw6QQuIN5AnzRHgnZc5ZkXH53zykce4PAcbuBf/ckf5eMv7ni4U9Js/UTv3Tnj0aOPEIKhlZICkUjsSsubGeZpYthucd76ABJ6UteRUsbjyBfvMx2viQwMwzkxJ25urnDSMQwb+k6Y04y4YK1l6h6WIvghSowzcY6cxhNdGOj7jmE7WHueEMhYb0209OdC0TkxFURYMGctxkrPMmR6u9nS9R3itdB8pbTLsR6ySTPHeSI7YH+f196b+NM//Wd5+80LLj4YOXaZnVdcsP27P3uRF1/8Pv7qX/4PkCe/zqZE7imOdJhTYUWp5nx5cfjYMYcTSQd0Tmh8wu6lz/Dmtec/+bN/hS/+nz/LLgW6veMkjh/4gc/wp/7DP8UP/vbPoB98lXx+jQseuVE4TJze/CYxOqJsLeCWTNZo9dZFEdKt1IHWy3O9tBvY8Qyb7dnlvP59LmbaUTNZto7TCmhZzolnedgsfxcxJ3B1puTV3nQiBiAWZ9i0MMxZboF7dSLd6gG1FjWsHd3l/xUwBQo4Udg8eYmgKyOnUuacc6W2fekrnGLpG1fEdYBybmrradfONGi2LBcBoRQjcwE/QAjBrURirM+iPaNFOaECvSyZyrVCeAWBVdWyR3XSmpO+Wg9Q2l5lZCksbdmI0PersXCE1kux1kbrksGrAnRVVbYEd8YWMJCo6zoUba03LCqq530mFGc8l3YkawpdLuvK6KEL02jNLpECBNW+wXW+K9DsxFlNmCiJJVNZ11BbO3WdZAP3pADRMc6rAG29pqQB3llTcV8WCrqAiSSRb9cSsvQzXdc6phWbBsyW1fFvc4sUX0hoNZSN4VVKilJR+y2ZR8tWWsa61rXWNWFKpLpQVZvftQZRFqGyVt8pBq6371Te6731V45zvPXd6j5ejAnl31bfGGqf0ZRwzjfmF4Wq3Oa0xdzFFlSQoXNtLFJayhR8UbavNOT63lSTFgI5ZrohtP1ku3/RJ/CUvrH4Iihjz5bVMtBJLRsfk/mCKVt2NOVkWUs1ennnfatj9N6x227x3tN7W3/qfKl7jgVIyUxztBKRm8hxTDy+vOEwZi6OkWmaefz0gqwlS1/aimyHQBcCv/DLb/G965/u9f9rzSIamMZU1E4NrUwpNxWz2uB4niecc20jODGDHbquKNnZEs8pM06z+SvFcTidJpPJ9o7gA94H7twt/V3SsSEtKZlhiTGRNeFwrbYIapsIOzijliJcB6EzvHuarbBdqAX0ji70Rc7eFVZRxgfoXNcoFxX9TynhvG/oYDU2ufT8qQdrzolpHOm3G4Z+Z68tNAjnPYuSqpTxTGbEVj0kfRFeqdkk54T9eY8mwwk1RiCRBtD7wi9+6Wv89//5P+LiOiP7B+RpLL1vlOwzKcPFVSLrRAjK2SbgCGhn+dnpZJ9zttvQd75kGBXB89yjM0LX4UNEuy2EiXGOdMVAbYeePAnzNCMo2yFANHRa+4A4z2GaeHJzxetvXdg4e+X1f/hL5DkxTyZBk7MahbTr6FRw2ZkCGMrEz3G233Gn85z1PZ/79Mvs94Gh29ANjtBZnUXMkYvjyNOLA4+f3ljN12nGb4Q8TQQR7p3fYbPZMM+Rf/zlr4DzbLodOSYePjzHB4U5EXPGbQfOzs743Cc+xqc/taffwHQDmbkhyQvNyJNK3R/Q1NlYfoWU+qCFNkPL9LV6iHL4LKIg9fCl1YzU9VevtSMgWg+vNaDD6t8Fndxt6p3wmMPoquJkTDggHkdkjvjNlogypQPxEDikkd3DF3jrW5f8r//N32YchfF0JIfE/u45LsPhZiJmmKZo2WixdiizBM6Yefp05Kf+9d/NT3zhs3TzzOtvvsHV1fvc3Tzg4y8/x7uHS27max6/+muEbuDOHWvHMvRber8juYAOA9IHThkOc+Tm6IizklMkFBU2uhcJd5Q7W+tTGlJke2dPcNYDkpzYyI44J6bDEciEridnU7gDyzQOQ29OAuA9SJqRlBiql5axz/NiP3SBveuK3bM6yaXe2xy06mQ5DaYQma1PYS72q4p4VSc/eeXHfs/v5uY0c0iRv/Lf/gzdtmOjG1KKhLtnPPz085w/7JiuQY4zWhzUGNZotRBFmXNEmU0dMh1tQW7v8Ib8Fv7a//43+b+++IvEcWbaZo4H5ZQ2/PwvfJW/87f+Dtt0zUv+HeT0HpochA259+z7DX3nyNpBtjrr3glj8uCDBXLrJtSsAquatrANZOtaloDtQ9lDkUVKH0p/zboX7AMqFbKWOxglkpZ5aP54be2EFqEWCzjLbWpOpuzNdfZ/yXZm0bb/qN9k5UAuAcRqL64C2ZaF5HZg0YIpFWI0pzMEZyBGoZ3hYDyNqCqboaMfems1VN5bz+a1Haj3r1d1yuu4ueIkZxJzEbPRnDmejkW23zJUfdeV9xaxHS09l8WRWHpOWhYytSC+0l6X51DaSKuSMDZQFYvRrOg8tdemZE3YUUfXh/Y654TtdlNonLkBeTLnRuv13jUFcs2Kl1IHFwJJhXmaLBibY51oxmki+FISUoAhG8uqvl6YJIU+2YIQMGEPKAGXjZVqoSn7UjtXQOMqoNNqDaUmwCyj3Orkc8b3pSl70XrIqQZ6EefttSYGUwJ7bB9mzaa2KcFqWrX0Ms0KEsp0FVqwWn+8KuomQL/pcFqCVVeooW3rLrvDfB7bAaIm1rKUC2sB4D1CfZ0sQXdV0i5nn+ndOSr9tvbzQzxCV3yyAlIUr1ecUbRTqqVI0PuwBH91/ZVnyimjrig2K/T90Py81g6lgBmtQqsGsL4mAmpdc+l9G2Pz5YLvLGueLQlQW/VUENEorcWeISQBiakoQWdUhZh9sw8pr9bICpTRAmKFLrR62uqv9x1oVUmUEn4KnE4TIMSkPLm8IiajjHq/lF0lKwxvYljeCX0Q9oNy9yN7Aymc7RHyyyawky0TGUtbkHmO3wsWv0uv/w+tMxJVrSzFQkF1xdC6mk7ODMNgLy+BjRVJ6+r1nmHTk9JMypngXTO4MSa86/BlAxcPoRyiLIiPODTl0h8p1I9uxgPqwV0QJWdc7JRjUaH0TNNkRrC01xARUowlG0lxNCBJXgQrVlfKiXme7FB3y+fGGBupAywoSDFxyqM5FoX+akIDpU9NbTgkrdXvcnC70uxViywyis6GsgtCKFTVpLDd7Pn+z77CH/ojPY8vM/35jsNNZk7XNt5TxjvH1ZMr5lkQp/QbyOo4FoRNxpkcJ8ZxJqmn77bsBztgTuMJN0/M88Q0z0yjmrQySppmpnkkZ4dLUEVqdvtzIJHiiZyU3psSZe5CFS7Du0CUxOztQDDbF4qzVjIsBem8PGVSHLmMwinOfOnVb6GiaJLCt5d2EEwxWsA+W++js41HrwM3xwMiQvfBEec9ThzjOFqGtOvJKdO/+R6bTSCNMGdldtB5zwfvnfihzz5it98zjQec01s1unYgL0YaSna5TnHBA+u6bpediIuj3BB2vb2eVoBn25ol013/KMKC6ta/r95bfyfll4fTxOk0Mk2jyfZrblmKPgRClcnHIUVhjpzJY+b84Z6vfPM1vvq1t/BnD/gX/8jv5S/8Z98g6iU3F0ZbHFPEEllC76x5s0omickvxjTzpa+9h9885JXn79ENG847zxSFV9++4s5ZRxcecP/FPVNKnPKJOUGaldPTKw6HkVffuuDiaIIRh9ORt5+OXBwi71+cmgS85sxu4/n+Tz5iG4RPv/gI5zKbHkLnON8N9EVtbr/p6ToTEfDOo2GAnFrwsOl7cEsrH+883oXiyKxUcAsYRclWiBp1nBagFMchBBQliLcaSg2IKD4XmjuKC4Kow6eRF4eZP/SHfy/feOfIb7xxjfAzdh8dcZroXM/d/hzSZM7NKjjJmptsvD19sS2uiJXECek3+PuP+OIXf5Ff+sqvkdPMfuiY5pk5Cz/82z5JPI2kw2Pefvs17r64Yas7JpRJA4zCeEz4oPgu4cklCM5IDibcs5ZpXF31qW7X9C6B1e0Xa7EN9rraI0+bDV9nzgoOrxbU+RqorT5Y6zlTfq4UNG7twg9fzz6r0TGXbBgY/bZ9Tr3X6is9S0FFlt9VoGhd3mnUY4tGvA90/cJ06bq6jz3B+5aNa2fNrSG0tVizOeuMq3OObhVomtBMAbDEAoBufa/i8NLGrXxNqdlHbQqgDWArBlt1sWVrZoSd4ULwwaioBQzzrohmqbXW6IfedBTcItqiauBsLjxAy55BcKs5YJXVKoFnzoJzGXEBH0wxtb66HE+r77bQa+u6XavEV5VMAxpoip/WLiUT59iYV9aSJDc2VDsxara4rnWkOOtlZZaWDzFFpulURAGt317KWnrmWWa/LC1T2xwGBDEROGdtJmwvOXDaxr75X4UyWYTgLeBVA/8drm28KtZKrQNu+65SWitl1e5bGThGZ5QGkLbFUwF5tNWSWi9qVzLnVfchm30VsTrE8n5VhaRopWzb45NSXgJwCpCidhi3eS3OoJZArrJ+U6vbFVJpfF96Odl96+SwzCWwMEwkkXMCHyzY1RoPLww9VddMj+JJSTESd9mPmpFU6a42XlUcKZcgVcriM8qtPVbMihJtLUq1bYsxqrrAirE01Alh01sLG61jbQOXZQF6pmjK/ERAE07mZkNrJhsEdcb0i2uD9r3ru+r6joNFza603yqIbXaNZ45m4jzbB/ShIHXGwQaQouCJmtLW4fpEjMo8nQhdZ4IaakpPIcA0jo0O0fcD3gs4LYpPFpjt9lsLQudj2ViOmCKh60zuOpWi4uAYuoGsiRyFLAIOujAAhXKiVt9jdT6hHEapGfis1cAstUWt3hEa/WU8jYQulIDUnnMcx1LnKMVA2oEf5wmKI6OdEELHeJqAaMYOQ3+ytwCy1XJoQmOiG+4ZJzzOCJnQe8bDzHxQHp2fc/+ucLg+sd0LYXMfIRPHiTRnXjzfIG5gSnCImdAHBgdDJ/gpk6aEuMxmB6++8R5Xh4njOFsftShcXpw4cE06jYbMyowDurCx9hQqzOMJ55Sh1JeOSUmkojzo2OyMEuxyRzxE7m4HtltndLvTTIwwRogkkIltH7izH/jCS9/H3bvnfPwzH2V3d0/qzogp0GmPyoj60cYwj/QxkcfI6fpAUuWQEnpybDaJ4IShKKk6F9C8YZwnLg+XxJh58PxzjMcD108uSFkZ7pxxduecB8OOs80On4VtD5lACF1zJepRXo3iImASG6qtsKJmLYfIgqZWoYpFLGMd6K2v9vv6j+IU+2qEG3hS9nENEFYZEc/MEDJBHJvtBsQxxnlZ286CFAcM4vEKkpTTPcfX373gZ372n7Db7/ipf+kHeX5zwU/9yZ/iiz//Jf7n/+F/4+ryEtcLGzYkNREBHwRNiaQTPghDVr7ypa9xfRn5gz/+Ba7feZVt3/P8vTOcjKT0Al4CPBkJ24Dbd5zGzM3NDWf9GbLZ8/wrwr1Jefetp+zPtjzYKmn2HI7eRHVkYIpGwbrnJoJXnJ+M6n7IOBGm65Gh6y1THVw73hOReFLr2+prXbIrTg6NNjTHsYy99cLSInLgtSDltZFqua+uDtlKusqxKnQYhUqdmk0CJBk0I/PE9uYJH3/hh7m8fozkK7IYKJecklxm2AzcuXcXThd2902Hcx0hKynODWUHA4s0C0JCwtZEQTZ7ePj9fPmX/hLfevXrpGnEdQLq6ELHk6cXTIcjb37jN3n9nufcv0I6POXp5cjl1RHHiRcenuHE8/DhfbZ94GxjkuleFO9LYJJvQSYtUFz/VOugarar+q1tLWddgrxyPtVswC2CqWDOTlVDFWn7te2zGrCU6ap7SOqL6n5a4kmbrpUzWO7+ofDS16eR5Xt+CPQpTltmof9JWVM1gFzesL5fcS9LFjYXFdIKNK4zAtbs74MAACAASURBVGsgq1JOgVbGsWQeV2OhoKUuywmF7kpz/tsj+QJIoKv+iOUe1SbK8vO6v/J6ImqAL2rtZZyuy0tMXTSnjCvKrjgB79FQg8XahsPCAi9uZfOElGlZNimAd828azL7fXO4YZ7mwopy4KronDSV60ZFLUEBFDV1qrbBUobQMIji28RxBjGaeaXAHg4HsgqIheCp9C10pYwlTjPTPFsWT6ALnu12g3fOfDAR03RwkNLI6XAg+IDznj44XNiiqtxcHxmniZxt3ucYSanqIJj/Mmw2IHBzdQSqwJIJx9W58lJE+7BASqnAjJaMk0NE21jYsi3KmE1R1YLNEDzzHE3UBvDBMZWm80bttRGuiuFVBNHWlysBmhbWTlmvpVVDTolY6sxrn9Y19XQajTnifVgEjnT13MXeCFVsqGQ8y/mdVu1bbE/FtiyMCp1Le4nqN1pyJGclS6QqtQp1+JZ9umYUpagFFA8W8LfEwrIJfXkO5y2QnlNuyh5tf9VsekotCWHSE8WOaVX01npTnA8l4VGAbi0qwUVRNmuyQLUTYrE79e05C6qB3vWNxSMxMvg11PS967vp+o6DxRQrv7qKlRV+eFrQSBFaViVU6GZ1umlZfDEpOQk5C2k2lTFDGAXNjnmMK8Oe8MGZYqgT5qLS5Qu3IM4JJ4rz5dCyLspAUTorzXcr5x1K0b7TspGNpy31BDfteKDWHlZgp5gHXR1kUutKCgIo0v5N+/rNTTc6jvPUZu2U7E/L8lSEbXVIU+5Rs5dVpMQVA1nhu5wjN5cjj9+74K23npCc43BIjAn6weowU4yknDleH1Ecc4RjFvqhYyOZ4IV5UtIMfafst8rN4YanFyPXh9moSGpGOWlEiSUTYY6XimViY1F0zeq4KnQ+oxCZ45dUmWejKItkpjwjCUIe7DsRiGkuxdZmjMQ5ksLhFHHdxAdPbriaEsmNCD1n3Q6VCT/MOJfxYjUFIo5tP5Adpqo4CNs+40TxztZJFkGdBUL7NJB9ZuPBdR1+v0fJdLuezWBtM24OE0OwQu4mFFBricoaqH6rlHW0gKjl4Knu4m1v8RYCuQSKa4dt9XqtDu/qFqt7rQ9oLVmJbxdz9r4jODP4vjgWtXuDioCWg1MgqrVn8E640cTXX3vCgwcP+L6PP8ejjWe+eJ+PffTz3P/VrzfAxMZaEPW8/PEX2O4Hvv61byJzwqllQLpuw53z+zz34ke4v32HLnge7M+QfGQaT8wTnO124Dy+1EFHc6Povee5u3dQcdw531rvqCnhshKnkXE6oXTMBdkMeCQoZ/fvEedEjtmy11nofaDvHb5bACEFpIgbVITUep7mcsgXOmJWECU7E8upViOlVU1rFU1w1aFZTaguohlOpdX0iKNk5GyNJc34MPDuk5HffPVNvvKlr7ApUL+KK0GEQJZGk5rJBE0GE+dsNSeths5BFrRQ+ZN4UhYO1xOvv/ZNrq+eFKqTA7Hs6tOnV4ynkUMeiP1d3LCl44Y7YnXfLgfunG1xEth1Xekli2W8XVXjreOwBBzVmb+10LUS+55d6LeBkGpra0ze4BtZ9kNr1dTCxJrhX8CVdusWB2g7ylRuP2t92a15bF9hHfQ+u/PaN22f284SWT/dh/OpbV/LM2OCtCi2NpDPy9cow7PYI7ClslBh7e41uKs0+RaksbJpzzzRQo9to27j7J85x9YB+sp2NWrxal5vf0KZI2U5r0VRt4zAYmtXc6mO2jKrZag+dPOameHWvWoPPfv/8hw5KaFbZWhbfV2lDlNswfpDWvjYgvYaWJi4lgUgfehsXMRA61QUbhpLpfd4B7PRbUxBs4Lz3dI7GIU5eWSecV7ogrcscwgWuG6txKBmDV3wRKnBiQVL3ts5ViwLmhMZSoP4YtO8R9XKb3DSAkATds+4smdsmBdBoGL8TJylTE9MqbSdsmyftWg1mmNSA9F8DegL8Oe9a3tHxILVqjeRNWGJ9ELnT9bv2pU5VBaab9YKsNhZUFXOta69ujUBjbkFi9VeNNXWaoPq/i5ftwIT1Qahaoqpapk5alu3+m6tGXNtzyAizHMqbD7zf72s+9YuvqvVLFqP0DlHqxEXaeUrrv19bltDMnY+IMw5N2o2RTnWzhU7X8y/K+JsWsEQC0pVakJZmx9hKsXCXBX/cynH+HYOyfeu74rrOw4WY5zpfKHbFSNqRcK2xLuhL6np2wdnLR4PYeGhB+8Kp9vul0oBshnzXJT7qqgEhuaXTVgL3g83J0M4qzwfUigcSkqxodB5VtI840v2r27yaTyZBH5xjMdkbTCm2eoT+mBV4yklE7IpPPFmDFLGFaqPIXqrOg2EpEa9DV1nh5oa0uTE4Tox+XitB7CQNRJ6G58udAVFWgkQVBnsLDg65ulUgkY7qI6HiZubIzc317z/3mOuR+XqOOEwwYtxHInRlGXfevsxc6Eu9N4TJBC2MM2RmxOIcwRRchy5e++cm+vIHJV+5wleSclqS33vTFxmNEqMJwMR79QyrKXoPaaEU/C90JEYR6HLHhcc4qG/u8H7jkk9ZHMAhm1PPBwIEvDeaFDjDL/66uuE0LN59U2yKpdX18W5LkbVCS7Y5267QHBC7z0+eHb7DT7Aw7vn9H3HNJ44HE7MKeO8NcEN3pGTNepNyUF2zHHm+nBDUsdHnn/EvZ/8YcJwn7O+w4lRfBanqmSdsH3gXCqIvYnxRHIToVjXXi2HzDo4XIs+2KmTVsXtiknNN7obLMXvz1jhJQS9HYjamiwOifgC3qSChGpBBAulWzKPR6XfDOzON/wff++X+eabMz/547+VTz6/5clr32IaJ/zHHE/f/4A0RjrnCRrx/YxIz+/63Cs8/9w5r7/2TeIESR1Tgv3ZfZ579BwP72149OglEGXYnCNZefs3f5PDeOCt44FwOXDneM5m0+P7ntlNyAn8UbmOEQ2ePnj6/Z4QPLugODKqDu9sL1bBCwcMfaArAI4U+i3Zah2tx5mUtkGuZSysXYGr/nqhShfQqSi9KQKdzXPWUtOkWLZR84dUA0NpQZBIJSitmSKF+bZAQ1Klf/AcF4+FL3/1N/jrf+NneH7nzKHSgKiBdnE2VWHBoVMkOwzsMZ44bqY5t2Ql+cA8HpFNz5iVt7/5DX7hF34RyQf2/YAmU4glZ+J0YE7w4GOf5pOf+0E+8uLIrggldd3WaLdpMmpaNCGRKScUh5bWDlRlbWRxKBuwsgo6Sm1Z6QjQHFJXHF1ZBZep1i2W8yRLZQfqh9Z96+NYJlKqMyjVw1mcsLx6ohYE2GYtZ95auvXZKE7a3rSttopEoQUx7bere/nV3r4lwMFCb3Sl3UKNaCsIIQXIaMBUoRtT93txSittM5VsXAVI6uvqZ7dxq1+xnk1Sw2kbi1sqktDOzb7rbFiLjQq+uyXedluIS+v2MOXVrEb7Xr45lOz4LVyhZFTXU6QYyHX77za3ztuJTV6L4NmYdvstuh3a96tl6DUIWs7u0kaCVS1tmQ8poLqU8aywgYiwGfqShbLaUxFhu9tSosO6Ato8geBcz3IW1NVVKbwbrKbO/rBhoXO6Omdlbu6d76x2r9YMCyh9A+2lCANmzYQzq2mPMZFTYgidrXepgW9uOg0tWATQ2s2wel3a5gBYqIgpNV2Ivncs2p+Kc5ZxrZk6o50qKRnjxYlnjpZh1FzO3Vwyd6t+iDFLYbUUoZ+yNmvwOYRN6XlLm0drPSIrGq7NfSzsj3W/cOuvXdpU5MVWlJVmwLi7bSPsviYk18SmAFXXBJjU1zSF2TNXgvSsJuKUNJU1JmU+K6EX8xnVguZi2FiYAGZXY4oNBKobJgNjtLpMAXKcQTPGYnUNz7FWcwt9FqUAo3bDlLVRbq13L03dFyiU4luG8nvXd9H1HQeLjgXNsKvKP1dDb5vOBxYnClo2RJxU8KQpqGm2wDB0hXIRrUhdxYyHSfpi9RelmDuU+oHgTZRi2HTQRPpT6QdlIhFzPJFzZrvr6XvhFikp+EaryGpKV84pMU50XWcBaAWUxKLSdW1jypnO9UWcIuMKJafWd9TiehEpfQutnjFppreosCA30gr2xZW8Wu9t88epHaxSELA0lxoBt3DzRR0xevq+5/xsx6N7dxhuJk43Txj8nsl5IplpPuIibDpH33eIUzZe6dKG3XOdNW6dEkPfIZ3y+OKClHfcPcv0wfHci2dA4vXX3iSqp98N+CA8zTMZYXNnh2fGkYiTZZdf/thzHI8TlxfXAMQU8Tpxfr6hSvmf7/cEHziME9c3R06zst/vSPnENGbylBlCoN92hHM4O9vy0vPP4cTxznsfMI6RDz54wqxwHTPzyaDCPI2g5WBXQWJms4XPff4z3H94jubE9WkkRWEYAhnhGGdiPCIpcHl95OLqxOl44PHTS3J2qHb4XtjtPF0uDr6s23evaFa6qnPwfhGq8TRVtdqfq1rrrEqojZerg1v+nFVXwWA5GGER32ABapwulGnKGn7Wx62vPU6H9nnDsCuCFWPJxhe/JSWyJOabTBeEYej5hz/3K7zyuc/SHR5z+WbiMI7EMSLTkcN4YMaKa4OCc1Yvc3j8PjdckXMkiiLMiDgOx0vefecbvPnVyPtPXkP7jgcffYmzzY4zf8a8zfz83/9lhk3PKy89ZNf33Hnhee68eEZywtW7N7z77lPeefeKs/OB558/Z7Mf2N/b4TvP/8PeuzXbliX3Xb8cY8w519q3c6lT1XXpUndbLRlkIZnAFywggott4IUIniAgAgJ4gAge/MxHgE/AB+AFiCAQhAJCsggsWZaM1LKErbDb6pbUXdV1P3Uu+7LWmnOMkTxkjjHnPtW8tF/qoVZE1dlnn73nZVxyZP7zn/8UGRjSGRICs5wIJ2W8uebR1Rnn5yNFCyFUxiERSUb9FKNHFa99Ug3ODgjuJImjp9azLcZoaqBY9pwhEodkQcmiSK1oMAAqL4sHCHZoRy/grSxAQEVIIRKLN/AWpUanwhXL0n3tybntI4kUmYlVkOrOc1SYKhIDQ0pOHTSmRU0FihKzrzkUgrVBClGZ9tHo8nczRZQUTSSjZCFNkQSUkBnHgZ968wHffvuCq/yMw+3BnI24uIANUGbIlrmw3logJaD1BFgW0/3TLaJxLyvXgvXqjkmpraJm7fHbUPNSs7MXarev2010v4m6o+1tY2z2Lf1R+j/SMgfdvWmBqntPazbBn8kdqBYcENY6zNV9vu8sbYPk5iDee/51VLD6OxNFa3V7hgKY42hKrXanJlzSbEgLWmoTW4L+/SrqIlcemulK1yuu6tzqBUWcZudOYYyxU4K1qp//tZ/dlvWxdxhiE4NpjvN9u9nslwEyZSPkYX3jqgvsxXs0SFMpt96OFYK1EQjidYvuAtwrKfNPiAEtpbcGaaCP4hmo4md0SpSl9Vxcm8MruFLq/brQL/SUxALy0EGM9q5Qi7e5Emc1qLNH/Osl31eNbY6+/WfBnXVNcWpvbc/TFG5bcNj6K8Y+CKKNPdYovE4BTsmU45ONeUsa5JpdCTV1EH8cDJSXIL2f5AqIOtMir2sK37d5KffqaRswNs8nRCIxGNAwzwspBsIYeyYyVyUSeg20Cd0N5Jw9eVAIZqJJ0dboaV5QraSw+q/GZEpuh+0Za60kVz01UFiQyfqHDuPYe3OOPreLCxhmr0HM2XqSLrO1aJJgtGPU1orZJPNXrVzLg8QOZtg6iENwUccMKmR/tqCr2E4u2VlrqzBQ9HNqycVBBAteW4LFQAr6fjMQQjk6Q7CWSllakigwOxNHMVG0WhzM8cxpEZyBVNGSDRnw8TWbFLpdXpYT6auaxS/t5ycOFofdtCJ2FUOiS14RJW+8O59KXxzto44eOXjuRdR2ita59kJlkcg4TkzTriN4h8OBu9tbW2QxdnR/GAZijMy5ZXXoimTDmNyAR8Y4kI/KcrD2HE1kx/pQGRWjITpzzaZkuZy4vbmzg34TzA6DNTVdakar1Vy2w7Apw87HA/3k8LN+Ebln3Oc8d8QL6L11whgpCod86j8rgJbaM0o2Vkp0BLiUQi0L4zSwv3hIHCfujoWbuwNvff3nONweiBIQSdyeCjeHhc9vDhznTFHYDYkxwvXLl1ydj1ycn6MV5tPMaw+fMA2BWjNLrry4FXINvP32u4gqH3/+kuubE2fne64udtwumaAjUpTD4QCi/Oij54zDwBtvvMbZfuCzp09JYgZMxArwX5xuWRYTsxlH4cH5hJSFN97+JpLg+vCZGama0DKQEEQLu13kG1+/IC+Zb731EAkwusjD4TSjIVJKJQ3RGgcvM7kUyikT7o6UXLkMwv61M6b9GWlInO0mUoh87YGQqhCoSBLi+WgUzSrEOfP+Dz8nhIVpmFDFG+rGNSgLwakWrebEHNUhRmv4Ps/rKY87ll79lkPofbss+FwPz+47rvhhd0Ys2x9872jPhgTZCv+siDe0HqeBsmRKLizLLUjgeHdCEdKUiEPgbIhkdjz+xmPe+/TA3/695zx663V+6S+/xZsXl5TbOx59/WvsX3uX/+HXf4Pvfv/7jLoQh8CxJm6fLVw8uuSn/9q/xOuP95x+/R8yIeQAUgu3T6+5fjwjl2/wjXeUKgmZHhJCYPf4kp+avsl/8PO/YAfl8Y58vGM5Hlie3TFrIZfC1dUl+6uB3bTjfALRzPP3P+H5iyNIQbwv6MXVyNWDHVdvvstwuUMHhRoIcUdByFrQfDIxmRRJQci50RMt2KjZEV3POCDB2mMsC4d8oqpwyMLtIXNzKoxj5Gw/8uDhA3KcSLuJIIWQbJ7ltDAESLsrhIxQrCvQYDRpEUgetJdUGMeHvPdnP+Ll508Z64zqnhIyKZ4IRSlFmJeAhiuKvEDSYv34lsqkxlzQZCtuEqPT5jAioSLTY5ZywUuP4kINaBRyMipsDomzYaTM8OL5M54+e8b5azvOLy+R6qJjNJRhbW+wHiaA7nuY1FF7fcVxcBGo6ora83Gx9Rwtg3hzO5OXwinPpBRJaeTyfGIchBHIBdIYrD2H19nkoIQK4pR41dopbUXWANKc2PuOOe64t6Bq3bFy77Fbj7eqKxBkwXDombs1hu0pi00Q6s6073ALSAXqtuzBALBIshpzsXPILmoCJKXOlGIBz3w6uQiTXcsEjpxKXcXZJK21RV7Vxf05KHbGRhfU0aV08Dcg5mSjPQrTRoDdzO3h7s7tmfkGs2eXAbRoz9jbHttQ/QDR0bNginTp0URVy2BY1tSetTodrpU8xBApYoFPLSYzFKK1owk946XUxv5pb+Cqm426HmyaTIykejARXADGS7zGlBAJLEtTEzX1druFiQT2mlBnLzXWEnBP+batz+xOtjnvWyAjUqoyn2arbYyN9t/8Cp8/sRYINFVoBRFTr22ZagMNejc/H2PpIAFNeVWFJZ/snYNAMp8F7+HXe3p6bbQqHYzI2mpW19p9qEhUhhjoKAJQyoKoJR+gUoqg1QhptWZQo2BKqFyej70G0Z60gs5eY+5lOAzAnpxnalXScIFi9Ht8PqMopXrtordBQrIFYc60CVjNaq2Fcjh4UA6B2IMuy/it5Vnte6qg2ZlIqLWbKJkQLbCdZ/NllwJLVk94GHhwWqxl09n5uZUBZfObhxg8uIc4RFIMnOal13RKEIaY3OYYsFVRlnlhOWXGaWfP5utKMV9WoYsqqe+pBsxENfsxjCPTMCDiGdyAiVA1JMZ3sEoTkFJXTbZlaL53AT7gq8+X7/MTB4vzrF0it1EwgjQpbLeGDamiIRWbrIavVXpGw1RCW8ExBEe+TMmtVrvfbr/rqeroSFVbcGZgN6pjfobkUhDUqQHmHNvm1d6vqXqA2FSrGp2llVpadtSLkauhbQuZUk3eOSasH5saYibuDNjJyIrWgSOD9peuO7LJQK0cAHMqAi2OaHSC9d3as9/L5zs14+76htu7E/vzPedXVwgVeeL1YhK4PZy4uztRw5M+viGqCY7Ub5iwBSaBnJIhzoeDCQipCAWT9o8pcnu64+nnF9RFuTg7Z39mLSiK9w+6PWTSFDm/2pvxyEoMgXdeO+/rqGhlqZnrazsw97tEHCAEC8a17Lk5XKNhz5B2XJw/gHpivxt58vgh05go5UhehN3OArVlmclLNuPWgvw0kuIAVU0wxydYHNE2gSKbn+hA6+5sB0U5C34IJG8Y7ZNoiGPq9SdAr1nDWzG0jHRrki1i6oENBGh7o3+68m+jeqzrwpfB5mfNENfanB2vPQkrUtxQ64o7mpuLdIEOtcxRiCNZzYBHEdK5iRWJt7ypy8zu4Y73Pv+cP/reR/z+d97n3/13/grvPL5gqJl0eYY+eMTtxWN+/3e/x+efvvTMiDIE5aBKTCOljtydIvNSmRBqCcRUOOUjhyUzXr7J/tGJu9s7lsWEjcLzp+SQqNUAht2YiFNgITLsz4gp8vTFgR99+JyvvXnJEAN3Lw5Q4MHlJQ8u96RpIMTJRLrygTAuzPPCp08zDx/t2e8HgiSk+fdeB2S1tlbvklUJrrBXarV+s76/kcjpZPbh5ubIacl89OlLPv38JR99/Jyvv/uEs7M9/+T7z/j+B5/xxpN3ialwts9c7BPvvP6AlALvPHpAGLLV3daBCAwXl6QIZyIkIF4MhAcXfPePfocPP/iQk8D5YOqw1ETQyny84/r5Z0gazV5XF6jq2WyMloqgNSGhko83VmM2PuTmaeH7/+i7VK/FsakUJCmLZp6+KLz99tv83J//Bl9/8wHx8AmxFhDLoEtXc2xOSFuzbNa227VNFny7xlscJaVai41qio5PPzegZ87ZRShMLOs0X/O+VqZhNHS9VqZxYBoT+ykhKLmavH90hxVVdrtdz+wCXbRCWZ09EzgJXVxjLUloe9MCgwYJihgbp/WxFDyTgIuntUFpmTNvOVBZwRxVNYZE+1nFac6R9ddlHV8XabOzzNRCm/BOrVZmUYrRnJsdsjIRD8IkY5k5u3fYnOFancqobke81l+lBSWWQfBXQl0ROIbQg/0m7hFi9Pdf1xZYtrJlc6AJGvk574GcYmPSzmgb+xbUB1QDuXqWBitrKNX1CZqdRNBgtIktMXAdyKb42qodA03IdRUIcnpzURdMsk9dbByWpYnFhE5zbgqSTemy+0ebkoQ2HCLWQqMQiWrCLLU6lbEvHnPC0zgQa+pCWy0QqxRvGxZQMaW5WloQYQF5q7XOpaCS7L08aGsg5La3p4gwJXHKsj1FrSbWEoL14dwC4cu89DXeRI9Sy0zm5m94wFNXEauWawhei20srZaVVvBaO8CombaCEdTb4Wz8q42yS1NylSZ5XyEkA90NkIs+78XFDhO1RjdGJoxTambY9NgOG+EytPl2Dqr4GJmmhs1ve65xDJQSrT2TJwQUoVEdamnlCphITckMw0jJ1n7CXwh8bbTM4n4UY7g4O65l2afdzoVolFJG3z/rzxRd6yoVaydSqvc1dXE1tHJcPKu/CMMw9LpDVSXPBtxqNapscbXV1o+yM+7CCgh99flyfn5yNVQKTRzAqBm2cbtCXc+QbBAVO/VAhOgSwO0wBVNragmU6jRMS71HVDNFDe0KTrcMIRCj/deyLK2eJYg1HA0SSCF2AYmqagqcxYPcjhJLR2SATjvIpboyardWLpjg7QOK0bFU4bYe/UeiK1caEtgu2kQG8M1hwM46As2otu0SnZajfrhtAwqjzIbOuM3z0sehauXm7sD17S13hxN3p4wSuLtZLKMVA0spnt2NQOBsvyOiHG7vKFSGZAf12dkZucLzu5NLbxvdCXcA8uGOawrPnx04PF8YknB8VKjXL3n38QVPX1zz6fM7bq8z+2ni9dcuGJJweT4y7RNxEFQH9tMIYs2bd+kly1G5OrsgpsRhPpBL4cHrOz7+dOH25R2yKPVw5O23XyOKIkthyYU4wPnZyMvnL5mmgQePrhiGRKnw2WfPOZ4WSlwYRxAxKXkJgTkvjMPAxfmeIUam6AZsMOfjtasnjCM8ugqMQ0QJnGZTakshMaZI2mTP6XNls7tmSbxGohrC23tmOrhiDuVmnwkI1RzoTcaw0dIa0KKy0uloe8GNvrtUxNAyCK5E685Oo0cbpyqQcedVhCH6IZMCiWAdc5YKVw/44w+e87//+j/k3Xcf8x//h/8y757vvQ/lQjzf8Q/+6Sf8L7/yq/zq//mdvp8Xd5DjJBzu7viVX/lNQqyW8UgDYBnM29NLfvjhe/za3/l9/srf+uu8UV9wevaUIe2QeqRqYnLAZ4yRFCJlGUjB7MWDhw959+tvkFByOfExO0oOnF8EpmEmhsT17S3HmtldPGJ/PvHy5QvubpX9fqLWhY9++JTXHp8x7UwpV5q5FB+zZsBkVWUWmkNQ2O2sJvLs7CEq8NY7rzFnZZ4LU7IeUxcPL/j6ty6pZcfTZ7d8/vSaHTu+dnnO6XTLzcvPGaeBlALz7Qti2LE7zewHRQehEkn1CbtHhV/71d/he3/6HkOCWBajI8+FWoX9oDw6O3E8PLfawWWkOphVdTFySPDea4tQlpnx0SO4fIv/41d/n7/323/Ib37nj3gQBWREtRKkcDrB7mziP/sv/wt+8ed+il/69hnnxw+Q+QBSKJ7NCbrWzPW13UAV1hihqvbAcus4NLvYAjaRwNnVOYTEeOEqllim5nR3R14Khznz+c3C3Vy5vn3J1eVgQkHVe6OpItkQQYmB3TRY255ibT2GIYGEruDZzjXtTk7ogjG9D6E0+Y+NDWhv8EqmtNVHLfNi51SK3NzesNvtKbMFahLtHrINSNVq9Y/Ho9lvB5PaEaXV1F8bCGDj6yCWr1frK2y2TaRlVQ3AizH6uWQ25TDPvX4rhGACc37wCNXNRlydYv85xDJSJjJSrVdf9gA0JqfsWXbPQDHxQKoFtLYOYozdUQ3Oiqklt6TY2szcY5jq4hrNV7C6qbUHIWiviS2lYroIbU2ugFyLWlIbkgAAIABJREFU+apnOUst9xzdtoZV1cthtjVgbdTtiA7DWo8TN+PdAnWzxbXFpjZNDSxU1z8QCFUJm9IWrW28vI0ramrGNFqn7zMF1dRe396wARA94FW/z0pvpp8x9FKHDkyCC9esJRC11QiDZUhLo7ka8H2aV/+wrYkYoovcOT8mjpRGoWR9/lbL2ZZIaGPAKrgIlhyw+Wv1/M0njAZIO/usg6rOTgtivQ4bYGKiPm1fCzmfvJ7f7Y0Ltk3D5Nc0an8LzFuNZ9PuWIVyA+qBZs+4u68oAloty2vbwILdwIa1IEKqIHj97CDs1DZDu6flD0YDgBqy4e9B9RYfiD2HoaFdFZbaRkYJ6i3U4tBtmoixpNq6U9fmKFo6UKNVzQ6VBgCaTWoiPqjN3cnnQvDn+ipY/NJ+fuJgsXqdSKC1AvDltXEAUKWJn20LvUVWQ0ML2LS6+ANd8an1vbLNa+hG7QWy66Jq+2Dd/8oG3DOJ/iI0WgCuELYadfzZVwelURhsQ4eObmo20YpO7dCKlShZqw8wAZ7aDLFjlQFD7lW19wrU5tHT0Cf1MbFrN6GB/qz+qEHWupJmPyyxKLSaiRCsZlEloMwsubAfzcmrIbAU5bCcqHUhZ6vDSiKcTgu1VOY023xgMvp3dydKruzPR7QpO1bj7T8/zNzemfGoIbAsgkrlmGdm71FFMEP24vqO5LSNUx6oIhyPJxJHo02kRKmFvBSyZBCjRp7mE8fTQEB4+ODc5lzNiQ9i81JKpS6VkGaUyJwrebH6UTtTrV4rJROuMbTW5J+TZzbKkiFXSEb9XGbreXV38xnjIHz6NBKicLabONvvOJ8S+zH1YMEOkY1HDP1g/KIZbFkAx0HV6oPWo7qtMfmxv7eiLdJuZGt89QbWn/Pahdr2id+5HfB2MLmh94yOk7lYyW+2TwnKSQvv/+hTHj54wNe/9jrfePMh9dOXqBYiIHHgo6ef8Yd/8I85zSeGaHW3jpUSRal15qOPP+ptARphLahlYk+HG374g/eQ9IBdAEnPkAgaEoHBvcNq91MXhfFarRCF/W6gzgvCwMOHVnc3Jgw8QljyTEjJanKlsD8bXRG3kOdslKcARO9jVWt3mDrVt4U1nlpogYG6oyxY020JMA6Ri5DIVRGXef/2N1/n61+/4nSCZ9cnnj97yNmQePP1K+Z5YKmLqdNKYB4TIQxEgTEpQSpZ4KDwJ3/0p/zgw6fc3J3YpdidgSKVQuBrb7/BL/7iz7DcfI7mGeqe6M6+UqhiDZcDanYwBObxIe+995zf+u0/4A/+0T/m46efch6sM9lcChXh7bff5uf/ws/wr/3Vb/PukzN25SXlzijnhGZDldpStKyB4Y/79NND1z/WJb4Kghi2YnORRnN4olguLrKj1sKuVsYz5bTA5TFytk9Mw9iBxQBW74kFWoO3OTJxE1cB7Vuo33ndzw0Z8OdrDtarO92PJHrZRls3HhQ1wSsJgZQG6+ObfE21VjqNxdN7/DbA0Z/JxV2K3r97O4dLya5abUGujmbrWs1/q11Tb43TgzgFLdUzDOpZo7oClmKBWctKrPR4G7uUYj8rayl938RavdxCu/0RiVQXdGpUt4hlvlvbAmNO2oEnniGqrb9gsIBMKDShkAbouDveAVv72usSdSumsw6c1uagVI9O2tkqNMVOqfdnvAWBbU4g9H+z9eK/2A/udY5etfMtq2dr0dd/Ux31deimeeMPrWRdxxVo6azuswidNmvPGbqgT/vYHLZz3q7bGsI3ElPbh/dAn2jlPxaIBrK3SOssgSF1IEFrZaixn5uDA/G4zgS47XDA3VRRGyjqM+qBao6NAwY5e3Dmze1XDQshFBBpgfuavS0qPrfBWWZb0FZYgZBGpbTTMUhAg9fy9bYdLXnie6Otf2dv4QmWei842vqzFizea9WBB3xNAEyd3aCbY77Nv+/FRm9vdOBufb1OsLTWIR0poe+X5pW08V6TpVZ7qqEBDatAX6h9qVlCRiH5UDcQJTXqvAeLMW/e/Mf6SF99viyfn1wN9ZRNiGHb+NwsrG0+Rwt3m74pq1EEvPBXcLCjK2hB29iqloUpNfcDyPfBPcUwu6xvBW+q2u8p6sijuECOEBnuGXWwrEvdHOcNcQMomo1PDS5T3MRttLFtfPM6pVBCN7YiyQ9Cf9mGBDrKvt3oZpD8a7lf5wkr/SN6gXVD3yUKg983OqXjPAT252eUqhyOC8uSGRlY8sICHE4z73/0GS9vbqiuHhZTYpomo425kuOSvR/gELk53CELfXyDCGEUuI1cnAV25xNDGok6MqRMGBceXF1xNkFR6+X27PlLlhJ4fpeJcyVNI8+f33C8y+zHkbOzPfu9ZW0vh0wcCnqcqfXEi+sdosI7bz9GgnCaF3bnwbKgGeZT4XA8suTC1cMHnE5HjidruRFj4OJsR0qBcRgQYIiRGILVCtRC9nrVkjNarcXKi8MN87LwwSc/QEtkXoz28fZbl/z5b73Ft7/+iCdnE1orJzuFXY7c1k7LODRZ+Pb9Xrzf5nvjQJiRlr4PvvBpm8a/7AmD5th0XGal8RTdqEL6p1F62vebEl6UhDdZsrqMamsOVYjmXNwdDrz/w/f5q//Kv8rbr11QbmbqXJB4YDdMFBl5drvw0edPmSaTJ88ZFDtoQlVCLJyWYxeEqlpQgUSkUClz5sUnn1FyRFKizjNEa69B8MyQqGVsegbfhQSKkk+FUayu4rVH7gRXoeYJBYb9HomBnBeeP/+MR49es76vx1tKKTx585xpGM2hVgFKO2tZvLdYcIn7Wuz+IoEUBmsbM2dEmm0pFKwVyXGZCQFSCFyd7XhtuKAifOOd19AQqUtBb24JMqLBtGmCChIfklHqkgkKeT4wBHgp8Hd/6/f5wUfPiGTOp5F5OZFEyFLIEvnmz3yDf+Ov/zXqR79NrQF0R9VCrAsSK0WEomZXE4G42/FieMJv/+6v8X//1nf45MULGM1sK4VjLlQZ+fl/4ef5T/+Tf5+/8LMRXnxGfnoNJJYYCRSiA+dFei7i3jKGjUOL9Nrctd5ON3vB1n3Ezpt6sixVCUY/jSERJCJDZAyRfVQepgGVkcIVmosBeVWsP5kooURaNZwp2zanlPUZxOqrEuHevhPEguAODPrvrnFlz4psX9rc15X6GVpWCmF3dg7AsFW9FKeL1tW+a1LG2mq+PHALQl5amymlbfeqJhRn2TnTCiglczwdOY9nbQbWTJN/WnAXB2uR1Bz3eV6s1+w0IRSWZeF4PPXsVBPPAdjtJgsYg9Met5msZqM8WxdCNQVfH90mJrLMM2kYjGkT7MwNpI0ts/vtpsno4HiT82Drp+W9VqjCFRtDZHTgOHg/5ZalBNBaepCgWok+r1YHvqGfus9RvdawldzIBnyutfR/T7Fl/gyNatfRRr/29a21UrKaHzE4MFBbsNJCGL9vLmtQ4rBbA6xbgB76KROcYmj2PcboSs+sAjSC71ezCRtHh/sfuT+fW8aFz69gQbVULP3pgZlRk+O6BpIzD0IBgtNRnYZcV4DE5ib7/nQnSldGVg9e/Zkq4uJsLTjzPSNCrgVqZckO9QjkkmiUXPq1II0DtRZyheD2KgShhoxKITOjxYEbBhv7XhZiIEsxCVE7M1rA6rPSfNyWJW37397T1liQSBvcQETq+ntAB5B60KqNteQJkrqKqOVlcTAn9ucJPgZaqxeTWRmLPZLv3bxYgO9quOjiGX/W4FhW3wSaFgKb9dio16uaqnq5zlefL+fnJxe4GUfvoVhNNfOeI2ooHCKcii2s6Nm4RudZC/Ntkbcaro66KR25aYeZYj0Z7yFr7Y7te9FznWIqpVUhxqGjb4aQFjuoW/CJmkpCQ/H8MbabkDT4fTaLWe01e1Dn6rCNViiyBgSN6ldlRWXBffIfFw/Ithmy3PsdxQ8135xS1QUILKgVhKyLZ84iDx+cG2I236I6sBtH4IJ/7huvG43JD1WthaqFNCZOxwrqBfcoOR+Zy0LJyuEwczzNvLy+YaZye/eCp89uef/pU8Yp8a03Xuc8PaDOiSFmZF843ibOpx0//Qtvcr6feLCb7NzQwrJkTgjjPrHfRb7znfe5O2bOx9fY7+DBdAv6iI8/veagme//8FMOh4XD6cgyZy7Pzvi5n32XRw/O2KdAnpXT8Sln40RFOB0PnObCsxd3VscTp47IjSM8uNiznyYeXZ0zpGRzrAvjmPi5/RMiUPcDp3lmrkoYJ15/8hqPL85Iy0I9HCgaiWmCeiLG2AWetgfX9tNVbX18tTY6ynYNONXrxy0Q7q/PIPa/ds9GWbv3ux6wtv33alPuXtchqTtcNQpZA+U4E4Kye3hFHUf+9v/0K7z9+DHfvroj1CMvrxfOYkTLyPy1N3n28pY//r3f5e7mmt1Q0AVgXOuONBPq7Fk7rAYiCTlHasgMVljH9fUzPr0+sjsXogwmPOC09E5fCeZQqQNMKtaXcCqJysxSAvPLE6oFwg4JO6os1LJAtczkg7MnPH9+x+1t5ezyAfuzhC4LKRWoM8JIHJPV7Ip0xcUgkTgMpDRyPBwMFQ7Wg9HzEC4QIoxxQCtcTudIwWpmX8wcPOsr+aXR9iPIDjQUcj7rvdVOVEIVwi4DI0OAXYoc9Rt89OwPmZaFFJSYI5UApTARCCFyRuC8BPSFItNETSdUrT9qrJEaqvVHLYWyv4QnP8V/9Z//d3z/vT9hvvuc81GoS2U5F063lX/7b/5b/Ov/5i/x7/2Nv4x+9F1u//BDZBgZdgMhmEpgyYXC4o7wutbCj1nPfX9s1mhzoJrEe/u5uQGDuCqqZ7qyZ7iqRJTKssCSZ4ZwIIVCLYLoiSEmpmln9ZqUrl67OmbmdDXaoXhwUJYtkOhZKcr99/B/3wZeXWyjv4PNZ/EAwlTF7X2Li7pktw+Dt3haz9bFn09I42j3a/9chDRMljWoDvi5fbm8uPAyD6PJzbMJd8ynkzeBj10AxQcXuWhnprWDkmCqkoTQgTWqoScx2nPOJxNjK8XG9XScmeeZGIVxGhk8O9fUMlUNWM65kstCHBLL6cjheCSlxDgMgHBzuLO3XxZOxxMpDuz2O2IMHA8nDscjecmkYWA3DZ4VtuBqnmdSsn6xPgFmM2M0FXARA0WD0e6js096CY2pApG1rm1aoquni5XBJKfV7nY7v4cB2dlVUofoipeNvqzeK7CFCroGM30KAgyjt1nSVuPaMputvtAF+YKL2+kaPIizvhq7q2UOrYSokIKt80bt1FqtdYinJFXdJ+vD5gFFvF9LXOrif9/sVfU7lcWAM7X/yHb9JrjS/Lv2DqpQlvKFM69RZFuGUjvAQ7/nj4szTNyodpvd+iu2PQqepPBnVi9Hij7/21rD7MJRo6v353kmaUQkknaRaRx68L197u1/pZR+PXsXoWFSIVhd/JIXo8C6v5ergTzbjGzNBVR7nef2/c0W6Lq2sLm1FnXCXDJLqUw7FwKi+C8pWauJVgUDYYM0kb71PqZiGs02DdY2pbWQqiZF3ecFBRUx9VotaFmcPuzroIEldQUcvvp8OT8/cbC420cs17wiGNX5yU11DLAgBGvMLmpo21qX56l/CYRqtC2zZ77Qg0tpqyFNDeVrB7Fq+1ljpPUN6c/YkWvvk9UCrqpWs9KrptXodo6N9UNdVLpD3ahLW2lxq4/cOAidI78Jb7V65nV1lgxhLX0EXv20YDWG6Giu9oO/jY90XNGRP/Fead5vI6yguDmApbBUM0CL02ZVq/P1S0fWu+F2NQkTcDAkd5TAEhb208iYIuf7HRm4nB5a8Hh7Q9bMOOzYTRPH4505dBoZY+T8fMc4mmz/sixkUYiVIpWhKhwrhznx+ms7ahGuLvcMgzAMkRQjj64u+OzZLRe7I0vJzCVzfnbJfky88+YVF+cDA4GgiSxv2rpbZqcxC3eHhXmxgzGESAiKSrW+iyLsUmQYIyqGAhpdOZFrRY9K1MREJVaQ48zJjWvNGXXngFrM6W4ngB/ZK6LZ5jj0ubba0yaMo+saaPmLnj339d796hV4KQ0F5z6Q0oLS7sC2PdEQ2x/zqVQoGakZHax3ahEDWD748BM++Ow5+/0j3v6pt5AcocAUR+asTFJ4+clL/uSHT/n+Dz83IMMz7qVR1sqKjFNd1c7ro2IwFcogitaFF7cv+dFHn/HgnR2P00C+m6nTCkqpCqGs9JXmIJnyrKDJgsfq2XwE7z+oboui1z4VxilahoEjt9eF+bYwxEuX9jbZ9RibXqKrD1flcHPHslxzeX5OjAMln1AsGPCyLB9T0xU3uXMbkxqKBbkxIsnGgyDUGIhi4lkhqK/VQCiROEymghgmliB89Cd/zPNPPoBQqQGyFEI1SnjOygJoSBASkhY7+KsFOlmaLcAEfSQAO6o84EcffUQ53gLWO04IhHkhaODtrz3hz731GH32PebDC+LZDglGNc5F0eD9tdT61vaa3K3o19bmucPWd8A9kGUTaDXkGwhNYbUaqBVwsZhq6n8vXpy4uc08uBy4OBtYlsXUE7Vwc7hhzoUwJoYIKQopDe4cFpsrv2NTdDQ764+v1eZAPHOyOUv6O7kz2DHQTrMyWxCG0fpxNhql0DNEZh5a3WGzCRumSbU127IPgljGvSWA8LraNqaajTKpLXhfM3sltyyNdvpyfxU/81SNYTCXvOmHJt57rVIWA0iqburMEMZxZBiGTbYDB3xCd6pb+46mTj4OA5MHXe1nxmnsNkuv7NmTq02P08hFPXf6eYR7XTADecnerL0Bz+v665nADcsieAawAccxGj05iSmt5pIJIZriqNdpNaGglFovP7tBdmZO3CyPktu8FQeSzaeJ0TM8LbvXplotm6pYH1IL7AwwkBRZBf9ibwbW6uzoPoey7iCxTDFqtthQZ0Sdxt+AdT8zOkXX/2vHRveTxPpL4wFjq6e0d7LeigHpgoRgmbn2Lt2f8z0Qhkb1b5lDC3bEgQ475TwI9Hs6GaCvwbZRI8Gpj7ZnzMW0sWn0V7zHtG9kwBINFvvae9VSSePY1febAj+IgQntYG/z63tHN+Pf1r6JVq1MI938m4ZKrRmjYXsM6yq8LUtr2VYPrlq9cru937d93Rl0nlio9k0r8fKvW39WoeUNmkZGy+yu9PHmw2i1Post4F4pzVt2gtnHleWg1nsYx5hQp/P62pL6Y7zhrz5fls9PLnBT1ZXYmlKVUzx7QXAL0KLXOWSnAqgjIquYTHN2qyNNdl3rO1TVVJ+iy/2qrghH62+2rZO4B0htDsRmwGCt0dJXnWW1xd3/6v9vFFP73XrvcLc//e8l92921AiraQz3YC8XJGl3aY7zBgkS6AhYu96aeG3f07Uuw41qobryZnC0p9AE02JMaC7k1ujbjia7RgwuyuPD4EGpFqPSDa4QOjotIUhgl0ZiSNy9cUOuYI3OA6KF03Hh7tYUJ0MYICjTlExdsQbGNDEMgXk+AokQTswu7//w8Rl1WZiPVtey31+wOwddBh5fnRHHAQmQy8LVxROGITDF4mXjESRD2NsBExqC1aTPbUyDiNUIOn1Ruh+ojuoHR/rc+CUDO0yYyQxtPZyopqjie2JGkB64uWWnyGo8Vxlp+oH1yiJkDSqdZrwxoVsnogsm+U82R3FNRPgJuV15m3vagX5fdAQgYv3/NFjgLDUzTQNh2nH34sTnL47sx4mr88kDwcpIpYaR4cEFh5uHfPj8wJ99eLQDMDutx/tPRqxFRlUo1Q7FGIwuVkVZUPZO/bw+HXjv/ae89eQdHu33zC+umfZWuN/quHqBxPY9o0CsiA5emzQ64yBSgFoTMjhFB2vtsB+iBW2SqRXmYQCyUVpzIbCQBptrU8O0ObZnMHDB6tksIE0pGUrdGAFq9yt1AbEgUbQQNJDrQsYaa5dFOb2cKaeZaT9S6swpz3z4TLm63FPuTux3A3MJXN/O/Nn3/ojx7ocMo9GqllIZa0SHkVwsgxixgChH63UlOaIhUIIxE9aMnwuXMfLx808Y9cighjIPIfL8pvK1t7/OX/qL/zw/963H5A/+AGVCU3Xl6OT2sVLV6G2yUbm7hx2v/ofN58a5/UImfuOsNLfVAnIQGZw6XyCY9H6UwOWlMA7CNGSGcWctNkok18onzz/n+YtbHr3+kEQmBbGecMD52d72XAdl7D+jcK4Ajda8Prfb6lryuvf9eW2/bcEgQUS9JtADFaWj8tvToIU81SOENTtUmecGTvrNqovi9DNBXVFSvL9av3vPOIZgDnhxIaz27JZh8cBOVoaJqo25CVbJBhRbg8C6+Z6puq5CbfdzB1uncq2pi1GI0Vp1NDXnJhgnHkgXz6gBTHEw+nQaPIB3loYapCBnkzvXq+1vglvzslhjeR19Lmw+qwc/Zl4c6PY9LxrW9/JnaAqTpeQOdtg6scDFgjK6H9L6srYMW8uyGqa11qK2bHdVA4ONgbXZF9jv23NEhNCD8ry4mJCr4pov5SIqQlcht56B87rqxBTBq2fB7vtU+LnFCrZ3sRPpWfRWT2fAmDpgp+scIiC2VuK9SAfrG93WtdhTFZ//tmTuM9k2S6k7ULqWh0rbP5sNjZ2MuX9rjeZFWyBZvJzKnuOzT56y5MK43zGNI3k+uW1YS4La/gtNlLC9W2hn71pq1bKYbVe4u0ARo2OujATTBtGNWJ2tZ3/kHpQa9Xi7NhTtfWMbgCASiNB7NDqxvz9b0yOh362VmjXbvH5NxRMLK/21uoiVzbUjGz781Z+jNMum67N/FSl+uT//DK0zqlMwW7Coa01gcMMfWmpdvSYAk2r2TdCyge06VONjq9MuSi5+SCVUXUFqg4D23M3GaW6Ng4GOluCBj31vG3y9YgW/0BB0RUVWO7VSMNrh32moLYigMbKbJVrvq4onOAxdagd0KyBe94x0vvu9WhK9dyuzLiH0MLM9b9i8WncQCF7PUPtF5NU/1QKq5iyJ7+LoVPno1NpuTFzxTBFSHJmGxDQopxhInBEkESRRJBMHy3pqDUxjsh5UtSCSqEmoS6FgMue5OpWpBm4PyiEXr8ep7PBaWQlIqWufo+bAOFVLgSF5qwetqAsDtYMsYrLzrX4BR65zLqQ4mA/ocyyjQoGoto6XsrhPIhjdeFOsrmvmrB1wazZ84wiL/Hj7+Mr3X12V28OuO3gN8dP7ASWvrCvdHAqofvH+/r4qFiyKWm/ImHbMi1qLi92Os6vI4GOKghRru1Ik8P6nz3nv46fMy+J1gg1NbY53oFXWdHfR915VvLWMrdiI8KMPPubpNx7yrfOIykKoLfBw+iawiqWvNHIEyP6OLch311Vc5l8ClsrE5xKIIRGHkWkUlnIErLamuTCrLbFgexiSZ9WaEFellMUk2Guxlj0r5k8Uz74AiGXag1a/utXS1bJwvDsyl4zKwpwzh0PkbCqcTkeqFg53lZfXR87PJ775zdf5znc/4/ZoYEcQ9fY2QkqR/RgIUqzmMFgPsb52VEAzGhaUSggVOKIUTN7LHZ8o5AyXV494/PiK3Si8WCqyi2gpqDR7bZ6B1cDU/m73l9krwX0LlF4JFNfR3v5sHzwLAKR2u2aZA8uCnu9HzqZoQgoCMQUWAjXAbjdwUUbOdgNSpcuQaMvkbRByEbp97da5gYL+3ea8drGo/tCNPreCo+16LZsWRdpFbH/cu7KNi43h9nQArdIVQQH6qvK6pP5MKrY/WjbK164veRrVFxwwUH++dsbcex/xgGaFG/27LmKmLgxj32tPzSs2iWanXpnc2rzK/u9OLW52CfM4G3WtGTCr71tVLNscKBWpoVGP7H6t7EKtnliIBK973BrE5sRXdbvZFCTVGRst6AFvvbFqN/T6L//d4sFeozdbCUag1Orquys1cqWCtoDAqIKqZicNCLbxaABobY67eHkBsHj9aoz2PVN0rat99D8iwlJK9wGCA/WN3tkyl9uREfE9I+JBvQc7DXhqcaQH6ZW1BGfLiOnjvHGyiq5ZRlFdbcfGB1r9ofuU2JYV083hYj02aw+a3CFrl9r+spcGtvq7dh87KJYlc1oykgZiLMzejzU5faTlyNraayVG7T1UzYbivpVlU9fMWlv67h2ygkV2jbX+Ee59pXQBKiT25+6/+8rste1cHTgxP8WD/QZuNKEsfwnd3FPbQb7xFfrYq4Ne6ibFf87GpBmTdfw3UekX5+Orz5fq8xMHi8up0lTCeqykavU6fgBZ829zIGJKvWbRjxZzaIBhDOx3ltKvCsdT5nQ4cXtzZNxNTNPebL0o4xD9sNs4harUurgjHvpmacqgxjhtlNI1UOzqYP2tmgXtJ40t7UY79Y3ckLUtUmwUkXtX8Ut5PxndBgyrOcA3WKeN4AeTO3EddAOnYtzPoCpe4O4PG/zfe+0CgVb8baiiU/Gqv18QpJrha4eA8daT9R7zTHGphYYytQPhOps4SZmVNCW0XFsriSkyjolT3TEvC8tyQKswpIX9mTWOPp2s96Xq4kZxT6lHcj5Q80DOwu5iTwiVlzdHrp8XcqgEqdx9cE0KgYvLHYcrZYiRIdhajHEwRy9W8jyjzsEPEswhDYEhCkGUIUXCkJqfZoigCGhFqvfxCpZ9DHc4TSe7QqbNXyQQGr0s+prWH1Mv6JRq9fXV6yRks2Kao6y6rgXV+9f5AsixGvHuvvl1zVa3bLFunGyn1mildpVHc1pVHN3EhECSVRUwV+X7f/o+f/b+R7z99mv8pX/xZ5mf33CaFywAixAL9XTiV/+3X+Z3/8E/5Xj3giEKOF3O+jIFy2vbl7SAVV3BuKgSSZBtr0wB/tf/+ZeR5W/yC//RXyTtCsu8+PgqSAtnbPeFaFmA2mo2ykJ1aqsqBC1Wk+ES4aZw6DQZEoSFXKNRtakEsdrAcbeOYxvxlqUWEVKM5Gx1YLUqSy7Myy2oUEZrTpxiRLGsiZbm/EUQZQxKpRIHE1062++5PV8/pxdAAAAgAElEQVR4cXvk6mzgbJ/4cz89MmgFeYsXh5kXz5/xllxwvf9Fwve+y/Dr37NAM1SKWAbxVJVH+4mHV+ccS0TzjjwVYmzURWGMO6oGCjMaAjpFdCrE+YREQ6slCGWMVAns93umSVBmFiDGwihnBFWyzijVBUhWB1RoJlX7nK92bZtxasHY5utuY9e9BOaU2Ly7CmG1mlCy2z+8NZBMZF0IoowhgwS++c5jQnxCXcyRbbU4ijUPFzGqlGLZbhEluQhKew47z9bsEkB0dVE279XYNauval+lYOeVbVbf9+pfuwphqb4uUoSyMgFUTcFZvH7NggJbS9bmQixIErMLpdVIV3MNa7XeerkuLdzxsS591OkZKtdEDmKiL8HOFUX7e7VRKa28wkGDlqFrz4GfbQo+dmvttiBIY/uI3FsRKa1nY61qfXIj/TxNUchL8bYf/j79/Dd11NbCAFnP4hgiKUpnGW3tZbebODPFMzu087XZDd0+qQLBe/Bi1MeWURIQCQYrCiCVGNRatPQZaO/ka16rs5B2/m82nl20LyVa+2iPan2UlLBLPXBHrM90o/v7QdR9/laHZ9/zQC5a9qudR20sSuvD2YR98HtjwYdKG3PpcphajDLawQjPjIUQ0eL+UV9Jfj95ZWzdJmg7H72tTYfZdM2kW0ZtDaSasujGzfJYp4Vcbf0LSGVZ1nVsB2flrTffsPVXnLY97fuYaLdFHkA5zbcvfqq3k1jbehgw1Z1YrHZwY+eaeBsG9tQGHoiXJPo+AaguDBaVrny6JijaO9r7Nk/O5k5par6C1UeGzd4Du17O3otT1gx/84UJ1gIk5+qgn+tguD9f8dYvXgdpfu16DlhdrLFq7kX+X32+VJ+fOFhsRXE9te5GYKW4qVMd/NBwnn4UsYNPhBRbCwrhdCrMS3Yn1hbtvMx2WGu1Hn8xspPRFViNbjENkRCEXI0CYsEM1Ix/r1g7hODoTzFjG2IkhkZrWakbrQ4idONuxqIZmIbErB+xWkuCZZqaM+92otEt+qYIPnYbb2kNQterqqpnJPyaFs24g7K9u32svjFb/B5sY/rRas+oVl9nNTbW5iPP9i7DaJm6Jg0d/DFX/rli6mCWM41pIBCtVixlDsykYWSWQi0zZa5kbIz3+8T+HCiVMSmXlzuE6A1mM5BYTiCMSBiRcEkYbB52yYxo0UtKNTWyfYocru8oQEyR02JgRJOIQM1WzQE0W9sPwVXXihmnELSXr4gk5tPi9QKYmpvgdKYApXqNis+103zEa8KLi6tgYTkx+KGj99Vstaz0aVtIoR80fRJls7I2WZZt/YkBI/fR+rYHtfkq/bAVpKw/s9aaYM5DaMX79jPVQZKo1qNMxWrVdvvE9ekFL24+Z7dPvPvuG8zP70yIIJqQRiAQxgvGt77FT3/7W3z6yQt++Nk1x2Wh5W0a6g55s/ZrbxdTRUkKgUIhGeVLCi8/f8bL6yNVRnZxpLXjbsqQXWC4H9bGDFani+Hr+d4JGBtlyp0NEcuiFqvhbXnbwiZTsknXt8PSatzsWtJ8sBAYd3tTn5wXsgsWhJgorrxXS/HrZvDDtymsxhgZxonL84n9w3PGeSHVzKxHKDCkPQ/OBy6ePCaz4//9ez/gl3/l7/Ph0xtiEIYxUhZBYkFFOS6ZZzeVl/mMh97PVHSwAD1Uo8KKoDqiFCQm5OKKUCpCMMKH2vtXhPkwU+YTQc4Yh4GRwecKBgbMz0yOhJvN2ZAVu/1sGYW1v61s1oitl5V+uk5edSdFWtAp0ca+SfuHNeduD77QhMmK1/rNt4feEqDVwbeApgF8nWjS2mRIc3JX50s31FBbF3jZhA+a7zWTSYweHHttTtDuiNsv+17sjnPb0M0BLd0MtFYXaHC/rDFytOmo+hng71g71Gc2zC8UmtFsz087X9q69zFua99IAZaoJxA3TB6fHLtOq3Mrur5XBwDcOd5SWJuta1kJ1EkzNi8tu2Vj7iUluq4hm7+Wm98GGaGfYz2DtW2RVZujrG0C+vP051qf0kJBuZ8Z619vQTx99bfwwKV8gXZJe7btz2+XveLtqtocQZTQ69i0tR9o/nu3h2CRJOu49DusqTd7nJUqb2VB1andfrbUzTi3wKxRKLfv3bJfnvUVWpbX7t/E6pWWtfSMr2BaE9s1IKzv6HsmyKru3bKkxnrfBEAeNFYXAdzOqy3uNajrwGtPANj9eg3ydiLUEyGh2v3cJvqmplk5J87Si0h6SVbjm7nNCQ5qKl5itdkrbP7sY7RZdz4+YbMO++/I5n393ewRtbsHjdbdabAtmH7lvdt6aGVYdqE2UL7Hq6w9eytAIXh2toFUNbcSLQNAGihjSQ1a7H//jP7q86X6/OTBItCc2eKp61akDc2BFSB4htFr6BxhqTUbWagFZmoy21UrMQYeXJzx9htPUJTD3R3DlBhSYsnmKsYAIia1XQqkITBOyVXKxIUl2uKU5mcjiCmvLQsQu9jNccmowqLF6JDeFHlIgwchK+LZ6jKbOpXnOkmxVVmv2b+GPK8ZxQA5cE+soO2WjgLi1Mp8byyNIqpOlXDnJljfm+WkJG+grgVqtsxvkOC0FhhlQrGm6SkE0i6CCpIwYQhHsIc4cXNzB7NlTFSVXCppSJSSOR0PVpshC8u8QErMyzOWuTCkxBuPH1BD5eOnH5JPGc2ZcYQx7fjo/R0SlIvLgd1ucMWtytlkSqtzVmYtlKKkMBJlIKSIUrg7fcrzl5kUz9jtBrsm1rcpDY4HpERdFnZLIKWADMHap9SMJOPeDzIQJBhnPwrjmVCKIgSrrwSsR4gFk9SR06kwTSOVjNZCCEZrs/5QoyHGpVgfwA1Kbduk1aO0NYPXi7h13BwE98816wuJz3urZVmVg9fPq2I17V97P0ARR+LdwRUTmKhOWWzXW5ZsymWLBfPDfs9nL+54cRgYpje4uoBYBmqYWeqCco6WhbrckuR1fvPXfov//n/8v/jgvc+4SJFRRqqePGAP3WkTcYfH053Vs4G7OIFkAxLUlAZzzSzLwepFCNzd3TDtJu4ON7Q66XbId5XZENc58dpmm4t1PLZ1QSEEoj/fWt8iXcX4fg3RmgFuAgKrkJDVLo7jyBAiwy7RaqXuXElVtdF/Wt2oXX8cRqoaVez25hpECDpxxwmkEMOZtaDhGUmgLI/408+f87f+m/+WXRIup4EqmSwnW2sZHlwmTsuBv/N3/wCJT/iv/8YTXtw8YxExVVANzOUpcAWxEPd7XpSv8b3f+ZA4FkQLUzAVW+YMEV5753Ue7i+RF7OTP41K10oE+upTCzxCWLPttlbXjGNbrWYCA0HakbS6tfcDyI1jJJu1rf1SG6d289FNSwRdzXkDBNU2y+ZnNl5LSOu8NWlI1j33ajlDisHKuIJn49QUuXMXj7FNLlSCt0QI3sbn7vYWRFw8A8iZoEYhTJ796XTFEO7t+yhlExCsz6RqQJjRwdlkxN29VTXBlPbzuYGosr7fJvurgDQhpm2rCNYgbK10aNdoT7Opd3rFMewBZaAHqv3sZrtv6UDbev37+7NP7ivrpgU5fc77vfnCz957LuhjUHzPvvpzPVrZxvn9+2tgtu1rh58L9zrM6bq27a8r2BLa9mo0T1r+ag1It1lR+56DMA2k2DxDs3WvjqN7bv7+mzEXubfmGhjeAppWr9rAubbW0DUD14KZxn5qz1vQTfZxXWuNPloVE57x7ylroG7LZWMPfLD7urAX8fkvm69fCape+f72zzVQhraON1NG75Pp/lybl7YfO2sI2x6mEhrXoIu2l9bLqEJI9HEvxZMug/kAjWpstdTWDmP1JTZg8GaP0vfKKsZkVF3t62Ol90pzo502X53uvPqvUiMRiGIqxCXXDrCGus5B1UpuzIMmDqktO+9D81Ww+KX9/DMEi8pmZYEf6vfVFwFHgqQf1o2O0xaoON9duDseAcsYhVyIi6Huy1LJNXMKVs+m6jU34tcTyAuW5sYkhlMM5OKy1aPVrYUWlKGkIa2WKJnipqJNc5WmcmabNFBLU2Ztxlj738Us/qqWB7TTcqOivDqdTmugIVtNZKQbu/VQaf1r1u+vVNb27up1URo8OxSNXtZQtOq/F/ywiMGoMHlZKMXFLjxzGGJmGgakhhWJ8sxomQu1YkIhIZGjoEV59vkd81wQieynwM3NCdU7rl8eiTES48TubORst2cfB3N8oxDSyLA7Z1lO3C0nWtN4SIxjRLQSgjLuAyEmcrhCbl9S6oKBhonzs4FhCAwN+AoDMDLsTQzJ8D7LbpzyQilKqVaHNo4jwZVMOyVZrWi9lOzZIFOkXWY1GXRPoicPFFN0mo1m1jrUVz9bz8G3zsahUF0rotjMbVVT9u2Bjj+jUZBeORhfObxW50zdWQ3rAaBOg+qH4tbpVopmAw5QRDOHw8wPfvCUy8s9r7/1CGqm6mDdZsgIC1CI+8TN4ZbTMhMipCAcSrn3LP2r5iv6PUVxNcCMeOpCpZpTH6LJ7g+JqoGURmIYmMY1894PYl8H1R3PrrhY2378YqDdtl4L3IKwmaDVAX01m4B4ztSfvYEAvYi/lC7A1T6N4tMQ8kbFbwHrkEaSOvAjgrUCAahIMSGCcjwQ9iPzNHIKhQhMydotNIc7eaa5lgpFuLy44slbb7HIx4jOqESbZ62EMpDrkSqB+fbIb/393+E3f+9PIFhD9eL1UikkBiopQKkzx6WwLM3BsLqXHjBKMae6NpGDda12RcSN0rQNa0F6BkWRH7eVNuu7gyoeLK4BxcZx2y64dsHmcCtr8Ib2b7dr3g8mts/zimO9cSKlqf1p7kFYyUbdXHLLeNo6mcaBXCqHw6GPR3KV0GWZ7feRjvav5xcuWmI2Yc1qKYhR4F599+p7I6jPE81pdccubmiyzRleR2P9hzZfaActxf2A1twdWLPwPa5Y86/qP1+3AdHmFdaBl37rFkeuzrtszKqN/+rAboPFL362AVGzubSygP+fn1dVs/2ba2739f3MaVtN96rONmEDq7/gv3c/O7R9h+1yXW1Fv3cLbliTv5X713v1Pe7Zo3t32vws9Hu17zRV1LbXaSUMAuqgstR1bGE7Xz4eLX5FjGAl1vNRBPNPBFcPX8d0Czh0eucrZ534NftCAffj2g11PWe3wfQmKP1iALmeAf7P9//c/l6/iNz7nm7/bXOv2v3l0hkp/Wxhe33dvuoaXHmf8l6rWDbPXNdMtd37vv/RHmW1ob5/Wi1vpzNv5q/NOQa+25nbwK82ti0pswETnMJfdQM22Ytv/Ni297f59a8+X7bPTxws1mrZL0MxNihDawngJm41TLYIgiMKsDphChCMhhWwPnmV2tUaU0jU2RzYUcKqgsrqSJ/w1hExoJqhWiZMVbm7aw2010xBCILBqe3vFpi1dLrFosFqLVMlTKuD1xDURmFpkEjOk4/BSrGQddfZod36I24M50pVdYMagsucC4Xae9N1p5iGxEs/eGJKaMMZvdjfjEA1eX6p7GSwOh8x+xEHIQyRMmO1FQrkylIreS7EIdGC/RBMEfLm9tZti1BPcDplShaOC1wfb4nxwKyFPBd+8N6HVA1UDby8Xii1cvFwT4pO2S1wtt+RBmFeTsxZyRWOc+Zsv+Nsb+qEp+PCaZ4JaSCFysOrPUOcCEy88XBkvw9cnhk9OQ1CCDtO+WiZrGIS94LVE9VqzdFDDOz2BWpmjBNpHIkRCxxDgJCsSfmpoCU7Ai+EMCAaqDUQEVeRVe/JpMArwYiwUjhe+TQnr2eOu5NrWc72aU71uo+2Ige+fjbewsZnWa/n53qjUNn2tP2r3t9LsNq6LEJQy6EEnQk6U453pIvI+RgtQxEzskTMwYchTJTdwlJuOB+EQ4ostRCTZbrFaxLbe/fDoW4OLHfastr+NLn7TJ4LWiNjHBmmSJLBhFcajX3jOLUhKl6g33qVNjvRGmNv7U/fh7KiqPTA/35gsE7e5udk/V1tSnbFqEq1C9xI399gtV2iTT3V6fKlMI5jnx9B0GjUfcGCiEikUgjTOR99eMv3/snHZvcoUK31hUhzMgJhwWqCzFiaLx/U+zzaE51SIlTlrlzx6cvMb//O7/Mbv/H/IJIoNTJMg7U0KSYeUqtSJRJGzNbK2kewdiEPW0M2jsWXm9xzEBq18H6rgJWu/YUxvzf4ztTogdrWeXzFid9O2CZQ+MJV/Z4NNDFA4xUP0X8utMbzEjbXajSx6hRpq6875ZOJgMTQs4G1KHeHI/MyE71Hn3grFqWSfE/X1mqmsxNsXRj7rQVJ2tfh6pmutDoDNez56r33hCAJ7XRV6fPRnNBtcNTC6SaCITR2xLY04/6fTZnVzqXVXrVYr19dmgPtICprs27h/2PvXXotO7L8vt+KiL3P496bSSZZJOvZVV3dUrfUagkWIBmwDQOCB4a/gWCP/IE88tRT+ysYBjSwIViABMstqN2SWlWlqmqyqshiMh/3dc7eEbE8WPHaJ5Nlgz0QB9xA5r33nP2IHY8V//X6L5ALkpUtgC63GAyrHdiXZ17MpY0S07SNQZlq63u4poLwNjc7CO8F37XL49HgIBTWSKq2UB5ZxpBhrqGb32v/SmkDw7nVQ6s5dybMqhQNj2vNkO1aGdfHW3XlQcmp60zzeH11IVNUiUFZfmNNjgqzllyX3i8Nz4ztKM9v71InhQI6+GOH6d/aV7zTbd8pbXrDczw8s+gx9U5d4WsEMqXR5WSTeWO5mT4utYRc1V9dlQ8osX5elm7Hkf2oXt9RFnZHRcWN9bNC9lZynfv86cpcY1BtMrjLOwv5rWMm7bmKlYiykGSaTKzeSLtHcVrYIFJLcVxGEKjIG/3UcMg43b85vpbHVy+dAW2z3oSfDNK/fl6tt9U2cWmhr8LVi2sgWIYF5mqeRBUqrvxDemmEQk4gzkEqpAClrs+ypMJSVxRDqSU3auyNIE4La2tZAJnCrKn4GmxVrg/Owj19KCUTymIVF9pmrgUoSBFoPek4907q2LEtmCq4bOHZ4q0Me2NtPovVoTA6DlbaksvhneDFCDWSxBKOaDmHVVD4YAA00b291tEe72CaQsubVDGlD11LrRyBJOwnx263Y/ewksU8b1fHGd3B9XFPTJl1NQG0pMzDEgnB4xPkNUFeOewDSbOFoa42Xs6DD3tQ5fbugcfHhYhwfbDyG6KRtJyIceLqOHFe9wQPIWS8X3hcHi2sL6pZ9hEO+yOqCQkwBbhPJ0Qz66zMKRvRAdms9j7YBrwaaUmtB2S4SnDRFYBn4To16lnKfG/DqtC+HA6RLZAZrac1vKqClgrQ604qRekbmRdHoDve9zKMZnxWbWQugK6HBCmWB+ZAz0zB8a1nVxx3E+uyMoXJvNk5IJMDDagmXt+9xmnkejdzGwL35xNzVVDGdx+gUcMi0gRK30DKrz4Ey0uOEZaVFOspUtbksGikAl5t6w4p4T7Y5p1b4kzfUKvBqO7erU31x6V2sWFOrn3cIHKRP76FZdXP2/pX2gbf6/jlUpetFN2WjLi5gJ8EPgHeSgS4wOPdLY+vfmvkFJqKRd7+GW1LnTJWGy7GBceESMAYIO29V2fKz92t4/kd+ONTfvD73+Xjn/+a81nw88w8CZojT51CTpzXiPqAnwQfSllC1VamR6AUgO+GCCljanJpUByKgjT0JFXZaZ8NY1XBz6WSWM/RftoWHIp5Pn6n/bpZ0HWzdkbP8ts8zVXpcCLNyAklJwePSGolDNSBqI11jJHdfm9F3Z1jXdfSuh42XblkU+5jmuVtuXO1rWLvWskyLpSDbOEhjYG2GWypodHdL7EBnfqmctE9wUKnTx3Vm/bki9+2crJeU5Wn/lld1sr25AvQOX7+Nq1Htq2h9suFYa+3cWus6PvxZROG/q/KlOjQ7uEeVeYMXTrKlhFI16s2bzJArP8/mLo1V4YH1lu9TWFU+ngXQ4gpHkVi65s1IDegf9NU3Xzfu2E7D6pTod+Pzfhddtn4+eVj35AFTR68pb2bxtY29D24n3Zxj2HOjMrQ5TNaxM74Q7r3rculHlXW+qz+V0J46992q7zZm+qz2zBs5OS2K6oS3BQ4VbR6izdyrb6DfWXlWXJjQLaonQsOhjbhteW3Xk6NyghbZfd4Ql0xX24g/Ob4j318ZWVx9Bg2CwjbxSgi5NXAj5T6QArNUgFFQBaUFaQwyQkgDvGl5qIAhXLYOVcK7wJaQ10xbja1ot4WUuKaxUOEwoZqtMLVMxmmGTRbjccMeKN89s5ZErqA91brbzlDLnWsgnh8KKGSroAdFeYZmteyhMuK9xebNTQGPAUdrGPNOl8V3+LljDEaAHGF8U4hxbUBFMSUXQG8y3gnXO+vmKc9irLEE4nV8tEK0lClEUTsD3tCMJIb77IRfbIVUj54Y2378N0CeI35SpPy6vbMy9tHnj0JTFPg2x+8hxPhux++gxKBhA9PjGFTo4XereYxci4RJshJWj07EXuWd0Yu83g2hsqff/ySNSbO6cR6Xln1hExXzIeJ3TEgmojLI4Iwe5gDrCgxWy2qh+WBHDPzzgMzj3f3BOdwXvHONQ9g8AEfQHPG4fFhQl1iXZfGajuHYDmcyw53dSA4qzFppQekgZMGicZNUyzPtFoGKzNqY7At/3LO5qGqecCU+/adoU2tZrQZwfG4Tgelsq3NkamwnBdjRF0C2SFuIifH7Ca+/dEzm3drxpEheyYFfwysrCynlV/98g5ZEtfzxDR58jlD0rb5tnDBqtz1VlK964iV5BGxdZeScPP0KcdrZ8rRp/eIcxx2u1auIqVoxBE+lHDQopSpEqbJTDdiVldfw82UlsvY8sOw9ZiH/La+ZscmSyOZqmCmezjFQHiGaTZPdgvRATRXgF5CdoqiGEJov7eoBBF2zFhR5MzCCcWzc56cI1N4zc3Va9692RPPkRhXFDtXFLyIscNmC2vdT7uiIAYkSCmfkZhkIoTAZy9v+eSLyHvf/0P+0Y+/z//6P/8vfH67wkHZXU9cz3v26zX7dObVi5c8fPAMv5txIZTi1UVhyBRa9lqwvEZgmEy0qJAKzEyxrbmAVYG2AZKGnOoYmFiuRBH1cJt5PxpaRtDawtpGoNLGuJ9nl27XkAg231qOXh0na0kqxkBfirufSmgpCNM0o+qbV7HWS3Ne8KEQQ5QaeDWCNKZtgeqm+1JLGZRQ5TJBtSysChxlCD1rcL/aYZy03KecqwJQ5nWTPwPA79hy01cVHNbfG8gWC3cdB6nWp+zdql0x3ODDDl7R7lEym8/geXJfohRuAOn4OZvn1DDjqgRtvcrD1BtvcCFbx3uN6QB2gwuvrGqx/Q7v25T0L/F2DQpKDwd885WFHjlhf9tJGS0AXWvzB6PkoJjW3+tAt+7qGAB5S5+Ww5U5P/a7Ea/U7hjfb6so1P7v7z3ulbUvx64oa7jIAWWrmPROkLaOXZljmywhhvu9cembfeRklDlG2JYRcwa8RZZIu67iYmnzV8rcFpWSlmD7Vd0T2mhoyesrbK72ZLeZA3VvaZEzl69Tx6TkjzcDQC0zptv+rmuhR+JYX4955lqiHcQb9k01OqhGirQ+lxLpV9tQ89pBqrxXaJERg8L+zfH1O/4aYaglXCRLs4xUsF1tt6rKbj9vBIVDNnWyqldA1chqTOFzZYJS8tZcm0PLOaK6UDc2X1hOXTCv5LLEDloojKJinomplO+AUsMxLYhAMBpLa0e0Sn8SrMBtjPYu4q2eWstBUqPHF9NUEeB0Pr8BWHxlNC2spMFbuGjwRi+cUy6bdW7vczzuCaGUl5BOaJJTqWmovV6gD0ImsZ5W668wgcAvn7/m+ee/ASccn14Rguej6ysDEekRzcrLV6+5fXjkFx/fcv+oLCs8rJnHdOZ4dcWaIsvjGRSmaYf3gVTaKwi73R6Pcn93B86j3pQl8k8IUyBmx+SV2YOTmTAJE4rmzG7yzDvPqhCT46oo3ojjHE9M3jH5iSCewz4wTZk//NG7nB4WpvCM437H8WrPDs9utiK5Cqx5KYq2UeDv9jN+Mq/SsixGfKJGQJHzt1BnuMO5mkBuIO3qeDAgp6Zoht2E94H72zuWZbE+CYF5DkzOQUneXqKWPJ3uP8uljhZAmEyhCUUY+8LKmXIqtN+uhZ3qQLdvf+diXKj5d6HNjbrpj2GW49ypXh27rgPXeowbY14VFxLZP/BwfuDl63t++Ytfczze8IPf+7Z5b/ORh3hLfnkC5/FT4Hsf/jGPXwh3D/8ndw93TAIpamFOLPiuyIHyVNseikBwAqcl4R3MfsIIalYeT/fEJfH05sBNumJd6zq0nTUl3/I42zsVRUyjFcnOMW7WpnOOdVnsOieEMHE4HHBemGQrFnWUVRegs3lie09Sd/yYYpGLdoETZzSthnYNBDQ50UsutGgLAXRBVEnZEZnZeQ/rc/b7j/jgO3/Mi8cbjvwfHJ69xy8+/RWoErIna7JwYuzfO09mvvO9Iz4/EPKJR2+FpH0Cd/8Kud7z5LDjZlI+foyAYyUzIdz/6hW/RflsnvjH//i/5lvXM/nFz/jJv/wZH3/2mpyUv/d3/ha72RPTQlpXgvfMk8m6nGINakCzGZ4q8KhhU76Qc4k3AvoOfit06oPRlRXX6gReArZLr1BTxIe7ViBWjw3JyMVQa3m2+G1IuStUvFOL+nizLTkli5wo5QR289TW6tXhaHEruQL3Mi9SrOYFUgkB66zCRblxNq9b1MEbOOttHza/KYgQJv+GJ2B44/LbCN7LfbX3SVfIez5qLrlroyyqbW+tu/AOtoiKBp7prKp5bENVGGRzrzQwTr8t0uKN3hmUvFbqYeyBL7mufvc2gqNmFFJlzN3qCrd2pbf+tAu3bRuMKRtDyKg5tetsBPLFHHcUDoA8RH5BZ7z8HYrS+H7ObedRxUCjl71jOTtqKS9bRM1n2mSpGT0NE9UVpaVPeli0neuKgVrZHJcAACAASURBVDRTjTO0qT2mEFz2V42Cr81yzUDx5tGi1i5sEE1JH/4vVUvBWX3LWg5i7CPNNp+Cs+iuxqJc2+Nct3fUPqHnRY851+I7rVYqREKlW0151MqQrpvrKhawZ4g5XqqBQrpMrJaRlDqPRvMgq3n7qwJYULUZ+NRqhqZoRmdLC3GQE7X+p8/d8Klg56cMupS9cxvt9I2q+PU9vrKyGIKBnjF23w+0u5cbTC6W/iw1Ks+uq1axjBa2xGwWf6nJstKnqYD3q1lpnCmHlTmrMkW2DawwPlp1AFsMFn5Tw2Mh1tj0IlhMiJfNNBu5Sg0pJWFFeVtIR5coDusCGd5ZREsNLLpEo8a322ZqP3MTts45shOWc2RdrDZU3QT6YqobcxHmyZX6gB7nPUkz96cT//anv+Szzx9QPFdPrpmmid/eGAvoJEbQgigxCu88e8oT9aQsPK6RROb6yVUXNhnimsgxEUIgp0RMkYfzApp5dXfi4TFzvxaFazmx38/t3VQhrso8O5493RG8t8T6bPQoKXumycJ6Q5i4u7tjnicOPjD5wNXBFOD581IcQe3c+TATF2WegpVQEZsp8+xIKRPTys3VgePhwG7niasJqIeHE+uaePfdd1lzYl0WnHPMc2i5rCk9J4TA9fGA92LF0VU4P57RbDUa52liv99z2HucgFQ9oMzJVDaI2o/S5rSyFo97DUMZt8cxHKTmOFRTfyvVUOZM1oZChuvrGgSNnVG3/GK5Z9JrcdZnVsA1hZnT+czDcubh8R5Rx7NvPWO/P7I/zIQpM3uHph3LOTPPB3ZXN3zyVy/4+NdfcPuwELMSyqaybdfl6im6VemDmt6Zs3mmRGBZk1Fv50RhvCGlTJgsWsE5bWCsbbaihVFwC+q6FZ1iSLKHusIMC9K8O1vwOIBp7WBOq6yroKX1PK0kSJVJYMCrsgtWBjup5QeKklmBiXmHokVS+EAmEpMysQPNzM4RpgOvHhdWubW8WRyinixG3R5XRdXz9OmR73x4hJcnapkLjxWUTj4hYebJdeBqfs0vf/rnvH545NWdlcGxuQKnZUVc4P0PP+Q73wm49ED2VirjcJjwQQgxkGqKQA3TdlPLS2pzGdr8RgXnq1fH5nUHaRWIbddGg+VKIcXS1vMyzDWtk6JS+l8on1uFQIcJpJvzNpNUxvPrJVtQWQvGVy+ddz3vp0YaCLS0iQrnq0e8l+qBGpIqJclLqYYkQcTj/LgndfW2m5rYfD+qNkCJjinjgTT5UfetHnHdAbEWmdO6tZ5x4ZUZPV71Djp280WLyQXQa3mDcd6UC6r3aZRpzYNGnSPlqvHzi96QoZ3jHju+39hzQ5fZMbCXty8GIaHDhcp2rr3xd1G6G964ePDGs6hbWTq2eZRFrRUXY8Smn/pCGeXU5e2bAtbO7X3cGLeHZ77N21YN5m2VDGNEm3f9HS/bqcM12zYzdP+wFobr61Hnn27aUNvRrmw/63h0VUbR9h7lk4qBN3tAec4wLS7HzNrl6tbeFD0tGmR/lXEtl72jnLemwsfhvEXsDQbgynWhxXjdDJvjAhzmfK1DXnqDQbg1JmaTTYa9W6SAl+Fzk0niMk51Q9CIE1yecE4HbNjzp3+Hbeab42twfHU21CogBgDWguTFmPNaqFvZhCjn5jKJdBC2NfTRlt24aosQ19xc521SUjY4V8MRcgt3UaSEBXbhqZJJKmipx2g5WS2TcpiwStKEk577tNklBuAnQglZlUL64wpVvFmgcvGM+lI6RMTuWz2iGytjzf+QiECrIwe2oHwBl5f9ry4TSuachfApa8qcYmZZM6flFQ548Wlm3u14cXvidFpwPnB1veedm53VZ/Oe4CYSynJ/JgThyfUREVhPC5od7z17ghdT/tcY2U+OP/2bH3D7sHD7aMQIxDM771BJnM6Jx3Pk+RePzNOOb30wc7UPuCycH1aWDGEnRrrhAs7NvH69ZwqBw87hnLCumfMp8fo+sjs4EM/5HHlx+8B7z97BTyBTZFlWXrxY8W7m/Xc9OSd++8VrVF+hIiyFZvq0RNY14T/+Ak/iyc01oEaQEyx3yIkZRF7vHxGxwtQ5JnbzxDRNeDyrRmK8I+ueKXhjKMswz+ZNT6XwuivKPECMlpvpi7IA5nkKwSPF25eyeaxdBZjZ5ngN3U7JmCZ9Kaxd/FANBprxoOc8MawTwUJNAbSUlahCWkRIMfFifeD29R13t488efKEefY8ffIe3nkeT5HTOXPPLeAtfHh2xL3jn/6T/40/+9lvefn4ONTl7RtgZSjty6iSD9imqar4SSAVw47YGlVNpPjIcrpnfTyzrKXm1tkRJm8lTLQSStlaVMOdVI+s5T261n9OzMuL9LFpVumNF6P2zRaojKReG2BUFIT6rGalpee45ZxLfrbJj5xTA4+XoUAFRiGiJqxjZMkeNPPpyzt+9qs7XpwW1phKyKkYeYYTxHnO58z1zQ0//MFHfPT+nuWLhSxXzFoNY8ru6Ue8Tns+ef7A5y9P3L34JT//+afc3Ua8T+YNVzgQ+d//6f+Nhnf4T//kT3h/esWz988ENxXgnI0VcXYIgaiAlFxwlcYAGFMsSmSvP9uJMTqr7DZEqkatmHxDSr+LNNILY+fbAtUKrGhGzQ7Uq/dnrCHW8FnHtB0WSg3z7EbSpvTkTglvF+Zyv7rvWb6zgTwDSik7YlpZltVqcubMbjchzoqk260EKcaRnr1oIWXruuJ9sGgVZTBk0uZZ64eiEHZFpCtJrY9tox7g/AAo29yv42EvI3noIYHOrzkCX9o+p3VMNorZAOrLpl5B/UVLmpfrTUWrNnVsS19Tb1P8tgqbDPrFW8Ia6zOEzTquR40GyaXoelvHDH1LUaTy9n2bAjMqeToa8ob21uveBqyltt3OrGC+N7OPefV6jgpiaxOX8yS3BaHUgvJFlZLyTly26e3K2MYAp+YJayUbqiEEGgFS1br6HlXOq8ojVRawue+XaR5KL3c0zk3Vft/aD+bNZDOGSh1jqEac2oCa1WEfGadEKh42CsNoXUopVxKY2N+tvmB5N9Heh228hpMVuhwqqS+bUhUoubCK1/vXlekao6mWvXP0+tLnetlDXfAs53PBDj0k19I2rF+Wx5PhYe836yin1KoP5OL9ryzlG4MFF/Pjm+NrdXz1MNRUiwIrrZpmra1SwPEYHocUNlF6rlS1pTm1hdHc4s2CUye2eTwMZE02SQuRi7humVfNeLwxEmIW/NyAH60QaczRSG6KVV8pwh5t52s2Xr4Yc/MC1Ps05bPWHyrf55QQNSt8zW3JuYTRhlAEau4ixtUajSYgajiRK7maNaym5ZAAIiU8pzRGJBvjYtkknAjH2fOj3/uIw+GBdUmkdbV/S2Z3PPD5y3tevr7n7rRyPB74WBI5RWYXmKcj5/PK/jARJuHpk2uC93iB4By3dyvz5Jmmiav9np2f+Oj9d/jg3VS8w8IkVuib4DgvkdPjym8/f2Sa9txcw+EgzC4Qz8KaE9PB6P+zejKBu3sjm9kdhJQTd7dnTmfl9X3ETcpumolL5Pxw4m/8wXc4HgPTTnh8PPPLj+9JeeJH3wkk4IuXd9zdnzktCRcExHG4MUKdF799TvCe999/hxwj5/OC4AjB8+TJldX4iwvLeWU37cgu8uTmaKGn3iZCSpHglCBYrc5aTqOOsVSrnxSAWEI9Jt9ZbtsYD8qKGwAt4LTXsEo5k2JEvcdpyU1znmqZtgtkc1/vXQuHbOFaUr2WVbG0Debl7S2PDyeWuHA4HEnxxOlhARxhslzitJ5K7q6yRsc5BT777Nd88fKuGEjKXHc1Z+ECOLY9oWyirm7QxZLbQAG4FoYdyTGSkoXXrnEBCYibbNOqgB2xMLgK1ory1oiCqoiRLmlsbbmuhFDaw4UxrEe5bsbu8rj8bPTgdi9CIQ9oXg1t/VQ9HVECkiKSM5Of8SI8xns8Rz59+ch/+PgLnJhhLDkpMtjyS8RbBMY8z6Dw6tUtT8ST1BGSQa0kGT/d8PLFiZ/+/DWffn5it99xddxx/2oBZwzFDthNwp//xU94//t/wt2j58NpgvyAk4mcssmmbLlZzgttRhalzBpVfm/wuU6DIu2lRn4UhWuYwxX8VmNiJfXq8l/eAKQVhNYQToY1ZYBIGmNn1kEZUmlEJS0kWGSTz3oZRtk/B9zgodMt+O9ts/JO6xpZ48q6roiUsjxOymouhEU5m7KdtLU5xgTimoI67gsNwG/6oXa1jtrUcB0D0JbtJG9KSHmndu/Bc7DRA/Timu2hXLSp9WdXQJqCNWgK/Zy+Rse+3Sojfd+uzxjbt/U49Y197J4tFtE+Zy6fxXj+5d/bPtj0iTZ96I1j9IjWXEcdu/6NC8rwjTe+UKybovWWB268h4xK0vYhY+RL+7bpZyOGG65pz2j60DAHiwFgaIcM008u3mFUGC8m3dgVb4zEGwaGi34pwqXju3oefY517yr9RLpxctNGwTzlZU8fFeKsxVCTtTGtjqHH9Sb1NlV29Rqp5jiwtKbC/p2yhavWOZW11UaUolDKsCbanlzSb+y+xVjXbiINV6aYrK5jMUZa3jOF/FBZzmeg11qvMimlxKyQUyStKyLCZAQfaBrkqev1kL85vn7HV2dDrQW2i6InQokHylbLbrXC0C0+vpAp+ErsgaNGq2e1KHA3+QJ2bdVVtrfcrFiu51uB6ac5k9XqTjk8PhszqJJJY8qAdCDhvZW4yBWQqJQi6YIPA4oE5l0oQronMrfk3Zb0bJ/PcyfKEF+ZFbuHYWgNVa4m2YJOATRZ/HgrH1IAa/WOWN6atddlB5T+IJviLfDD95/y4w/fQ7Ny9/DA+bzw6pRYloV33/suD+cP+NWnX/D67sTruwcggBMelwfuYiSRSI+Rjz9/Diocdjv85Mm//Jj9bm817/KZx4eV5WS5qR986ynXxx3XV3OpmeeMtl9hN094txA+tzy942EmzBN+Cpyfn5HFIS4ifuF0emQ/TeycJ+VExJhbc15J95kX96/NyOADf/mLz7g6TLx7s2O/8/zoO0fzWiNojLz35IYP333GNFk9RlDWbGU8/PxDsno0W0H4ZYnEFDmXep9OhOB3TGEm5TMpK0tUzuuZx9MjmoVnz57wJMyIwHG3R5yxw9oMrLtOZUEzoWu1JwsBkhvDVF1RLD0VNOH6pha8eeunae7zrO63TQmyXbYy7+Vs1KFxVSN+KhPbOatvVdmGs2bOqzGdvnNzwyyOBy+cz/fMc2AXMofDzM3TK0SUdYlI9Ewk8tN3Se98h/Dj77F+9jPyg3kFozhUHU5WGy/XQzJD6An0ZXXbdpmM1RMPSiIDx/0zdlffYt4fmRyEJ08I3hshSM4I1nfTPHVvIqlYT4sxSes61JJHbMyoguJdhVclr7GCl1yBTTEYSbfSV7DaQEED0EP+hXRmTGlKqJSxh0SXKRUg2aWljE9Sgk+2jnAoiTgr8SEieuSzT37NX/7Fv8cBk4eHxTIUJ+eQnPApsn96YGXlX/35v+VbH93w3/zRFcv9mUe/4BL4M/yH+7/ik3//G17+h7/it5+/4N/9m59x+3qx8NMInhVxwpIcT54+5YfffYebo+N8PpHSwpKmYmzLqLOyOGta8N5ka8quKSKt2Hq2MG1TbGr/1VIiWqJPxu+6jKzKV+33auir+KYCKam6M4pI6oYC6EEw4gbvdkN4ZQuQ7dO1E8XUD5tcdz3EXEuodDnD5l/OVKNMNVzuXGDPxNXVkdPpxMPjIylmEvD6dAYRDru9hdWvtofFUr4kSGB32JFiZDmbvErJ+mK339ncrcA0W3hyI16j5M9+qSKjRlLUel9N2S79vZmzlaut7MUhdOMQKrgaLVONqFrvB7FEUFQDTQWytR83YePlpqloG9K8ONquh1rCgq44fJliRTckoJScct2MNRujVpUrNI9Pyy0fFI8xouBtyolgzMyjIr/xBLa/pXZbf4emm2wV4uYjVO2iiLIHDu/flbnLMS/3FCmEtr2tQCNUyuN7DYrllymToyxUBbI0g2ddv7oxkvW7iFDI7qTJ0dHx0JTTwZBQf3+75/DSqFANLFr6sBsaB7OApTKU/sltXff88vFoBgyBXlGkoFy/lSW+YNZG1qU6KGx9HtQQX9uSSjkitf0650SMq5XoKFFMFoFha6DnplofukIwo9laFVMiZotoaNVG8opqtr6nkEmq4lNGxLObfevy5AzPV5IuvTpgURCpYJnugc9q3A3LtNpYlvGqqTC1dN3bzC/fHF+P46uHoVI2ymIJ7vKrZhX3CV/zUqB72rorn3aeG622WGHpGiJUw3mMdU5sc0Y7WxraGAmrfG1/S78n0kM5q4dzo8bVzVKLglmKs5cvaYJEK5TtgEGbIJKyqQxhStXkJ9q+N3ikg9wp11GFnCvhauW73L1L1ifmGaAAfgvxBbKypIiI5cWFacJPE8ebSM57sgqnJfLBs2uWJXN/erTeEWGa9oRpR86JdV15eDyTU+a0WCJzRjlc7ZmmidN95PF8Yt5NTMExBRPqp9NKXFeef/6a7AL4ieNO2e8CT46B/bzn+jCVkE9lt/e4m4msStREOMxM4nHq7F8xOpxjQibPk8M1KSaW00KQickfiNFznzJLOqOsXO2fYJQniWVd0YdETEpwE5OzHEh2kcyZeQoE73hyE/Au4N2+z4kaZiUnNHpScsSYWJdrkIndwXOYS/K8w+p0OimlUgbPSFsztu2ua2dE7EQIedgwa2je5aY0zJdxI67bdgM5Np5eprp6LCGwCOlRcTVlSri6nslZ8cnCmO8fHrm6umZ/PHDc7Ywtt+VPJXDKPO94tSifffKS/+dff8wXz+/bghrzA6U3tBk/KgipXjURIYuzNSFSahbC/cMrHh9fF3KUuegdtgadk01P1b4MPuAE1pibJdTInYwgyqIBCkNzkzsjWOt/W19WCzMX55Qz3Rac1J8GUrbeKPu85yr362iySXN5o5zRZIprFo8ky+V03jWDXc42XpNkVHKpZ2UK0atXj3zrO8/4h//g7/Of//2/w+Ov/hkqE7vDE5aHB+5ev+Dm6gN+8OMf8Trc4D/+hF99+il/5e/5/OUdBMcuG/FKCHu+++Mf8b3vvsfOn1hPd8Q8G4HNVOSyhmI8FJj2WGVIJbIS44oXb+crqPMNsIv4YstQk5G5aeq2FkrqQSPdEFfhayd4KUvDztnK5hEwDoumrKFxLLdw5Y1wyY1y37T7tqZsLnTgSpuX2hBkXa+qZ3AO8Z7JK9eHCcv3EWIlxPIzzgUrqaG1HqcZE3f7qRlAzZhoSkj1VDhXA9TtyNmifnyr2/qmR671j+vqbg1bN6V33O9yU1AMT5vhspaJasbiUlLDVRKO0m0padunbU/M7X2a0lWUyfr3ZgzfgixbHlX/8f95aLvX1ttdxWrzsMn2bl/mNYWufNb79N+Gc/I4H/r3m9vqNrqheqXaIWWcRpbV1rYtp0RX5BjeR9q7o0YuZXY1t5nrY1syNLB/IeHpCuJWURtDnLdvW5W+vqbqsr+ck11RvViT5SZVeR3r6NYQ5FzWRX3nqmi2sW9htsM7l3ZUvFc03vZ81Rod0udlHesa4dPma1Fy6/20vrjmoecGWTG+G8Pc0zrmrq0LxcpLVRHTKyDW54Ox/udmtPLOlMkgdOfLsP/NswnTqnhXp4UqLMtCzpnj8YrciLvs+pSilYzKhnFOpxPee+Z5xnvH3pkhvabEjEaTFNPvXFPfHP9xj78GG2pqgjmX+GxbhvX/cdF1D6PFRicaiwQUK4TVCmtrH+WsuYimfmisLnXzsFVlFbSFlkIJharMpRfC641/jKDCntPc4Q6zho/YQoZFCE1Ra9TluW8UzbrTXqAosUVyZjpQdhdgUzvVV5E10vrZ3nvo41xD6UzZzCmjmgrAFRwOH7zlzCVTIt85XsNRiPGqPAdmJ8wOct7hp4Cf4Xxe+PyLV6QIh6dHRDLLGvnlwwNJ4NNfvCSlxPHJgWnnuT/dWaijD8ySmWVBzoLoxO2SePn8Bb/+zQum3YyEmeAn5qunIKaAv/P0CgmOg4ebqz1Pnh45n888+YPvcDVNOBbEZXxwuKx4tRCJlDIpOmI0NkhRJUvtA2DNlo84BbIoaT2RRSyMV0FjJGpmyamNX52fUX0Zu4jzwvFmLjUotZcXK2EUSzy3cWyC39l9MoV2mhpaad87Xzf1rkyVSdA30FbsNrd52xLfpRd4H59bZ4k4CwfJmvFiVk2C3cH7QHCe2/sHPv3sM754cctHH33En/7dv0dcF8vJ1UiKkKP1jXfCYzzx/vtPeX6b+eST5/yLf/ETjscJP0GOStZYDCLSQEafzw5jjQLEsrFEMqIzicyEhdfcxZX/7B/+KX/8h79vhojziayzre23AIpmlVexmoo5o+JwtXBysy5Z7UGAEKQ4fUawpmgu/SgFeA+GpjeeWzzo4i4NRCUnsg5T7QLXjUAj+Gnj7i3EJznFTRNCQHNCieT1kZQTx73j2VG4mmaiBiZ/RlNGYyZ5R549y3nl+nDg9z8KvLd7zq9PETdPyPMTu73j6ofv80/+DH76b/+cf/Z//TnrurKTA0fn0PiKeQqITKAemPjv/9v/jj/44RWPtz/n/MVzXq+evJ7Zv/eUx7tH8lmZZCYl5eeffcZ+t+MH3/6Iq+OO89lyWuK6IFJBSLJokWJ5n+dQcoIL2/M0gVqIU49UkYbkW/j/WDpg8GBv5kUFPTKsL6njriOqp+5mW8+vp0O6EqkyrL8Kxs3jPXrGaghbnxftfhniGgviN89wTlpcokLMK+pW5tmiCva7mRoa58SRXQlrR6iG2spUPno3tLUtQmLDF9B08mEOduOmKT7OO3zjZCyRCVlRrd41y9XzLiDeckcRY680rgEt8sb351ZiKrW25kqbTCd+a3mBcrH+qtOEvgb7C/S3Zujzt/1sh1YDW8cssDXOtr33wuBQV+8G3F/8/zYf3CU2yJUBtsmPPk4tH7UqLUM4vFJwjXZl3BScEoWk9czLvuh/b/qlYI08fC7Dc0YDgbXNvdGfTT4WY2uuOl5TuEdlkzKfq0w1Y0itk52zFi/V9v4C5HJN1q52XIYy2h5bRqGeNJxfN0hbIzVqy/d+KV0uhc253mTsH1NIaZ5ZBo9hVzbf7HMZZMjGGDW2lb5FXBqsdEghsXcXispf5oYrBGBWxxxPwUkJrXKyYMk2NFqi5qR6lUuN17Kuptm3KILzEhthnhmeLZfazhSur6+L4yFSjQ2IyWzjZ+jKsnF7bNmmvzm+Psdfy7N4eXThMe65Njm2YpVBaNAF9KUwlW0YCNAEik1wP9RvGq1Xsm1PXUldMnWLRhFila25UrEbwQYNz26PqliW3aO8nwnsfu/L2PNtP+kb7WiZPE3qjQrDlq2qlxuoJBhtb+/KZFMoXWtLBWbdCqxMfm6Kp3NWwoBU6lQiTJPn3adXqDrCbiKlhckp3/voCesKV7PndF7xcyBMnmd5Imbl/mFF04KmaBbyAOclc14S51PCnTPLeo8LE8KtsTROjpevDuyDw2siBE+YJ2KMHHY7ZgHJK3MIXF/t+d4HT9nPHi9GIS9uRskE7FqZQhGIGJutTQgDIuJLHxsIzCrkXFl1KzKwfgvJBsmX3LdmoQRiqSUoKOTYx3kAZOYoqTu8lpIwPU8qV4Uq1xpHfbKaccQs77UEQfHnl4FXalhytaZ2L4dZiOvGoAopxzL/zEr6uD6Sy3tM08RhbwRD5SU2aLKCdfNgFxBQNtk1lo2dvmF1uFRn6NvBWg91M2IB06FsPf2NP/ox3//eh5AT66rMB79d82Xmb967gD0XBgZJreOEGWJK2E9MUqIACsAv7R2t+Vr1Cd0q47395T3V1msDCfTogRZRUCzUJjMK0VUb72IVl2QyQbXMjoSUGpDemUK95EeyLIQ5EKuCUsYoi0djIvjA/jAzEYmvX1rJFlXEJ85r5tUXyl99cuI2Z+a9cj6d+fjVwv3dmQnQUoM2+MBhf+Df/+wXXB2+xbc/2rO7eRdZlLju0DVxvZvZHXaFJAuiX1lj5v7hnvX8iKophofDzkKfcyRnyxHOtSRMkVtGetEZtitxiJWCccM4133iYs7XURmNJqrtu75WoLEQNiHdpCjjJ2UQ6936HGgKYT3XkeVCKR1mST2qeCiRZTbTNI+rBDDPsfPSgHO73m3zdOtVVaboxfMulYbN51U4VNmnNk+rommsy6lqDEPtt8I/UBma3WL3zH2111A+YzbNTRT04KKyb0vt376XuZIPVdvZlJreY8OeuMUU7UfZADYK2/DDXj2j45g3ubZFLlWHtt8v+3Ls8S3Ybzet/T2cX5vd+BLe9h6DIuVEWr7upm3DX92jOLSlYaN+f9WLfhuOGpnVDP5FLveF0hDW8JxRwxnWFhce0aY1FaXnbV/p2FejetqvGX/CdkyqgWa8hYzzoMjoUfVVqhx5+zppBj7GXiy4sfX90O7K97TplLqrd6UXGAjgpImaupL7u3VMMSqktHtU40bxsqq2MejfsMXfly+pvS0VV0jB6Lb/eZPZ2Yzv9U4WEQAyeUYbCyJMUyg5lCWCqM7WL2vHN8fX7vjrh6G2RUezzDWBrxa+1FdVX5CXc0RL3tF43riQ6uc1eTaXjb6Fp5TnORVTOly3rtRjLKyq1VrFuDg6g1jd/PuG3wVBoxQvMqZayX0txl7Pr9dv3uLNdx86dHOmGwRQeekmRAZx38hTxvuL94Msr2E+Jn1diSfvzHL9XiYcnJFTFDKFKQSm69A2KmUChQ+eGNj98Q+esayxk/k4EwynU+a0nDkvK/POQF7GEZNyOifWlHh9/4CqsjwsOO+ZdjOHw54gVv/ydF74/MVz1ph4cUpkVQ67HU+uj2T1fOYeCF44TJZvKsGsVTlaCRaZ5sa6OAUheGHCQqNUZoIfN84yP7My72cEF2xacAAAIABJREFUsTqBQHLRSn1kU9hCNo8U0sFpZT1z3rW10AHpaNUs34mxpQFIzeXSwo5Z9gVX5tDoYK/hzeMcwPVdcVRUKkujluTYCnVzyX1SlPOSiDGizrE/HjnMR0IInO5vrXxMNiZesLwKEbH8igyaFE0ZyVaPM0dFNGA+80gpu9RXYkFvqlqncz9UUGLh9VVqktM+eHYS8STwZvFNKZWNp6+RlruGUIu5Ni9BK/ROX5flc6nlS2q4qHQwYOu3Wp3rOPZNvY+Ba9b/PJRMETGilKrEu0LINc6FRkNuV6OUosYOvIaSJ5mbYWMKV2RRPn3xKR//5ldEjTgXjTSh1gtDTFl0M+88OfLOwZFevcb5mUTCzUJix32auPkA0uGGP5HvcffygX/+rz7h05f33OB5THCOK9/96Nv8o//qv+Rv/63v8v71iuaIm4883SmZa+aQLGw/OjMi+Mz10++wxsTj6Qypk/jsppkxFDc3ggdoxcyLHI4xmr9NqoGAMrfs+s70qM22ltPg0a7IrVzbpHFZXwaiq8Le94u6ZsuCKj8zbQLV9haDS9sMdLg5/fIRFNVol2IuwDnznvYQ21bJzZizKQyU6EDYIq18Rp0z9e0qoL0M3xOp85Q2H0eluwUVlvld2+zKmss16VCk5a8ta0IxNmdVWFdL26gsit3g64mlRm+tEVwVER3Hoiq6w36GVEXKwl7f8IQNQ7Q52r43fFR/ViqFEas0paqo3uNNpeLoYVy/5HfYkp288fTyo94+FwKGSxIb26+r8aQ+pytvYz/Yd8N9h+fX0g5jmCTjfvTGW3S1WoebVmKqml7R7lWZi8s8rH1sVWDKPaXnwI4Xj+8wKsTNYM/w+/iupX8u+6B/P8yR1qn1pfoeWU0z2oLBpODBnjrQ+yt3iT8oqaNe1LqryILmoW/fjXJiS+pT9xgpGLqu6qrAdeNFNRRLrx/7pX1Qn8lgaLK9pdVcZItT63NMLle5XXOJbZ90zpNiRnzNTcyMsqQEGw7pZha90yL4KPUzpbcvxtij6b45vnbHVye4aRNxK7THLXdriemTyb6o0pq+MVlhsmHiX04cHWincxOYrT0VdEgll+l3qcL/bZaMTajFcD8LZd1amnpYa3+3qixSFlZfBK3V7XeTA+6NdvS/+7VOsym4l54KkcYeWUVHbt+xfbYOdjOhANgiQKVb8Xs7i0XfFyisgmZTDlKKJc/QwF6KZ3wQdmHP5BJILXNic+PmOBHjvtD6m7I+H2acs9IX52Xh4XRDypn1YUG8x/nAbh/wIhx3E+flzK+vhPOycHpYSQpP37nh6urA1dUVj3ePnFdFmJixGpDnZeX2tJISIOdWXmI/OeYQ2IeSeefN4pVTJHiHVVJIaE7MO8uNk0IUcS4TNZfQRY9ANsDjSu5bTLUALps5UjFum18VbyIbtr4+pxh2/EvvBEXpaDdpn116WFqoywCIEGPbzTk1kOyDjfpa3m0KRtSU1oibHNkpvnhzUvHwtI0nm8fLSwGjaqBHVYxgpkOHQRYMcqDPenvbrOAsGd+uEe5fveb8+jXy0R7vPSnGDeFBK7swABMDCDVxX5q3VgYwU/MmasnjDqB7v27eYACwLcLhAghYnbzcBlKLzOrAuXoxu2V5O8bmlzXxWCrrSTEiOMGpN1VJ4HyO3N6tLMvCIYASCsBQkx3O4MgUPPPsgTOxKvLhyLTbc7Wb+e7q0Z+8IO8OhKeB9995ya+ev0JWwREILvPkyTV/+OMf8Pf/5Efk+09YX74gZWHeOcTvcPPMmpRzXmBN5DWx2x057vY8uT4UcKgs62o1RgHUDFBxjUjJndW8NqOTqpJjRBEjWqAWpaacm9smI1itRsRCLS382BuZiPaiE7Q+r+thXEcdVFaZvwGzl6c3sDV4wzfKjDSgqpRUjXEt1/lQFKKeS9jngwh4JyyxEGANwC27XGoTUxiUa3TC9j2t3m9uDZfhu66/dMNH2hhNbD8VLV7NUbkSYYn2PC3sx7GE+gdf5FcuURKuFFWv8qGEpjaPU02nki0Biw59zDAW49G9Sxd/c9Hfw+G0rL8ifx3d8zVIqOEZb7tLPfMtbeLtz91e3mWjKp1AaAh1bO9TxkLzxefjfC33y1T2n8GLXnf3Qcn5kpfa3Fu3H9LWxPDW1RuO9r0O7H1ye4ZwuQ/9jsc3BbGN35coxv03Gf4e+4g3+7IpZh1Tldjddu3lPKt4d3zz+lm+GOqupGKK9JuNhnav2uLx/vUUHZp0cfEwvy69qeNno6J3eY/t2F5+b2OZczc0GW51lvJTyH7MgLRVFDclrOxFTVYXMFSNMqq5VC4YiIu+ZL1+c/zHP76ysmj5Cn0DMkGhqEsbAf2mFbCC15pS2y1eUnIMx4m3PQp7Ut30BkFSvf0VFL5tU+lgfWxf3/jGe0ppQGc8o03mntMhHfSLWW5EtBFMID2SdGxFDXEdrSz12VtvqhZLi9Sewog2ShZY2UDsOa79XQVkA6iVHCImPEZ4o6qkNVqIWZhaaKMWQOMw5rkU+9hFjZxPKyKxvPtCmHbokktZk4zVtFcQzxfPn4MXVOD0sCDiOF4fEee4u3vkdD7h/B5wLMvCaV04LQuz93gnXO0DeMeaE2tUUjQwfnu/8vo+sq4vOJ/PTCFwnPeEEJiCENOZV49nvARiUmJlZHUTHmWaTUGaJmE/zTgR9oeZq8OMd8o0e24ExIsxS+JwyRR09pMpAzkDRvqTyuRYopFQzPMMMZc6hoKffJ/jRSjmlI3x89IELePPIviHel4NEDRr9LhWlJGN742ZV+ZHLqEg6xIJ04w4j+VDZitJcV6MsTXU2nbD/CxMnk6snMdyPiN4bm6uzQtZisG7svfWcBQt8qHN7ExRyobPYqm5mUs9VHHswp4//qM/5KMP3yXHl6haqYBKf9GRXWlUkzV2f0ul6p6ahipr3LkIuEwXJR3op4u+1eGSEZj259X9sMuIJt9UWzmdYRixAj2jsljkoqsbaLVmCyl5FEjnR3Yp8e7Nt3jv3e/y21//BtSRkq314CDlFXUejZm8RtZ14ewekXyD10B49iMkJFJ8wen1ZyyvX/Gv/+Jjvnj1wKefPSeeTyQy83HiyfFA0sg//+d/xm9+8XN+/IMjf/MP3jHigsL4/C//zSc8ffeK7314xaTK+pCYg7Fh5iWTqEWbsZqkWoGvIG5qyncs+Z3eB1Py0tajYF43bXN9AyDLr5V9T9BSHmaEVm858gAa+91Mho7gcVAmxrHvo1Y+H8C50EM4K3AyAUApdwMx18LaBSBmbXmXSZWUpYTYC0l7XJvlhdc8SsrT7B4bP2nrwhLlMPRIU4rf2kPa1voYDVTLWOWciUtmzcoaE1kTabXQ4f3eGJ/JyrqsZLTVRE1xIa0RRXHija9A+vK9FF2CGbgEKQbhasS1ljdCk8raWQYj6ZBzeKH4pFrKQGuvDX2nv3O2bHtog21qr9HmSlOcNopIfzMdPxgVozJXLpWltz6vC6O3arobZYJqAJBmDNie8zYlw44aCj4aS+q5FS+1MPpBFlaM8iYio13/1kMzSiUhq1df7BkVvzEoX01ZK/Kl5RMPzyzv4WxT7orZsNeNDpBxeWzSEFwx77Xx7vgUVYNJrenDdQKV48JuL8XYU+XO9nmu8mGMyvhbMG4ljdMqd0qrbCwrPhxCVXW4z9g+TDn0vpbaGhTOMgTeu8Kk2vc02/tc+z3XUj/eZFhXGq0TdJBW34Skfr2Pr64sJsyaSF8M3Vre8zdMvtgEHqZ5U2SqUDFP3ijELKStX1M3q54L0kJTGOLriydQS+5XF2JdltajMOTboq3gvCzcFqZWLL/mQu9AkN6qzpa6mey1U+rGPGgBqlvg3BZzHuVgKT9RBVbNpyrEPmXNOfG4cpmUTb31LaBJOZ/uSTHjwmTewbMR3MRl4bxEnA/mMVLFu4AjsCyL1eYroC3qimoCCeS8IMB+J6SHBR8CyzmSo4VgzrPlRaZpJThjPT1eXReioIwX4eb9G7x/wrwTy6FLB9aYOcdEXE18BCckMg/rSkrKek5MfmqgQK5mro7v4r0piOclcv+YET9zpRGZHLunewTlfDpxWiLgLSn7YeUxRc7rQphmVLHyGi6wrGf2O08IgRwj67ry3ffeY9p5druJaZr48P2nPLnZ470R6YhT5sOEZCvtolmJqeTtlbEYAaXzvky9ccM1oWrsnRTm0RF2tq3MPs99ztm3w/nDPGoW1NIAHwSc59XdHaqPzLsDYfIcwh5QkmbWGDmfFm6uDwZoYw9by1kJhX0tZo8SDNxjSfQp1dzFgOIQWTdLo1oplUS1fueyiTh1xmKripJZNZH1jnQOnNw957QyJysdUlkn17gMhAgVVFHxvgHhAbVJkxVlLDBlMXjb5MxTbAu+jkuNFqgezdqn1YxjtSDteUmzlWTMCR+sb0aInnIl5uqW9zHXRMC8jwq+1Ncz8hxnc92tuJS42R95+u5TVut4fErErESB2SlBHY9ZWf2Mm58wy8I9L/HRIU+/jQuB/emG/+l//B/49MUjn392Z2RPpW6oSEBT5L/4B3/Af/Knf8Qf/d2/w2cvXjEvr4ivHwiT54uUOOyPnF3mL37ya/71v4o82c9c7QLTfmK3g3duJkLYN8KKKTirDVpCwJ13PJ4s122ed6CwLkszdFFlWQuDMyAU/NS9yl26EubR4mJj783NWhT23EhgbG3m4hncyvUW/dI+dA0o9bXYn1THzyZf31qd64BMyj5jdoCEklt+o4X4W9hyvaEVvLa8TqB49q19IZRC1/VBuQPmoTXUPFvoyui4Z2kJL23RtGVd5Vi8997an+seJK6QusHNUYjnxMMpsZt3nP0jmcznLyIPDwun88LN02sOM7wTdlbeyk84HCEI6xpZlhOqZezp6SU1gmKz50o1LA89rn6YAXXNt9drgLjKLugeuuopcjV6p/TD246Kcbdem9rHddxpcoPh3NFLd/lZi2TQKr1rSHb7sHzWlSIRhs/r5SUaqpGelHeuhsqKb5r8oikmuXmHtv04/t2qv5TJL8MrtxY0eVa1JylzdOjXi73p8mjyu2AlHfq6dHBTGd9mmBsN/rX26njvtgYHg4oKQ5kSIQ/37U+0c3rEan1OIRJqZC2DR23Yh8b+6T8tYqA+u2JAu6yH+zZuAGo46Jb4sZ1X2to8oFVQaX1WVXZLTza9trRNpWNJrTmPNIbTtv9VtneppaA6pk9pbeMi4lqe4huMxjb5G+ZW6IRW3xxfu+MrK4u2IMei4nVrrnZwbYBo+G8zOctvVPf2cPfNRlyfdxk608DaoGBeSK8mEMcLm8xqO76A5sZqadtzBRqjUiflO7o1S6TFIRiZCIPDUXkzBFsHAfhGS7dnloValUC7d/msfuEU1V7PrRKZVEHeLV5FSBRBYiG23kIvXaH5csIUjAAjqMP7UPKrFM3OLLYFKDhn+X+kRE5KTkqMCZfBh8r0KSxZkIzl+qniBbJkLCzZ+NlzUiQXQCy28Tvn2O1MiV1zwjsgCTGq1RoTYRJh8oEpOIIvDH1F01rTAQmO3WHGCcweDgdF8CxLYl0npjWyrBPOe1NuxBdGrskAXlZizKxr4rSsrBpZU2K/11LDs66BjBOxEGDXw5s8o1AuykaZdF6k5Tj2RaSQxfIMVcilwm71wFSVkPZ/gwBtXtdSUN3IcAGA25qwObWukZQemeaJ/cEYicVbwYM2X3LNU+kAw4kjofjJEx8zr+/uSr7cNoRO3jKvt3O85sEaEKk5dykpOMe82/HuO0f2e1/muG/XNSMVUmo0DaGdw9rKdT3AUM5GyrJVpIVr0TY4O2kEpK3b28ZfZdzQwV0GVplkFh0o+N+89n28RgbA/mmXaZp9a1vd/AWHZOXdJwc++ugpqBvepWEDKEAxKuAm5jCDPli5llLixU1wDBOke7JabUrBsgJnD+sSuX3IvH5M3N0vPD0IuzCzzwUkSSblyLffvWZKwit3IniPzo4FRWPm7kGZJqu1aoqaN4Ois3Hz3rEU67UrbLs1FFVq3hKlPzSX/D1srtZO12HOSweXuZZraII09fOcw08ey1vtngcb6wrKKliv3/XIjTY3GI+ikAxlK8zIUAF+n5u5AdmiCJaH15DVft+ueKSci8LLULO4PnnrIa2QurJjtnk+nOMAda55YikGScvfr5EpxZBVAa5gshtBRUiSrWZijlCU3t3sIU+EIBxmx24uJZAwz3LwVirFNQMopvzWhVLWgHOuv0dVUmD7nm+Ahb7vtj25yLtKDNcJYuqaqvtcBehVkdsqHxvRUtZoxQNNsI4ntY+341SGeqOQaQEPb/NS5iaAhr64+DlK+oZ7VLqy2NrU0wjUVTxBe9/60Fqrt7bDux5eT5tToEWmWv+aMuEbHpMmO5saMGDAjZdu6NaGzUbNv7Zv6JvNteN3o0K+KTcGY/HKJhuGW7S5pZffKVUR7ErkcGj/pW0XF2O8ec9NZ9SH9zFu9y4yR9+Y6OOr1/cVqkGFIr8EHfphiEir1434uHT+2K0tgmP8sO5HbMfvDbZpHXtJW5vKC3ccPbzDN8fX8/jKyqKT0aPWhVHTC6uAdX2j2yyPNwS866urnlVOrJuWHXUXGCByE4hlo5W6QW/zVLoQKO/QShBIy2lpHjkFo/JvCLABuDRYUaRJtuGdRwt1URIuzzOQMxIA9AVcQ0BSLCFqhayHJlxzD9vKqb6sAfpsbWzPEwjTjjAVsoSSZCyACwG3t1yhtJq30U9C0oUgRxAhawQSwkzOHlXBuVyIS4yJdY2R6/2+jZ/3AeeE02r06inZTuG81TNEtViUM7K4IryL9VscSFVULRz1xh0sPHKfuXs8o+wsLElLHgzCFHZMYcfVwUJ881MjZRHvbX7qgamUp0i5ALpsz1ujFWO3GmSCBEdcrBRHjLnkAzkgE9NCCA4viRRX87KJMyUnZpRYQKE2ZcBAsZQ8q5LorxkHuFCVHDHFOVd2wZLeLtKIJeo8ufQeAFZzr25i49dSQWNV+EDUsZwjD4+LeRl8QnVlt7suYNXyMH3obKdUJtYyP3MWlrzw6asX/PQ3r/mznz3He2OURQTx5hmUnG1NljVVSZta+EpZb4jgvCNmAxqxFA6+3h/54fe/xbOrTL695XDYEaMaS2eZM95POGf0/hUlVpa2lHpuZm1Ek0VCW8Nj3deeU2xrbbS6Olc244HsJCOlrIV28JtT8XxCWlNZ6wVgafUAy1tyVou8LIOYmeydnD0zS0Q1IA7CvDKHDOotb4xUyIE8OSdSUFbN3L56zavb18jvHcjPX+GOM7/86V/y6nzi7uE3fP/bH/D6YeH+3jw8a61FmSMrO07uwK9fL7z6F3/OP/y9Pe88u+JwtEiAgwPRzHf213z0/WtO3194WBaev164vj4QgPPdLcv6wH7/LlOwWqY5J5RUDBvCbrpCVVnjSsYxTwADuY9QyiU1GMaaclPAq6d39JopndShWtuXdcU5z5pgWSLiA25XDCOFyXhfAG4uY+bVitq3nIJxA6vb1GD8VIBOitwAmHlm7PdUre0ijbE46xg108soOOklpXI2hTGnwvY85L9vPWKjx6nM+5bLBsS4AXXbULL6PPt+Wa0tvV9tLgpCVEf2M+EQURI+O3wI3FxP5s1V0FRkcikKnrKNWSzsyfM04ZwvpWzq/lu9X7kD6WGNaC3dJdsQQCfSlOlmHBgtpvTcZbuX6+UOpH6vTXlyF/evnheLhCqPoMLmKjO0lTDKpZ9cAUdan1lu6kpusqoiGWODrmPa9YbNYc8f4vvHca4fNVC+/aIaGKuS2D1rrp1r41t+H55f8ZTlrnY8l3JFXkKFlBV+dE9ZnfODU6AoIE09L7cch7t6valKZW1Lkcu9BjZlL6kKSF8Tunn//qx6bjcIdCXNpr8a0RpdwalvXQ0/9p7Zcl7LhSI9NLVGrzUoOkQS2chXjzbt7/pba7PUmVW9fm0nf1PRHuRjnzzasXnb/3Tbz62LcvugRdth5TbGdlV8OYZGi9h4eRfonlXrse6RLH1X5rfWufQ7FOFvjq/H8dXZUAd5YXOlUE8PwsWEuW3gI7NVVZDqJLOinl2QNDhczrVNPxfA3DdZbWebAMs5g+/LbVToehz70LasTRFUBoW0rCJtF3RPgRMDumMoyiYRm1FIa3uHFhJrj2tJ6E2RbELONmVxUvqleyLqBaolgK/0ddski/VItBBCtM1JChHCgqu1r6B5oIJYHphmU+KSZsJED/d0EILDyVT6eyFrQjGv3n4n7KaJeZpL+GUEzby/P5LiQk6J+XAwYpUYSVFJawGkLmFQzDfg5L3He8+yLGjOeL8jeAOYH3Bk2u2QQql+d3vfhkwwZRiByTuc93hnU9zq0ea2i7UQIBGW85msmeCDhVcCiz8XsGBW/1jA0uPjg00ZzaQSphUFXK45BWWe5czkDIh4QJ0grdC6EW5MIRCmqc0VA6gJ5+pWYC+TNDXFZjO/KrGS1ilbNro8hL6m3JgHa9i2dwbUXr2647A/8O7NkXl2hGkiZVgfATmDW5j8EVRxkpuVP6uyxMgaz/zqswd++ckLPv74M6YavqbZgDYOSQLBchc8tPzZWGowtpAZsW1aNSKExtSaljPLq1tiCAQfEDxrXG3+ZrU5XIF2URab50SqN7LIiQpemhyqoaMJ28V8yanI4EbWSJpSW9dqipW92e6biEjJ35OcyRpxU0BzNs+5gHordeBkoiKjTlBS5V2uIgeAoILKipIQmcBlMg7dCb/67W/4d3/501JNxRgqK4mIiAdv3zzcPXB7+4q8+4C4LHCc+Plf/ht+/tlLPv7ic37y8Wf85sUt8VTqkDrFu8RyUvxh4m//yff5gx99wMvnd0YwlDPneDIPuIDzE/e3Cymu6JTQlLj//J6Pnr3H9XHizi28vj2xm/fsDp4pHLAasCfrA/U42ZFT4u4xk1YhHBI5lbpbYqGaFOORAfZMjSM22WUhUhIGYFIKXpuxzZFS4vFxQfzM7WPi5ct7Tiv46yNMHo0nRJT3JBhg3wnOZw4uM0nAT97yZV03lFYA56QKY5vLKRtLqIlsg5wxJZx6Ukqs62qkWtNkhj61/ct7X9ZYbl5RV5QLM4z4VhKo7nm5KPd1bVUZMe65tly07KFlzyp7UtYavdDZxU2mCuI8p8dHUlZT9H1VKhLOOeZ5YhcEd7hCNXM6LYgP4Lzl/ObMNJVrkrEpn89rsW8aaZrD4ULoisHQ5q6g971ZiuyzcPEOtLMqeNfqo6qUkN5tzYbyo+YG29ikHDuuqEqVwv/L3rv8WpZc6X2/FbH3Po97810vslhNNtksqeWWJcBWA7JsQzZgDQTY/5eHhuGJRwY88NADeSIYEmBYkm1BbbfcVLvJbrJZrK5ivTLzZt7HOWfviFgerFgR+9ysnpCTGtQmWJl57zl7x47Hen7rW61/bSXLcpsjq9qJWzdolw5tdEe/aKlsvtWRhBXhkK7WpiDFM9DSXrdlVj3A1Az0ukLu40Abn3zN6651nhOSaP25zUP/ktknddp05di4bVflqe+VUhKGFDJURs+pWx9ViwEHhrqf2sBylfviA7UxOdBaVSFoY9ZV74UJVsdLH/Paaene4b0s7Srwp9rnS2uQoKEUcEdyDYnscsXtMDHlQ3PBHYWl2nrr9gxkrvr9ng5va6RNVp2hGe5tf7dNm7VZ7Rl3diWUtt3dyeyM6jXwIaWtcU+O1GBkyz7WAG/p69rnuO7ZNSNUm/be3m29BrqCj6/tcU96nEFnv72+kddvBUMtdVMboqlnD1yiiFQiD3qN1lqZSfBN7/3lqkMWuvPkd3Ona6zGv0MgAvU7MSCDO2WuFPv3+o28HuI8mho0rBxZE3euN03naXUoYRyGWr5S32WIVX7aO3fGunOprVXohaEfxnOYIG3eQJBxOKMXDjVToZVpzoV3LqVBIJHQHDY/3FbHBYIRDEzjWCF/iZQTy9Ec9RgHxmHDFLbkNAOZGEZgZJkzRe/MOM8RCZFxn4BASjDf3ZHSFVCIcSTGEY6Lh5FIdzccj0emccM0RabBamVyihSUGJNNQBbK0fbCILbWpyUDnXL95fVVjdzC/nJvpBcpV2dwJIbInBZIiZxPbc3HaUNeZk6z1Vzu93sE6y0YmudttW/bhxdNCIcYWGYzyJ4+escCBinVgm2HgxmhggTfg9HsnJoJsBYZ2epAQzA6/FK4vbuD0r8XY6h2jDW1N2M8VFbcLk61KDFWBwgP1Yj9rBjpTsmFXDKFQBwiMQoQSYtF+B8/fMS0CWwme/fr62uEiYuhUIige0ol8Anjxsapqb7XkRgGPnjrGZf7J7z3ne/zP/+Tf00YlKEEU/BSkMGMHDtLiko3sNb7Xuu9p3ELoqRlIQ4Djx5f8N6zDduY+OTTF8zJ5nC/2zJGIS/WPsFrBlNKFpwCUsqM0wjF4IpuQLthjdgZKWkmSGSqDc9dviGQ6zmSCssrRQjDyHbcoiRCmG3OZcJayhSCFMaw53B3h+rMbrcjhGgtRIKiWHYLDIKN2h5q4d8qUxEoUZEshBKAEeJICS+g7CBfUnJg0IWcBAn2fNFEjEoswiZMvPXWU9559g7htTk96eJdPvwHPyT95Cd89M/+gu12hBi40UzISsjVcAqF3Rj4m28/4G9/9zHPx8L3Ho0ghUNWkoxEInMS2J0YNRBDYMfE/sMtp+M1rw+Q5gJpR5qVw+EVt7efApFx3BKHwOl04ub2jmka2O+3XF2duHz/LeIERe7q2RaWYJBvsocZpJ1tzxlU8wh3xGWsP1MYwsDz+ch8umHYXPDus8f8/C//ipcvX5O0kMPC4bjwb24Wnj255MP3HzNI4UUubLaXXMRUDWht/XglBsYY2IxT68GqFPYXe0pjDTTEwrAZkBA4HI5oKYSxO1+qWmt/LCgVZWgWot7rr+h6xB0Uqx+urVpHbJo8AAAgAElEQVToBuA6Wi8ixAo39We5xomI6ZTq6K59Ai1Wkx0oyLqmvj7veDw1uWltYSJahFevXqHF2hwtyepRhyrHhnE0+R0iQxzQnMklWyayOnqtz1t2wpSVIyBmdOdy6o5iKYQ4NGRG+yz+TtKIa9xJsjlyG6PbIvYDz/6zgqbSHABdW/I4/K7eSQXU+AZaj+c2JO02iet5n2/x2jttn9WSLDByxixNd2irrWPOrQUazuv7eHMM0CGN6u/kY692l2BQUunz1ebD75erWyd+5rRmoByW6k5V3cNAKHN9bs+odRSF1Xp3hHCv/fOa7ta38V5p27qBRd8qNhZniHa2/D6T7tza320sua1nnb32Z4dv+qSZi7cmanNyl5IM8SWVtdxbvrUESZ0PSi9n6o5sqcEibftCQmg1gxKksTx7QsHHVpBGLpd9zdR2WRxicy5LTkjuLbpisISBt1zzjkAlOut4H7v/We4FmFLqzrg73m1uawmTl0ytA1kuu7wm+tvrm3n9Vq0zzhZWWR2mqsjVYRhYBLCfqfqd84omz9CVCv1scMkVVMugZw5dNJpvFwpRAiKxRtdcOPT7eC1SqUJOvadX6NnBFlmVLpiyRwHdOcQFmglDj0aet63QmmnooSHx6BgVfnTmKPYD5gpEVGpT6vqcBkd1i7IaC4ROV4wYW3mN0uZUuLs7kHJi3OxIt5lhmMi5cDgcWZLNxbIYbmraTBVGWohR2O8vEAaDxAZT6stssK3LSyM+SScTzNv9wDBEluVECAktoc3JvCi3t3dspxMXF1sePtgRRFFZKBooxZi1UlrIKVWYs9W5xDgYhHaMRuFOQMVaJuTFaudKbbAexCLeVnNYmKahBgdqRkAGhujZXkGCkUS4s2hR+kJJfh9z3gaPiObchbwbSJRa35jQQM0OrPZIDTyU6sSHYM6bqjsLNXraDJ31QTFIU14pQ888r69QgwmCRQwHAYYINVNaVBlCtBqxIROSsN3W+lWxVihXX12R8sz+dy+JEgHrtzkgDBsr4pcUiQRKUobdln/7//6UL14deHlQilqUPgTLbmeVGshYQeK0noX73YpNXLCkpTq1PWNAMPkxbUcm2bb1Wgd0gLoHvEbmnNSjHg0zPIoRmoSaWQzDUCPBaxjTveHVWt3iZ0xrbd2iaCqEmKqhYXCsHDCIoxqUt+RsyUvEHPgqb1wetJoQFxduj7EQHQGZqRnaQNhEchCWZJIpSGQpHkALqGaWJDzYP+C77z7l4YOR69dfEqaB7XTJ8+uPGPUl77/zjNNnV3y6GdCnD9kOEykllpK4unrNnBN/+hefsMwzx8ORix8+ZTsKJcSawcyoDJb9DiAlIgR2m4Cm2fphVvaI0+nAtA08fHxBKYEYJ+IU2ZaJuIsMIfDkcs/bTzfc3Bwox0AcA0MMbIL1sczqZr00x0fXewVo/TK1bS1zlErh0eUFPAgM0wbiyG73AWFQCBYYKCXxxz+9Q5fC9955xoN95O72ZDKQpbZBKY1p02vJUl4sGOCEFU60BKtgVG5yZ7PdEENYwbtMTxnTqPUtLLmQc25MoOvrPGOyCnDUP/86o6v/XM7/3RwON+C6Ayv7rckocYfAxuhBTOjonjTPKMLNzS0lmxxw9m9FGKMQh5GcM2kxcrRxu2ljKKmsHDk4X9j+11SSGZ+oBTRHgJphXteotWxI3yOG/DSIbGndLnXlDdIUuQW9PLDbHZv7KI8ua6oDQYURq9f1dUfLAw1rWGW9SX3OKrvrg3bSI3eE1k5i/VaxhrZvjA3tmU6g1TD2cd/fK4JKYVmV8bQp0fMl8YmpPoitef2BJwSgBwD6TK7v4zPrAvr+A/p3WiYXPTsD+d55cLkg2uvPPVPoK97fzHfwvTG15dH2OW3vr21e2zRzf256BriNq469QNuX/iX/d7n3/usgJ2BB9gqvLjmvtpDU0iCtZ0NQDRZw0bxaz/4+bS2cY7p6iU0uyPk8r8+Tyy7X7n7sPGDhaDz7nmCtrHKds3Nv37LIPYny7fXNu35jZ9FY87zWg2okwloQmLCgCUtxP4f1gaY6hKEaFl3IhVijw2vGyHvRUn+YBMOMl1rHErDoi2Ori5pxL2DRE1g5Z+02PeKBwyFo0VsnLznLeN6LstiQzg+C1SuE89pD7c5nfxeXTj3d35xy7b/Dn76WKcV61oRgEcFpGE04B4tQLSmhEhkGEyYShTAIlGIO4hRrFm1Ak1IWq2Ms+TXDMLDZbpEIx9tTJVmA6ysoBHKyd1tKNpa8oFBmRAbmnJgXa/q+325ZinJcEsur1wb/TCbUhk2ozplwuD1YdD1aTc6SEvPpyOXDh2gWllTZK4Pw8MGCFmsJEoLwulToy2A1Mylli2LHoSZufE9ZNue0LNXAUWIQwhAZh4HNdlMhx/b9kqjvZuPa7TaMMVCKscSKCONoBrRHEY32H1v3WutmARKpQVntsJy6R8lamQYFr41TiQ2e5PcVoZGAULeciHBaTozj2Ay7EKweN4qQ00JCGcfANAmXFxfMp8T1yxtEhO99/xmKKZ9MIGhgE4VlydxcHYlDYNwMpALTeMH19ZEHb+25Skeuvnxe2ZEHEsUi1yJAqnUJck8/1T3tEkADSKTowiRbogq7zZ4f/OBDto/eJywv2I4viMOGrKA5IWQGAgz1HqIMVEfdMK8WrFIqexsIEa9EMaMtEGRTAw2Camcg7kaWgEZyESIJQmEpMxQYhokYIqnMIJbBTBqgzKbgC8Y4LDRnPg4DmkurUdMmo0ozyhzuG+PIUjInVUI4EYpyl05sbz5nefEJp1cvQZywIzreGhk2HJfEo8c73n7ymP0ucPPiBcvwhAs58vP/+//k1ZeveFqEz7cb/r3fe8rdceHmLvMXnxxISTlmZZML3338kO+//YiXzxeeX73mwcWeadd7bmptD2OlAQPLorx8fWWZo3Fge7llV2BejqTFWtOUrFzf3pDJnGaYNnuGbeR4N3N7Slx9ec1Jhffeecg0RE5p4WI/MsUtOnBWY+4ZuUbaoaZPSmU+NQgWxKh2RrFsP+XEgwtjQtayVKjcBf/J392TSkGKKa/HjyYkB1IYe7S/1e/VTArVcC9KCCM5pSam/S8qhqCJQZjGTc1arwztCkM1aKk5ovfLN9ZwOzfGXCe1FhL3Arktq9Icj6551t9ZZyvtl5apaO2bVj0QQ9OF9vZx3CAiTBtzNMdpR0oLS0pshxFRM3CzKq9ev6pHX5E4My2REAxeG0QMAl7HtPJwVs6SnXMjxDE0QUmFu8OBzbRhHKauIwMti69Y7ZWqNhTIaKlnvFegrvaQVv3bzJoqNKSOseReSxpiqIiOmscq9qUhGENzblk4SNU88F6564zSek+5k2M9IWv9Z41iefZvfVmdeP27rynuNK0+5x6O1oxd/2d3XMSzel/jGRYfg++1VdmDQgMUNmeIhm5aX+t+tn4VpQbiOH+/N8YBzQmTbpPVkdV3MqhwcNrrM4OpOjxug+o6s9y9Pw8G+X6tgtycy0JNSMjanQQ6vDI707DWc9gCLP21msMetNoL9s+4GmNxdv+VDLCg8aotFxDGXIMBpntQRWNpiYdWa5jPW9zlan9YtVd9o6qym4xq5cM2XkcG5jrXzm7dM94d+q4Vpux1ng5rLWUFJv4aJ/Lb65tz/ebOomlYZJUdVEItMaiRonq2nFXrTDS4gKq73CF9fqgAtAhFSi+sV2005A2jLeDZPa/1aLVEmBBQaIK3+Zf1GQ5RaoOiP6tdVRC25tp+v6a0a7aCsELYdYVeUELRVs/m6gCcDe7rZGGfQ39fr8HpCsHrk6rRsBJE0oYgDLXWpiYRzRBSrXBac342k9Xp5Jys/irGSsiQUc2cjidSUa5fH5k2IzEGro8zEChYu4nTvIBYE3DBnNa5YD248sJxr2ymDXdzjXQtCxQLPMgYK4zLahWH2LHvORfmeeaQ7khJyclIZGIQTksClDEYxKlR7cuCUOEgQBy1RfWdjn0YAvPp1BS1ysCAsGSlnJa6Bx1aYes8DgOpWF2nvacyxFDbAphzGcI6q2X7K3m/OJFa20DbjybApUe3RTqVdvGAxnkmGjUjKNY+kL5zSz6hg0dTtRu3MZKLkubCaZ6ZthOatZIemcMuIaMkltlcKqSQqqOVlluKDhS2lKwsMXN3e+TR04d88erA8ZiRSIdK63qXajMgbF92I9DOv322oFV+VNhLydze3nH9+pa9LFUBWpP21p5HnRpecRsjhMAQa6awwqLWyj5I6M6G2r3agVkZpFoBTL1PVl2FYopd6zpmNdnQaq3r3NmNqlIUk4WelXa2Ze/NattktWfqUgcdKYjVsorN1TCMIFvmRVnmGc+IOnCuiBJUiUG5O93x/NU1d7eJzWYiJyVsJz799IpPP/6C+ah88vxA0iOn08L1XeJwZ+cyCGzHyKNHGx4/mpiPQgwTscLjtTF+KpK1tXzQAPNiLWc224l4UYNY41BhwubTpqQG/5wLJQqahVkTd8eFyycX7BG2G5MlSY0zpmS1uaes6rTdEeqtkvr2W5twvZygRflzRlN1KqOt5TYqmygcq7zPGryiabVCNSO8bnyuWrPgfqaqnimuC6V143Antttr2oJl/h5hFRz9ukzhGn4GvSfw+vft5auerN7duSypc8baMa3Gaz+15yig9jWfE61ZfVy2ClYTrBUpEAi1brwUkxNdvufKDH2fSVFbTMn1dK9HxiCrvrbuTOPaVfpn3QZo33fCD9tH4j9vH+/Owtpx8BNqIqsZKzZHzqTSjF23tGmfa/Ps46nkHmcZQDyIvLJFsHddDeTrDAaTif0Tbbxtvu791S0g3NnSWlNXxXNxh/X+I5s+uj8GadlHf0Yv9enSkbP7nL+S1LVpP/5rnMTmh7qevPc5VSrDrzsgK2d19dHSaWDvPcF+pqu94/ZkW0+XO7rah7jUr/f3Vi11n5w/pXpgq1XzJMH68trK0t7TPutO2tl0tqHV+wltNOtMnycudPXuBlXv6Bobn+AINqsfFdabqsa0+1Fx59o/Uew8qren03tBL1/se4Gwb69v3vVbOYtOgOKb1w7nquaO2nPsTOnZ95s4dCNJgZrxaUWvK0XYnutRuRibA2UGownhVrCORY/7vu7AN9V0JrydnOBNSIY0WemMmSVn4jjgQDqTqqsDov27fpk8WcMIuyPUal/qg5qyqGeyCzhrLdEJCLRGZ2om1SM1VWmXUkgVG+7KWep7hyC9VkoimpU4BEPmqBKi0ZujQzW2lWWx7NrxrlTnaCCLZaFuDwfu7o6ULEgcCVLYTNHIbWrmZBxHjmrQ1OMpsMyJkgvbqe6Txcc/sxlHVEPL+IUQCOOG45JJpdK0qxmzw2z9IYXFKNs3E9vtluubQ81UF4NeLtGgtk34m5G+marDJYFpCoRsjm3JuTIPZgTY7rYsS2Icvc9lZpoGtpsNTx5dEKIZyCUvLcMxDEMz4FLNNHj9QQyVsXLODXLb4Bu+x2tLmSWp20YVkmZZ+Jwy6kQxIVpWdiks5GqMVSdYlSXdGdmPwjwfGedCSSeGIbLdG8nOzfUNRRNFPfMOQTfEoRBGa40yn2YE5Xq55eY28Wz/mMCG7WZvxEoCUqQ636CVQVRX542VLLAx1kNWksEBS6KgLGXh5csvef75x8ybxOnuyBBzQxtsRsuUe2ZpnXEJ1Yl2kpsYrCWLkxyJxqqo+8/c3msZKvqfNfREDHY+SzEHO4WM5plQ8N4HhFj6OQ8YLBwh10OdSiIyEBBjM8YCDl3XdoUZWNBY6yXTQqCwvXjEZ18e+PjjL1mWo81vKYwkclCyWPZ5isLV3S2fvXjJ1esD78SBYYCf/sVL/um//IiP/+rXHOdMnEZyCZTDibQsLAr73cSPP3iPaRopo3CgkKLwYLtnGiLOH61hhJIoyWSBDLUvZFHurhdQYbMRO+ubC4YBCickBi72W6udvsgtw3F3l7m9nVkeX/D+sz1bDBURNyOq1idWk9Xyej/ONl8OLdS1jat1LexsOdmQKyAlmREkAWRLprCcTAeFaKRBOQtZQFJn1lQn52hGXpP25oCsdEBjv2yG4QrBIh4woAVC/auCO6Fdb8KburTpxOAkFOvPr53KlQEufYT9OT2z6A7L2qBrsF9/oDufel7T7xmtKDBMsaIKtUKPhGHY1rVY3QdqHfg9eFp763smtFYouFjQzGW0rwCtxZTZIOfBXyr+wA1ZVkgfba+V29A8SGzyzdk/UQ8kWabG248YrLxCL3MfBx4UrGiHUveK20odJvlGQV7/s/nRbxrVUte2+Brfu1aVl6s/fFYrk7S6LXf+/LZv7t22OXZl7QgpNsNi9eI1QNpu2gIA9kI9cG//CU4co1prK/3P9vI180mf/3VrEvU3q/u2InpaL8mv8UdKJWBp76FnN+uypNSzXydU9ey3zVFbT79DLN1hZCWy2qI2/WXnzXdnG68Yx4MlB1bn1YWdm8pZvbVlu7c0Z9bd1fp/1b6kdRiNe8TnWftMngcL6naoYxN/z/a5bui75d31fc+wyqqOxPt/fnt9M6/fss8irE+eKwncqTlThHLvBv0enl5fR0JNgbkwP99AVj9UoSW1/sevIIMJhVKMEdSd16JkjyI6jt5sxDa2KMGyQquNDs4gVY+YR5Bt4GfRGtaRYhcMyOrX/st75DfrSHgToefz1TIcrfDToTqdJc0cyioOquXrVOuCUDiABKZhRwqReUmknBliJOeFiLDZjIiMjIPNR5oLIcD0eEPRwDjWvoJBudxfoCifzbfsH448fPiUcdxQtLDZThzurs2gE2F/saHobKGmMnB7nTjdHbi82LLd7MhUYV6U3cagQSUle++g1SdXcrHWF07X9vDhQ5aTEUbEADLANCl3B8vwnU4LS1FSCdxeH0m5UJIxVILy8NGelBMxBKZpS6iwjs04UnLmcDxStPAgRY6nAzEa/GM7WcuRYUwUKRTJFm2mC83Wcyj0vW1QU3NAc86W2cQYD0NrGWJ7ZRyNrCfNS9uPQ91H85K4ublhnq0ubNhsGMeRUjKntFTnyLK2RQs31zc8fLhjv58YQ2Q5LcCMpsEMnFrPOQ4bDqdbUlKWBEPcElJhf3FpiqnAdhNZ0h3H08yLL27RLLz73jNKUqvZVIESyEFRMkKs7Kjnhmszput+F7H+lZVxn2EcefrWE3YbQfPM3d3MMAmDRLRkwkVgGAf2+12tdzCnSrVYBisFhmFj61sN9pQTDtEtdQ/lLM1AdliSjasSRpjpCwih9mlL2eRbL7GoQqJG0LXYCXaDVoJYTWc2+VPI5NL3SqpBmRY9roqzxKU6vZBOixk+20d8/Jc/59d/+Qs0J6ZQ/RByhfEJOQtzFobNhgcP9wwDvHx+w8Vb7/Bv//XP+POff8nhNLPfjYQpEMKOORc0JYYA293A97/7LvOcOR6F6zuY55GkmSEXZMScUlGkSO0UaL1YS0qklBjHwdAL9SyknJColLIgIbLbbhljgLBwOByZT3B3m7l9vfCzX/+a6cN3eP/Z1pyvMBIZCVGMTbnBBUuDPzmEKqXSsveecZUxVjlZ59dbmbgSUEFzoIQMg8FpM1WPSNUZ2vfvX2fUuEPViSdCJS+qvUPR7rg6m+m9jFrPLqycNznXB1IDMet6pjMm07VPoB1aqvRsgqmuVY2QdL3TToEb+tCCWKz0tZPvGCQuMA6hBsbs+7l0pm6HYq4zoHEY2GwMNno8Lb6Ruwx1y7pRPHZjf13bl0vxvHKXtZXf4ByWe57diZW1stT1cv3uAW93OrU+LUiwukrOdbSWus/cLtH2KjbfwQmFVhBg1aq/7W5a2/d4gM/2SGlGdlvPN96i7svsjsibe1N9LnGHxsd2ngntz2me0Nn9vg46asRfXWqu57t9tzQP5t74+s+BCnVcj/NrsnH1z56tlJbKE7rDIff3iZ/bM7IovXfb1b+rI1Q/6HdvH14nM5T13CmuGFqwxT+rq3uvLm0/kvYxD/Yais9tRF09u70EnnkvlZimB35YIWNs/E58s7a3tdpensBu67cmd/KVWcsWn5vVuVScOOecsdbnowe0zuft22rFb/b1W2UWrUal7/y1GHGB4eQi93TdeUSmUuCv6wXNMKtKcgW4b9EiJ6mptL5nh7NGLHrK3u7jzKlNbFaP1DObBaU1y/HneQ+YWnvpWUHHhK+zkYMr2lWE5SxLKg5/WUXTRBCPDq3n0aNLTcdpo4hXzvH1/s3z2J7dMw6ByICIMBWbkxBHxqhMtVyuk/pIM5xiNCjVWAWPFGUM8PTxRSeewXrLXW7eM1bAcWP3UsOvp/0elYgijIMSwoRU4pTy2AyNKMowhHXQsWVJQxMmpToDcobyQcQyMtm2se8XQXh2edEVQw1MpJzpC2/vFcdIb1br9NnCWGuHcjV8YqWqb6yldX2CCNMYCKIklBJDi3SrGiPnWtn7Yvle9/pGqWQ6q+UmpdxqbNwwm2u2M6dEiJXBE4jjyDAOFVYpZ8FGpTAMkWmMBCKnuTCnRIxWnzlME2gwBlsVNmFpZyuIsuSZq5epklpUGpe4I25mvvjqmi9evOKjr54T6j4borUTWUpVpDUqChYRbka6G+94pD/WAUesDjSw215yc3MgpiOHY2YoiW20OT3czSynzPXNyfmcWpAp1PM6DIORCnUd32qi/KRElFCE4qyaDfJVDXYUMAbdhUiIgTha/1FrjDKhobDZWZBjOc4k9WxmzyBBrH1Ueiw9VhbXnsEoZ4aESIfYhzEQKMSYefDoPcL+He7SdVW6lc5fIaigIXBKytNHj3n37Sfs95HXn8+oDDx9/zH/8D/8Aa9ev+JuWTjoxLTZ8Ho78fJq4OrFFYfbhT/7xSdk4BQKu4dbLqcLOFr20mswrcl9OZsvQdlOE68PBw5zYcsD9hdjdc4F2IAYmUvOGTSxLDDGgWnMvLq54WIMFJQ5ZUpWSspEuSNIRgitJvH+pcAwRkK0IEjRbLV3pbLa6tBknGmBYAQmqgh3kAuHRUCybceiSC6EwfaltBp62xceFPUlsyBmAKpDxbqe3Z27+n3VMyr5UsqK2TQ35+1+XWFDnqwMcqXrRP+9T8g5UO3cARD3AM8MwP4MP1P9P338K7u5ym8hLSZv/Ps5V8O5nncPIGttDVLmufVXvO+GNNieD0L7O1sNX+1FW2v61samfcWy/xltjo8jn7yC2aGK6/fQ+uL+DbdVlA6rfNMfcwimC6G6Daqz12rFgBBdp3LmrPsbCN3BDcH3SmnBD2BV0kL7ju3Rtfi4t9Zrh7MFlc/3RmMbRSq8sAcX1o5Cn59q+Dc6hvaB8+lRUO1BgpZYIHP/GDdei1rP5jaAtjF2BFvb+3UvroMd953Gc3jx+md9L3twsI2j/cr33VpzaDu7pne6o+wOqmeJbVv0OV6Ze3b/te/nO9/vVT8XVszJ63ZtDb6pYHLHRup7pm1W8f3jiYc2Mbgd6X/3NWnBGlmfif6ubQ5Xtm47SzVrr+3H67nzcdfPqLZWHt9e38zrN3YWU1ZiZ+LFzVJvG4FqZUKUZhiCO03nae1CtjC1rGGsK9RCkO7HYEaKw/pa0a8bph4VFJqjoEG/fhOK1aKgxh5YgFAPfodmC6hlo0S81qIK8lVX3jNHpj+ALOeCweag/379WScIakBTdYNMfbjA+UPWdStNKVEVtgutRnbSa9tCCIzDDhEhV4cGlKyFQiYwGWNk0FZLKpq52A80DHxVrDFumsDz+qslZYQNeVnIeWEYJzKQdTYm02hkBmbgBqaNb0WtetbqX0spZDwSX9CSDU4qgsSBGDetZsyEoFZCm5pJqHtKRZhri4QQRgyamQglG1unN0IWd54TIQjT1rUgoIWczBSa6wIFEZZktUZDDHjGwBlR0dW6SBeXvm6hptHW2cdmFFVl2Gm3e7Zys9n2IIy0xbDvAtSMvmW04OLBA4ZxZMmFX/7qY0IceefdJwxTsMyMKhTr57fbP2bazSQSwzhwvBUGItvdQJgip5T57KtXHHLhq7tb/vLLK37yF58hmknZFEAWZdxt2U07bq7v0LJYAX89CFkt2JJz/VGwbNA0wRCUeYbTaeGzL79ge/mY/bCHYA3bd9PAGCNpqey8h4Vc3Ii1Gt39foMEuD3ctT50QxwA4XQ6UlTZ7ffsthMPLjao1mb07aBJ6/umgJinQxp3FBlZlhObCEM+kdKREwNffPYaAd56+oDd5Za0LEjqjrKEpTpLFZaDIjWC78Z15bywHqFxIKWFqEZykbcRzYFNLDx+WBi2hbs5EXJBULJORCkEMkWTZWjTkfl0YggTbz28QPSOP/zD7/Gn/+4pX1295pPPXnF7CgwR5lLIc2ZUoaQDn3w5s3v0hCdPv8t7T98ifXbDPAZyHWQzOIL1vCsIucxsRuXxd96B4Ut+8fFzXr5e+Lt/6wPmdODFi5nnLw7sHwgXlwY7FRWmYSLGxLQvfPjhQz769MhunJhkYLGqTWPhLVIRIgbByjmhweSJE5AdDidKORBDZLfdEMeBIU6gJpe0yguDmCbL6IiCRKIOLA7HX0y2LwV0gUM6uaVt7mA0CPucEseT9WXdThPbceDickcDc5Zcg5celLJjniskNjfZ3R1gJz+5L+u7IrmX3dSebf3rrrUhr8X1pLjlZg6CcHZfAZxv0UMsa6hry1ZVBljby9p0WNLQSjCsn6nVmtMerXgxvWqv5W2OIqz+tpqRUomo3NHOubYSWjmYCBWmUaHr5lIqaj1BVVsvOmei9hpv1s/WDk1VhKHW85/NU5XnudSsigSihJphXo/cbIdU/Nyv/K+qs+tr2W9q8KjvBvuvW1RuqKsUUgsA9M+dBZClr2NYOantE9odrF6B7fBkWY2z3rmSu7ijhGpF7NTnqxJq6YNjM1ZuSbMbpX6nOXrupNX+pP5McyqckMf1Y18jqtPhTruTFvWktK6eu0Er1fMAACAASURBVF5kbc9445dvOEirj6w+2+5QP6BhtT50j0nWNxDpY69TJv1muOFo+7rLApcVfmtCd+RUtBH4NXsYsCL0gGet3aCWeu6bnvOHV1IrQfp6reYQVtnkapJmRwHWc93WjM5bIGoQVM8gt8SH3r//t9c37fqNnUW/PCrQjsu9kMl6A/gGXstO/76cbXK7KukWoSitOS/aDqzwNZtrtdcVVhvx/HPrGg5Zb1r/pOuaqlz8UHfYSBV6/dWb4lxDXqT9dzVAbw3iX1VzsrSSbfTP67nsuqfE+8fO4RHtA/4j9eqILg6k6iE/9M5a277a5K/X2VimjeLRXP+dsZVSs8NSZ7AUBQksyZrXD5V5NVWCElIBsexlyoEiRjJRstVE9ghfZ4lUNShbStbwWkIh5VCzExbZ0loDJrH2S3Iq6BA4HBYkZGIwGGpKC3Ew8g2vG/T1i8GyfnmIVeBGUoXtmlCusxIjhMH2rgqxNoEu1UBp61szroornbXiWq24dkdxHVRRqpKvwZFOhtH3iUXyCnFVcB9UWwJsSYXTnKwOM07EwTIgpUGoIEQhjiNoRnQhDsJ2s2EzRIYxwDTCFJgeHYi3xnZ7XBYjuJFSnS6FQbi83PL08iEiynyKnE7HujukkicF9tstQQKn46G32QGiQCkLV6+uyGHLuDW4qaqRrnhmqxQlDLWPpVSFJBGp8LIQbbN7xvW0FE4p23adZ+IoJJmgKEOM7ZxYnUk/slIpTmJQXt3e8fFnNzzebfjgqZF5nJLw+vURRNjtdtzcZnZby3AOsVoOpTQIfJdIVZXqSh4IlR69w/ns54JEIWXYbgYePdqzv9xyfXViCuaHimAtadRansynE3d3M6qBi+3EUZWnjx5R4sCrw8Kvv7pFk7kEVPbpjVCh38LF/gH73Z5I5HhKTMNAEpcKAa3tIJBQzYpcgxkTOQWWkzm1xpwdDOlQs6nqmCnMGCoFkhZORbkrgTBECBEVg5gbP6DVC3uGrmVCqorwo6OlkIpymiOjKkxWIyohGOFMbfY9RMv+WsZ7QEpgEy1yH3JtBaOh9x1bQUdRg1la/b4FcmZmKAvTNNp6CVA8sNq1TdMZpWcJRAyaHlYU8l13vgl/bXDLOhZvs+QMhC7NvRa97aPqFEroPV2RtX52a127DOpfrt/vG7bv6H6dq62Vs1XcYa5cAX0qKizTzqs7fX7fN234nqnE90KzDWpGBTHjeTV/S9XlhtiwewU30uu7rf9otkOdjqKF5MHpNsXSMoi5lOZII6FCj0FWnAUeUDTnkGa8ex9Ae6zWOTESQTmbYVm9a7cRikNH763ZfdvHdIO0z3E2v32flP7P9vvOZr8eje0abXtoZWfQ58+DA83OEFAVnBC8OYtas/a1NYoLRRUoGtrzbTy2MGuIrf2bNgZ3Plst57mJulqXro9XPtFq3vpY7s+Wtr9LG8P6krO9fm9Sz2+Gbzbfw6YfVphmWT9f+6L4vN+7b8uRrxzflSRqTndbJPo7Njta+vuxDizxJuS4rJ5/PzDQ96Q/ZXXevmavfnt9c67fyllsDa5XUQqvUay2Lg4L9UipG+TnSg/46zaJrM4CFs0umZZVWTuXiq4ozauBvhIM6+e4cvZ/O8nN2WZVM7wM3mj3cmgkcBYFFc4FelOOVXH32sY64OqDWVuN9YHr3w1+8FeH0XvHu7dognVd51GaIlZWggJ6v6h6yLUsfZKbcB+IweA5pTZRFXtZcoGYupIRHPa59JRI/fkYg2V4tvsK/zWBF+JIg0yIzVlRJetSC9ALRc1BC5UIJmIsikMcCTIwjX1Zc0pMITBOI0Gk9tiqcxpWwgh4eLkz9s/1pkF6hm4FAbxfmmHCfqpCsq+Ht3XxgIn6muRMJrfzsTb0LDt9XvfS961nmWxcBsk1SBqlZ5EXVaQ6IQ4vKjFUtouAhkimELQw6EiQyJ/9/FO+eH7ND3/0Ho/2OzY6U1TIMpIU7m4W7k7CbnfFfoLtBIdTYReF568OPL89cdLIg4sdf/bVFV9+/pyf/MnHfPXlNXdXRzaeLQ4GwdxNA/s9vBUfsOTCl59/xXxarPdVNsbSZ08fMk4jn3++cHdYKAlSDEyjIQ5e37zmn/+Ln/Cjd/d8/62BzXYgEsg6E6U6HhEGlBgK0wC3d4VffnxFIvHjDx4jMhBl4vnVgT/5xZekXKHxXCGhcPH4AU+2I//+D79Dycl65hU10iB6w/dBBqQsXL16wZ/+6pq3Hl7w1qMnvHW5Ix/gnccPKFG4uZv5/NMrfvSjd9hMhRhHooyEKaMyIwRKrUGxHGax5Eq1tlSF07wwH09c7HfkWOyD85Y4znz86yPXy464e4snTz7j9dVrVAJFvM9jIMpAUOW0FG4PM3enwi5syNsn3B2fEjYTcWP9Si8mg3ymStqTZTFpkJXL/Z44RZZYmGNhu9lBmUkl29yIvUSZzZ2OcUQCLMvC4bbwYL/l6dMNOZ1IOXN5qewv9xSFvChjUBgSy11imkZuT/Av/58vePz2W5QxkMQcyEGDMTXSLfsQhVjbJ2ixnrIShAcPLgDrHXtzfcfdXWZeLMhzebljHK1NA2rsxm7ADbG276l6jKEiDphAhClOVvNcCqdUrAyrQIi11rkUbm5vmVNu7SEUq4sLIbDdbHDolQiVmGkFg6z6omSQ4I2bbF848qCLrg65DxIgUgMoPYPpLMEiUg0413GlypJV/d1K/pyXg1RDT3rPZHNUqizzT6oabHfdB4+VgaGChkj9UG1lZcakt8yytXAnsWZYqx5zA7T5ttJyF1U+a2OO1pr102D17Xmp7Y8UUNvrWTOe6RARpqo/xrG3Mmpyv+p2DQXvFedr5TJ83QfP6su8N+56nOeGuKKtvszJPXyZ3OHQGgDr/sV5AHmdTG4O8r1AZDPQdeV0tKt5AfWfbsf0Ea/rXT3gaWOrX1HwRmPeaoK1Xly9Fz4X6vtoNR9Nh1qtcIxjtQnombQ37MS6Xywi0MYdVOpmqTKj7rnzdTAbtpHGIGi0++faL3L9FS1G2hZqVNz7byvSYNj2uB788/PakFH0HzpMXbXPrf2/G732+3K+burnz+2ItYMYO2y9umrNsfeezr4nV/N134lbQ3Zbi6e2vgKlGONzWdmfddiqLiX6Tnc50ffvyhZuP1uHEr69vmnXb+ws5lShgNKZHFkrs1V2zf7rB6MLkRbJrBuskwJ049qdBvO1elay1WWt9pYgvVm5djx5+207pBWaeC4l6c5OP0C+uV1olJUDopw/3x1lqhA8e77QGNE88llV/fkRUctKqSqT1MbtoStmd0Zc+nac/PpVtGZ4v/7qtaA0KaHNNslmlIlBdopHcLuWxpv/Wk2WILVW7az20V+6ZrnSYrVNsVAnwoVJnXMNTHEibELtI+g/t8dO42hZxwF691fhYr+1+3stZ13/cjKnaV3v6rj4+/TmUuGRa6XMvfkzI0xofdvOLl3tVf9MaLU9IsZ86vexsayGoEpw6j3pCjdG2+MxCKqJpNafUShYQmSsZD81el4iqpHUpl7QoOiQmPaRL69e8ZcfP+f3f/93eO+dSw5f3bHowlECh0X54z//NR99dMuPfvCU739wyfvf3ZMWIWnh1e1rPrmauT3Co/CS//a//+ecykyaZ6RALDCGodahCiVn/uqzl3z68jVD2bLdTdR+20RR9sNASZnnv/4SRDjVGuAwKBoKUYBSuLu94V/8b/+Kux+/z+Uf/g5xCXxneGzZmNkYfMtgWf0hZYYwcCiJz26fk+aFH3/3EUMY0bjhxfVL/uj/+4hl3hOGyDAsKInd7pYffOcpH/4IMyKLncu81MCKFIIxKrG53PB7P3qbH/3N75OXmXR7xdXNDSMb3n/vEgY45BPf/c67bMYLXr+45fr1wnYXuLhUlmWx81IpYQonEOtuaSyAdX9kRdNImQtlShZbyhNFT3z2ReHf/vQv+Vf/xx/x1asX7GIAjSypWigBCJkY4fXhhldXL7h9dc3TXUAePODF9YlfffSKF88PDGLOQZZi767WUkZUKQSW5cD2csflo4csu0CSgMqWIAcMqh0pQYmzMA7CsN1YOx6J/PD3HvDLXx15/tVrtsNEnISXX3xFkZHtxYbtFClLRrNBgDdSePvxlh//4BnDOLEPyiYOhMFkkEyBkmSVRVoF2Zo1vap9D4Fps0GWheOcOB6OpHlhf7nj4cM9QubucKg1z4HdPrLbTZAzuSRSWmrAZiInWIKx9IqY4ychGqpBrA8vIoxDJDPw+WdfkvJi6xggaWYhWVuXypIsUlb2klYbrEIymzy2oFepJR0xWqCjGYhitUyqStEFISKV8Ke3Rey6wrax6dOcToaMUAtemD7IXcatdbjb/9WoXtt0ppNrjVatWbBWMuDw/jXjvipI0R6AxTL6oco/e6/Qx4ydwcpHRX+57tg434CIoLW2yzLYymkeKSwghciONC/c3syoZIpGMoHjvJBTZjNtqrNixHlD9P7DgTgY4dU0WP36qbZdApgmI+nJ3iJpDUugZz3RDpVtvtP679oD1m5PFA9evHH1wLXP6xuXrufdnbR+XrwJe/MNzqKk9kPfCvfv7x8Nev6NNdLMnUvtb9nG0j8XVvu0zlYBcMhptYHcoWblwOr5z9uA23y4A7aas5WNVto4pDl567lsPqDQmNeaQ8X5nJzV8K3mwuXUGiUQaiLDUFMVpxGqnazr8duNpLjNW/d7LbnwIEWv3z4PZgNoKP2efr+zZV5nUlfvoHX8rObX7UVVg1J/3fuu5qO/d+jmJnJ/4trnv4Y/6dvrG3L95pnFlVMHvjFCi6g4Na5vkzXEYHWTFtFBVoZ00VVEyQRZzlb07xHhJlRbBMvGRPZoqyAOmXQhVPp4WxQIPzidiOLMyat9Zkp1MEJZFaTXKNzX1S5AzSBRITiNWnwVIa7KcZ39Wked2lUPt4TVPLrzHYSg/vnzcaym2eakUatXgbsSiPdJiEaP2DuDXYwroeSGja2wr1sl02/1EKWSGHjd3RhikxEGS6s1AEUJQ12jXHCK+fYOAhKFcRwsslcZS42tdKlwpi6UpRLwCL3o2qO35vd2KWy1pqkp1V43WMdTI8tW17naGzVC55HYtYzz+Wg906psbNFFVWM9bQELMbJ9rU4ChRgD81w4nWaOtwf2+4n9o4eEQSjLkWWeGeOEYlnHLBFhtmHHCajZoSgcZeZ/+Sf/jr/x/Xf4x//5d/n1F9f89GfP+Vsfvs0ggZgWRhHe/eEHxHcLH0wDlzthOQn5euEa5QfvPeO9x4n/9d98xj/96eccX7/iYhDKBElsTufbRBgGQm1XkRYoWVC54foAmzpJS04kBZGIZN9DylDnMi6FEhWC8HQa+aOf/YLPbw78wT/4D/hP/97vc/XLP0KPyrFEUpkZY2YzRDRHbu8yD6Yt/+Xf/zuEGHl19QWlJKZwzd/720/5L/7Rf1Wz5Mo823dUEywLzz97TcT4WyEyxUjOB0QC0ziiJfNnf/qK/S7y3tM7I/mKI0PcoLlwvE1kxGLs4cTdYWE7bdlvBeVk7XcyxFjaugsRzZkxgOL1woGLByP7h4XxEDhOgXlQ9qcjqif+4T98n2V7wz/53+FwnZk2gWNekDESKIgW8jwSJoGi5GVEY2B8UhgePeMXn33BH/3kIz799AWjFEZ5wFAORJ0rqQxQIg+HxCe//Ij/5r/+n/iP//BD/rP/6PdIn/2MME6EaV8NqSMhK7qZyEfl7osDh8PM9V3h4eXEdpr48e99h+2057Qk3vnOdyhFIMA4BKKMFArjGIlD5Be/+pxPfvkJ//gf/SGbYWTOJ3PQwkTJAWSxIAqxNkD3AJ+H0gSCQfqGAXZP94gIT5aHlCJ8+slXvHp54HCbePT4kidPn7IsBwTh6uWRP/mTX/DgwQPefucdnj55Rs4zqbxmuhBy3uPdE5cCUqzecQiBNFv2SkJAWPjge0+bbEtO5CMGe41RyLmQV0yYLj+KWZCWJmz1cwJamuzuMt5lssu+aJnPpVRHwxxLbzRfSiFGy6oWzcRxy5IWJCbiaLJKc2TQyWR8Q57E5sCd25h9LI2RtXoW6zpr10suF60Xbez6KHSnR9aynI7AqMxQra+wM8H6OCJiSAscCmoB0FLgciNQRlBlLkfGrXJICzkFTjdH5mWmIAxxIC0Hg+MHGKfAuJ0wEpZCWixIWlIlYFvp07R0lJVIqAHItlL3NMS5bi/lXDe0X8Vu1XeDXM+cg7JyhHJZzVuze/r9aiVEjZFW5FH2mjQFldZCohoFrTbY73Kel7Sfd/tj/cS2on1v15YhbQ7cE8P3iTv+FjxIS7cfXO/G6rynktq+9uys2Qyubz2AWxpRzrmDuR5joJRVWxzpCCXHULW5pzRb0O1QI+2rf1ezOdI9Fn+HI3eH0Wwle26uz7jv6PVnS3VmWa2/283SzpusakbptuR6K1bb5WwsQGezOPOrCWrzWdrXe2C8eGu6leHodzF7Z4Vkc6ezfsrncM3NoOh5qvzb6xt1/cbOYmNx/JoMlptBLoQUVpExE4AN9qk9QuMEL76FjWjufAP7IXHf0AHp7VDLOoO5dgy70FpnNWF9gP1wroR7c966Yu9v2Z3idrWv+ffs316fca4oznOb/r32zo1Fz/6jmFHufqa/9f1nno+TdqCbaLwPz3Whs/phzl5H07O6YOvuCjzULKMZK33dS4VqLctS+yFZFD02iFSf7yBWv5RzMXbEVTR3HI2QJFcqfheMEoxkYBwGNqNF9Ifoo+/R2PVcruen0wp0WKfJ4r4vfA/6v8/uQ1VQHgCo82fCrhtU6wy017e2cvOVsm9Qsjqu4J+VjEyFTRkYNnBKC0WFSGEM1m6hBFOIATXikQxRFiAhAvOdcHNckGwKcNbEkmbiWPsg1vnKS+Kjn37OX311h/zOU95/e8NmnNDB2F1T2BAv9sx8zOdf/BV5mFgCxlbZamWgiDIXZRom/s7vf8Dv/vA73L18ybIoP//lxxxPiWGcDBaowvE4931SLFoZ6tlwx347DRyPt/zpT37Bs/2e733wHof0HE3FoJBZSFprNkWYl8LzF1dohONBOM1HM+yuEqdfXhMGGMfA5WZb2SsNklZOs5EAVOM8Y202SoGcMgHYbCLTJlJChCJotn6mhGCGPrHWjgEhImFAZbH9HHZorSvzNjgZIQ7m/BCUnOfqQm4onCjbwACEbKdf2FDGd3n3O5H33/5jvvrkK1JtZZPVYtQJoYyZJQglCKfjHS+fv+R6G5k2d5xefs47zwIDFwwCX3x2rLU/VZaKEijoYHvzmK+5m684Hm4Yx111aHz/BsuKLoWbm5k/+/PPOByPvP/+21w+2jMEm9tTmllSQcjEOJixmkNtVWLBgaurAx99+pqHTx4xhIF5zqSSoIDokVTrIaMzDRavpz53XNb/ljs7oyFGQog8evKA7W7D7c0d16+vbS1rxi9MkWdvP+P19ZHnL1/bXgwKJIOMyskkg2ptyl6YxgEJHb4eBjvx6XhayQuroVUKS8osNWvYzemVg+AyuZUo9awPanLSIZlO6LLMi8kAr2eWeK/eVmrfSCMNw7N6au2FUCGfqpNGoiYmV47BysDsAs3knazeRaTzbDV2OtOnqr11huvwItpIcepENOfSDHw56+nojlJVft30rN8Pq3k02FxBg7LkYNB3PIQWCONEjIHdNCFaWLQYAZYYairnbK2LJCASKSiLJqjz7M5Ng/Sd774VFPBrLlkxt57ZKec63Oe0f65/pgWkq+6xqwdPzCd482w0HVmzSc1R1zVpnWLlJ9U58WCDsgLlu8jXtsbNS1i/qqz0ctOnde2aZ2mjXrff6AH3sPq8Z7nK2Z45v9yZ6Vnu9tZvyAgf4xqhcH45ykd9HuX8FRUq+ZlPOta2B5//e3tynUVrnnsNrLjeX9nEPaLvzittLPd2ynoIZ3PzBmv0SqDcf5f7H/OlteCT2fRu9dSXOnvuGunm925IuEpUifZ1PZsbZ1n+9vpGXr9F64xec7WOmvXD4JEgV4UdeiFW6NE1Tz00HmHrwlDe2Mx5DVFcyUmPSK6jSusNKQIlyOpxitdFrKFMUo05v2JYQxV8fP5G0pSx/061wkUVvH5QRMnSY+BnUcZ7SkDx8yfut7YDWSq7a6ALYB+qP/t8ts7/WcSUdKzK1rOEIh1eIWDEBy50MXIQiuHmzWCrEdxloZRcDerKJitixqAqBjByAVEYghDDUAll+roECWSNwIAqjOOI1/XYewulmLEXZOB0WkAs+zkOta+mlFWjV7Fef/c0iYRuuPiqSYuWK87Gi8jZmvpsetsSEMY4wCC+C1YzXvfEKgOpdZ07tEux1hAVyls8GGHOhsFR1KKRuiGOd0ybLT/7xWsOp8x339nw7tsTZFBGRIUQEjIcmcaHLCVDKAzAiBAvR95/7yHDuCGlgd/93hOGQSF5ZDCw2QT+/t95SkrvsZkGJlEGlC9PN4hETvPMr758wb/7s5+ip1uCZNIIQwkY1Dhw8WzHfDqxHJUhCsucefXqyCCRBw83/N7vfJeUC+O04TSfiFE4nI6kOTPPA69eHfj0+a+ZsDY0ReGYlA9+5x3eupz41c/+nP/x5x/xt//wD/ju48CH72ytD2VSZs2GXIsRVDilRIyZdLxANJKjcHOaefn6lovLiSkAJ2sp4tk8i1WVZmRYcCOSMRiUoFzsEhISV3djbVNhtXAljEC2ur0CyoLEDUjG66ukKvwghTBkI76JG5b5lu3wmOPdkWFSxiBc392gAaRWNQqw2WwYLx7y3/0P/4xffPScTz7+nE2AE6A6tICFulGXhbeePeD933nEXA788Z98xt/4g0teffoLHk1KuNgRcuRzXjfyK1FjyskKcQukwu/+6EN+/OHf4umzR3z10QtiLAwEC4xEQywMo6L7yJNHE9NofRo3oxAYKMmyMXEA1Yhqh2BJUZMN2w3XV7c8vz7wB3/zfUhHI8cqNqehRIQBKGa0s9IrbtC5o1EtLXNQ7HcSkq2rQIwDD55cWDb4aIRPucwUVTb7LW9dWMbtbj7ZXjqeyEsiTNZHMMZICLVB+93RHEmC20MI2vp6FoUhGkw/59R0m9Y3aDLDZchZ4JIWXPP6xAbzhBZgymo1f97gOuXEEDq79BBDywJkzNk0v6gGKVSQYmUHApRwqoGflV4rXT+5oaorh63ptGbQtzeiWdj1I1mLOWGthxwtw+CKqN97xXHgtVC6Cg7DCpbsrKr2HAs2B04pg4HzEY0MMfJkG+gwx8icq74rShwCWh3xWkBKAGunJIOhX+i6ImD9W+1Nvf/xPcekzQ00woJ16w/pZTdtnulGd2+3Qt8x/T+gb7LhnkE9z35V527lqPn8ezDEvlv1ZSVI6vEHD1x37decqebU+T5xB6uwLuGwx7khYHMWg9sD5jylylilaA1KdztH189sNpB2n7VNj+/ZlfPSZkFq3aG9t9uOtP29ss2aXQDFW5vgU2telcEolVUooDpc6wx5t9t6QmP1eXyb+55WC+5Q59BvCjgRlK81nDukfpV7n/Gn3kf59TG77WfzF0RIuZIxtYCANGg8rZ60v/P6Xv6DNYHTei+vx/bt9c29fmNn8f7Sej1D+/29DXq2GYVVmn+tLusG8iyg0g7MedZMz4SGOyN+82Y0rKCfNpa/7gVWY7wXhSl+0FdOcEU8tbYMNGHqcEw3BOxnzTmtB+3r+k6uJg7nhAwSav+4dXHzSqBAFVLQC/7vzzYGpayC0NtRuGJUlHuJxkYA03DrNfLmcCfPAOfKRhiDwW5idY5jrMqotiapJgjDYIoaWcNDOyGPYfl7fWBXrgIMlbkwNnY/8/0N8+8ZOt9D8V5Uj2psnEWj19Fqm8pq1H/NznClpb3/Y6uhrR90pWtGaY8CinQH0u8ZxKA2Q4w1wu6BCI8NaAsrxGAZgXEUigrj6HNsPSxVA1FAg9U3fvViJgzCg03kchuJAd595xE5G5x2Gia2W+FwmxF1lkrhO88eMA47llMinQrLnAl1zjMDSx54++33efrkA3aXn7HIiVdfvuB0nDktM9MwQE5ITCiZz794wcurO7a7wOV+y6YiBeYsLMtMHGw8xijZtrPNY4wokBZ49923+MF7T9hvAi9vT8R8ZCMXTXp4PZRle8yAttYsEEJCc2GZF1Ja2E7CxXZiICPBslohGJTK+oAZmNoMZIccVaMmGNwt5VwNdCWI7d+SCki23VhcZiwYK6sbCyYDAjBoJhfheIT5cGI3HXh9c8uDy4n9zgidiljWSmtUd9htOCXhX/1ff8KvPnnO3dVrRlGr1Wr7y55lsigQQ2C3Gbm82HJ7F/n5X/2aP//oC15cHTnezsb42Yw8P5N1v9YzdDrCaQkQB8YYCLHXBstgAaHD4Y60FB5e7thtN2zGTau/K6WCN1XQYo6HBjM3gxoq4HBMHOfExXbgyYMdaXYm2NAsQz8zHuFuItBPS7N/3YBUoljmrNTWKikvDINaG5hhYjllu5NS+/0WtrsNcVZyMvkbg1gGRotBaLGsODE02JdWw81rorOUNoxc/1JKaS2DerP1c2l93/nSWit7hpJQD1n1/4Lt4VIZpXMoTR4agZiXD5hctixfdxhCO02FkM2pblVQ1VlcpYVWf3Rh+XUG3xo62+qtVCnhnJjD5au071VnY12fWXWqG6trBI5/vpVYIJRi+qQ09JHtIVG1FkiqFbIq5GJMtIoT2tEyoS07JtaTs+Rcx+nInnNlXu37cx3Meow+b/19TG/0fYu4EwLWJ7Tfe/3ITrC2qtW9r7/dofH19Nlodrxae5Pq7OCOYmMqXjsl/Y3cGFnvgx64WWX3VvvAGYHFxy72p+s6s686YsBtPa/zszNjWWlp7/x1dk8ds5z/tK2amyidoskcxhWxTDkzaru8Cf7O6+MgfVFMH3Xbto1Iv+aMtH/3eUGcMMcXQeyawwAAIABJREFUm2Y/OKS2v0wTIn2kIqs99vWPOw/o9M+eZT79T7dpzm7Z5V0/uXr+yLWj2O55blzpved+e31zr9/YWXwDfurCa5UyssNvyjjcy+q44PAjsk6qo4WsnmG0eztjlTcuP5MBZn03I98M7tCMdPtI38gte+n/E+nOVxWC/aDXDNfqfe/jrM/rEZoKOJuXptT9ANXifodAnAnk2n+riBsbK4dKVwcT8dQnWdaTcb4+Up9bSjeEwWvq2ktVJdHhQv370u4ZKvwYlGljK2Hj98zYuZOWgaUKuiUpoeQ2nr5WPj/aBIiIVAKdDumJMaIVXmmOtxDUSEGChLqGwUhB4ptENFZNJmc1GG8s02o6oEPoY43O26wXUjMYVnPd6OQts+BwxDU5gfMEDTICyqK135cKPSrtLRYKgxSmcUdOMx/++AEhBPKSSHOxetL/n703a5Ykue78fsc9IjLzrnVr6+oNjR0gQBDgohHFGZOMlEwmMz3qA+hZH0BfRM/6CDKNSSbTmGbGTBqKRoqUSHABQRC9odHd1V1d610zM8Ldjx6Ou4dnVmNkBj6oHzrauu69mbF4+HL8f7b/8RZ6FHCQlqz9lufnlyz8EYtbA4+2N/ziow3f//odnjx9zuXFFUsR5OwA8eDVnhlC5JOP18AF0htBiRfPanXCFCa2HHHnlTv8s9Pvk5zwrR8+5/zFZ/wf//aPefz0BeNaScFzcnjGprvmZjtyfXXBZThnk40MB6uBw6MlJycHTOPE1cWG84trW9sWhcmic9lD2GE4f+T01iH3vvo1/pN/8QNeOXX48w8JG+VqvSWoeTG9OFKYTPnFZ0VPkIMt4Tpw/uwKVDlZHTGEHu87YrBakhburDhVVMD3vZUvmCJdNvJsNTKFyNU0sOh7PFMG4I5OnAHPokAhiLMi9CqpmePzfN9uhGfP1/zZ373NzTqBn7i8Hvn6a6/yzbdu8xvfvMVmrcYUOtlcvlzf8Bd/9g4/+/kviWFk4TXPaI93Vh8l8x3hvCkkl+drbs7XvHH/Abe++oD/9r/7V/zVX77N08+eWH5jioZusjJsxhu1sNrJMsX+t3/1b3n4y3eQ7X/AN089NzeK7yO9F65eXIN4Hj36jG7RI/SAZ7F0jHGyxgQqY2qRF0msRqJLyq1bB/zNP3zK04sNX33zDncPHU8fj4gH9caeSdyCM5IS5l7eW8BFwcryTUB8Njzh6P1A1w0kjWzXGxOf4q1cSNdDNjyN12szBHjBO2E46kAGQghVfMeYIEHX9/iSQ03KMsvkQ+dcLk+RjXLqi/qWS5P43ebTKBlFRmPGmlZiFeu+yRKd+bpSMHIw74hjqoBvq8Fy/YEYFE1CPyxQjWjx0kour6SxGkFdmqV5C/TrXl321gYE76dffB5ATrn91bOSH5JSzJ5ZyWQ2amBehJnUjFlZLAyuTc1ITan2dzXQqCepQ/Fs04Znzy8Yo9L3A52YPD9eOvpFP5PpQe1fLyX/fmp33mykNSUmlvwtq5pphodch7nd2+2WbYLCrCbUfaLAlqLs6NzXu4p6vaOdMkfsUm5k/TB7hFuwP4P12WOlRYnNa6Z5xPyc5je7ZaP013ecFcSqlKqF85bvjFXWrktZMbbz51IyhX/A5Mbc/ILfSg3pti+1kr40/alay7bMRx4FbVeXZFzmKsdBefOyNgvWqMQ0xVAv7V3JGNjaNhuYZ0xgr5X7X0svzLJLpV0jTWpMfv+CPduQ1Hkd5uu0GcVSjmdPUWzn5k6FA9WdqDrafrKXyfOqGAQK1pwxoDbjW3F7+bb5Y47E2kdiXx5flOPX9yzuCL9G1XNFEXGtJlCPGcC3lhfJHptyP8gzyCBBKRoOdaG8lBOYlcgyiduJmfKFrRC1awrgkLKOyhvV/31N0i3ifVYOa65ETnT/vPxN2QuFZX5iVTrbOljl+SKzJb1sLt6ZAJP9++eXnVXv2XPXvvg0TVXh8znvT5oxajc1l0FarQHmiu25bpVGDCOOMEVCiJbkn0xxdM7hfccYtoxxynULHQeHCwNRuXkW1uWyZTQrSrn94grgKUXVPYo3RU3NA+pcZ/ToUfGZXr56LpoNznqiuISzldM1jHvaJKTvjKPdJ0w5jCnnI8RszS4bWjlXURb9YKFq3qHRgGlKBuJ8lxPRp4jzwhRGQGuIrajYJpryZ/TECCl1bJ9ZsXXJIazORzTYM5Oz0gIHk+d7Xz2j1yVjmPjTnz7k//y/P+PO8Q948WJLnODbb51x784BV9fPiWNiMwamFFEX6ZwnamAaN2xDYB1Oubi6Yr1+xrPHl/z1zx7ydJM4vtdxeXXJz95/zGYbSAj3D3ru3T/l5qbHn18TpjWRSJcCpMQrpwe8+uCMN9+8z2a75f1fPGJzfU2MOQQJYYuyTDCM5nUSUd7+2c9JseO3vvEW9/yKFx9foH1Ht4p0qqTkieJxXYfPnrKIzYfuYMVi4egWA50TlosD1pNt8Ba+FnAy4ILHTzGHx3vr/97h/YBq4ur5Oc8vNngfeOPBLabNlRlIpCNEzWRaAqKEOEJwiO/QqJlYwryPKSVWhz3rzZbPnrzgT//yH/nk0ZrRbdlO8LvfGUlJ+N637xHSJf0UIa1Iruf8xWf8wz/8HDdZLiVixelFPUkSMQVKOBUpKzMOFgtBfM966xk66EU5cj1CZEqJiBEsJVVcMsN+JBEHiwy4Wl/wyScP+dnfv428cZdxq5yeHnOw7Pnlx5/SLwZkAWe3D9hcw/OnG3BwtDwDIKY1IgFNll+cUiSSQ5uS5TS+eL5hPcHZ6QnjdWIbwWs0sjMw9kwVVIKFvmfFq5GEVb7aEpbMTGxd0RrGnHNZiUyM0xpPj7QRINmTF1PMxGoJ6QRPx2JY4rueEExerNc3uL7DiDRilseevvN1A7JYEQsXNeItV2X8vvehpEbMh+UMpxwCvx+xY9g6ERNsRiP9UolstqP1b37CarUElM+eXnCzmfjmN14BAkvviSFBNIMLTlDdmtEhs1ybhybviWIh5xU4atmns9xsFITP+1zz2GguWdBqlSnFyuKtWgBmq1CV3/Kem8j3zVFKWTGPtQ6nyexx3DAGYYqOj59d8e4vHnF6Z8np0RH95EnjxCt3B45OjhiGHt/5Gh3jnOA6j9NECmaamaZpp5ZcjJEp7w/OCmrOSkYBJQX0ZxlRQH4ByEJslJtdfFGUV4pStr8/SQHkuXv2sESD9Ruckvfwxnhv55ReLgZ0mW2gFMWqjfh5+fg8HFQVHp3/FrUc86qsFSUy5Hx6hVKSw9ZLiaLKjM3OV0KlGfdQ2YBdNX7n6A8F1Zjnx9yfbchlUea87+Z2l37JnaDVEdIoqrnfhdLHn/P6TX+V6Kw6SnUeN86DPF80mXdcmHP9Wu/xjjFizzCzOy75mVqmaJlf87g4kboGk1nF5ufVR+Q5W9tp7S6GA1fOYX++sLcumo9aufHl8YU8fv2cRUqoUxFmu7HSO6F9qtU7NqtxurOAbPK/rHDteATzRG8nXwlI2lG2sueoeDPnNuVrqtWl3Mk1MTd5UZkpjFRCgOYGmcKwtyhnITFbfOZk36KQNFJXZqGiRcBna9pcY2fuo51uadvT9ERttswCbb5E6Psecr8456qXsH2X3VBII0XIj7Txqbu2gOvtHV3Ed0WQWXiqFZZOpgymAURzHbZMRV76mmxR1ZI74WZBU3hTxKyLDiFFjPHWOVOmizXL5bARCeDnTIryZoYDZ4BVhObLdNn2juraHtb5nRvIovNANucJ6+04lyexO5LU4VQYp8g0RXRUDo9WNbzDgFGk5RvUlCiZRsX7CGqATsh1vOZqn04SGpXNCI8vL7m8WvPw4XOePXvGLz7+jM0mklT4+3cfcvSk5/rmGo3O8gx1ousXLIclnSghWY7X8cHIFBPuwDN2gacXz/jsxZbrqWe93SApsuwczvdsNyOfPbtg3Gy5udnmnNZUx/pyvYUnF6wny6t6fn6dDTmOlAzWerHSMSNWwgIRXry45u133uevfvIeXr/Ka0cLrm5GdLRRCDmXw2miS9aX0dm6vnmxxdHz4sLISY5XCs7hRfDV+5yBfu9Zh8D7Hzzm4nqDypDzbyNTmADHmw88QmBwSyvO7QSnSpc6nNcMbw1EONfZJM4lErTrMEbJxOpwyetv3eW/+C//Gc+fBy6uL3l+fsE3Xr3PV9+6VZUG1y2Q0Ywjq1u3Obp7l+AdEEvKLKqxykQVA1AhJfAQw8T19ZaLywuWXeSD9x5ys74hqoV5Ri/GwFsMTiKWd+NAouV0HRwccHp2zOmdU1IK+IWQfGA9RoIGnPYc9ANOOk6OYHA9i4URB6WKTLpZ9mVgbyljNnZBjTU0pWDytRaUDSb/cOYZKBZ/k0q7K1ylKmZgOKeAmVIDGIqRTrCQZU/lIqx4qNC8d6hLmdjDIdIxTpE0BaLR+XKzHhlSMlmtmZzLSc7lzhEsWUg5qKEKVUdq5Ed5p/1jptef5XA5VyzWGqeKuEhKnpAgqUcmMqC2KIcpjFxdb7i4Gbm4HhEdkdWAAIPv6VyJdlnkPUnqXtcaNFMF2WQNZJaTrbydFaR8XQaEzrVUNMxAkRm02l+u2ZaLlywD/5Qjk3SOOpKY6n3MWGDvtlouWKgRQPXDMWeHjtXhit574iYSQ2C5cJjzNVXCNkFRJ0j1BNp7+8wMXnILy3ua/M+eoxzKa/u7NN1Q5mlRMKSuiTqN527JP3PKRfPFvP9oVRYtuXC+Zi9gcb637oxSPn8HLWTdZxdllMtqiOSOwjI/IJlQ2nlW3dPnR+RIhjxr2ja52eM+KyGKaFNDdAKItNW6siN3DpHPRuDyfdeZsdmnmaCo5LuGjBmLQk2a51JtXtbai1F6lxW1vFT+V5u1sI87Sp8V1aqOecEku57gvVHcUcT2vmlwd/uc3Ed5jIpn8d93bvncN46QWbEv7ynzHJN5ndvUeBkj773EfF2d6C/Lvi+PL87x64ehzklzWd4VhYgsGDOE1bqSipykTOpi4KrX5UnpxFUFUMFIEPKEjmrKXl0AUn6muuFXwZtrmYtvvI5Qrc6zdTDWG81WvKykGTUjsRStUoxBUdrNk/reM5BJe0peFg7islDLYZvF7YlWhljBwsjGmHPfXAl7sL5sQ2jLprGTd6ggOSemFTa+gCQnRFVimrKFNiHNf1YwuSQ0l02sWfhViFrdKecTznd4yZ4DjQaYdGLInxX2NZ+czR2nqDMeStJsZRfR7C2TTJEvRqWtwmY74fB5fkDKZDu2F5fs+EiSAMlqp2kGWg4guuzByyEwDPld0txxaDZPzkRHUvJUxfKWBDKBhRFbiIB3ib6DoB3/5t+9w9V25ODogFfuHfODb9wGHfHSgyi+M0CMgCfPJTHgbHT6KdeqE5KGmkgurgh21+ioGVZpJjiQjovLyN+88wlPzi95+OgKBF5cjFzdbAgxsA4bxg9GNAghKBfXa7ZhYuEHetexiRMblEkTqxCZVDm9c8xms+Xp+pKrzTVvnr3O5ZVy92yZ87Y8YRx5/mTDdrM163DJcc0h4VfXGy6vNnz88HkOMTOjgvWrQ8VIeTQbHIyd1rHZBJ5/+DF/8dd/z8Fi4Ft/dI/LzROmYJbglMdQsbIcCawoswPdgiyULibOz9c81Rvu3zvi6OCQxXIATUzbiZQCo4/QCbfPeg4OHSpm2Eh4OnfI0C04XSY0mkfd5FfO78qWDcHlNsUMGKoGgmRv+2Zac3i84vBkxVfOI+vzc37nR1/ngw9/yflGuQyKW3nii4BfHpDcDVECJ4dv8OZb30L9X0BaowghColAtnAgmtkb3RZxsNkmzq8nRq+88toKQiSkxCQWRh2qbM1yxQHOCs736pmi8vW3HvDD3/4Wv/f73+H6/V+wOFiwWh5BUE5vv872ZuT9j55BUr72+m3uHC0ZE4zBlDSf12LqJmxPyKFlOSfUd6AdTJvIdhtwtwbcJlgeXZZBxTOAzIpGkZYtqBPmeq8lB8wMN60F3hacquCkb1Sccg+br17I/VmUx2TlOlRxvsM54eBwaQpVCqC5jcnyBnfqA2dvCDoTssSUjQhNmLpk8owyJpb7CjmpudUybb9hjnBYDH0FmcvBauQ6MQ9M7zxJFxwMA9sQWAwdiKdzME2BKU41F7/0bXY0zvtbI/pLjcQ8KBnjRlALa67lk5JSiDk02bukTApVtkYLSZzVnxp1g0AyQ0gBlWUMzR+ez2n2pmo8zNcrttd7B70IR6sVr945wLnOWI6zlylEiCkQg5XJSNlQEoEpK2OlpEbNtKEoFzMmKJhHs3yX8la5mXWsVeu11UhR8VG+v8wYaIop7/O5Puuep7m0B2ZFtCoze4ZjbT6fNcB5QIpaV8+rymD5vmAZuyBlnDLvoTQKZG5ZBmCpLcORX74oYpI72UlRUHQmRVHDmSV3M2W8uF+SYkY7xVBDXYNzv/q6lFxnuKHT2Ttm3Zfrz2qynSXldJgE2YlsYakVuxY9r/Gm1/5uFb7d9qYS0VSut17K41fWRMGmeyHNVU5QWYh38OYMuy2/k/LMgr/z+DU8HfsKnoXPajOXs9Em37mMmcmCPRIfyrjmK3fmUO4zhR3q5i+PL+zx69dZ1DaAxn73UIWGZEnaTsqqKGYhZ8pd3uxztIFh4d1QS5tYjbeondD13lq1JdtsZg+VL8K+WFKcmJWqrX1YnpXzNCT/Y94NGibMctKsVWpR6qoFMVuzM4tatQDm1VGtyVUptvukElZGBszBwLLLlixxc5hCXWA7GmFZlrqjHBfhh5qnMAQlhAhO6QdvrI6YQLH89uxxceZlUDLg0uaR5M0/g8uySVJy7zKwK97gYvmbm1UsnyZoXNnk0hy/77Tk/OQyERroXfZ2il3vywadSqhsTnzPD6o4zJGNjKaoFtEsyjwmMiuFpUWtIcGAaqoA1OpvGnDyDiPTEOHF5YYX1xtOZODkFLzTTABkobj90OHFk0ah5C0lZwAqNlT5kiW0k7nUTP4ir4cya3POQHJIZwQOXaf0A5ycHpDiwNFqicZITHDnbMX1peARNmMg4umDWWtTHNnqhkkT26g8/eSKUWEbjWETNSX/tVfv8OzFhuubDdv1ZHl1ybq21KKznFupu6Blc+VaaSVno7yDy1tZXouFsEUB8SBJiNPIdnOJxntVlxcRfN55nDMWT5J5sbv8vPV2C8k8uk+uJ7plz6g9wUHvEj2g4ohTZFh0fOuN2/RDV3NRVCBMnhQcV9fXbEdTLhWQZGahkGIOay/gIBGb3VHAPL8EC4tysN2M/PWf/4w/++mnHHXf45OHn/Fku8KtjtEcwkxKJBGiwPZqzfZqW/vMWILrEoJkfS9i883WorDdRibpOL39Cme3jugePWPMHjpJiuZ1U2QnItUjmIDFasHxyYqzW0cMd47o+oGVXxpj7WLJiyfnXF1fMwyOFBx0g4Fwl3DJ5pmKs+dIAXpZGXOYl9FlIKIevMORcK4zOZ7BSlHZtPaojbvMblGbStka7sAYZbOxwrm9vaPeKe8hWfZm+4WB8yq7IWqwHMfO03dD9mAMVvIlWKioGdqgzUcsnhGXQVUiVZAoyFwegrJeMkjcU4JeBlMZ/FWyHNvOTamxvGMTacqi93jfcet4BSRubjYgjs0ULI0ghlqnsKQelFIctUSJFsV0Vsgp/CKuCPgWtBeFrcwt+6iyM1JE2fy+u5FFSoWnVVfeM0DvakHWpCrT7ftxbLy8weSRc2YoLYyyQsl7n5Xy0s4UU30Hl/fIVhkosrg8o7S6oveCOyATyeyOX/29CI/2yGJ/pnEp/86oouyvOx699tj7bMYHWhXY2vdF9yllDLRVICoCy7ip9LfO71jwWDlPS7SUXV+UX3uG9Y+mkhJSMMKMlShttdjGTGqX0U8xIO11WMrM7WX+SX4HzeBO8rwWVxhYhT6z1Tts/SBCcg7yvpdEbewSZszOkEtVawSUKahNJNnOINLM1XaeZ6VO5rEra72upKJMFlD3eYb7X3HUb6W9TGsbdm/VYPWyRsvPtvRd7vvqZaxgswBwLVOn6YGMajMmELV91RKYmefe/vz/8vjCHL+2shhjrMCiTNXYLJK6/dVkZdtMKnbf8fapbbz5y6RzKJFNsBYm5MnFXmimNpNO7R4FNJRcACEz2yUlJHM7igj7AtrWbompV2bryPxuNRyzXYm1BIV9qQrijaRFcttK/8QUsmIoswWvXAeEYJuWYmxttgabLaN53855ppA9Gw5ThksSplA9OypWNHx9o2y3E87D4aG3Gm05THQOg0xos6+VUK2dMMm82E2QlPyjEn6bwDtEO7POe7O8W58rxAST0rlMDoKaVsAAuU7gNgYkllDZSOc6650IikdxvLi5ofcdy84jMRC3E9L1dBJJURmkgEfHTZgIusF3HYt+AXFj8yp68KDe3tAF8IT67iUcWKdoiosIUSdUtkAHfuBqA08+Cdw6cfw3//UPTAhOS6Yp8uLmCk0DE+CDhVduAUfM9TcTmoyIoxNF6Ji2kVEn+pXVkzQAp5lWXPL89nUcRJQucxoeHTn+89//Jr33fPr0inc+fs7m+gVvvnGb05Njbp8eQbBafCkqkgIiys3kGAO8djYgzhThJ896FkfCpht4+2cf8j/8j3/MhUaeXg9sQyQmzxQD20m5uryh7tF5jRr5Q0AieT5br7rKh1Ss17bGSuSAKWqmTIdgubBd19F1juePn6GTzchYCKhQYsQArySSKH7o+NuffsDbbz/l+iaRPFxPI/ITuD6/5t7ZCV996y5/+HvfpV8siWnDzTpxfXUJKc/TrDBmH3IG1GLrWhvLt5gXo1hpnXdVIahmBzVj0KIfOOyXnD8/54//4Re8/+icf/3jHh9G/vAPf5PvfftNwsUa1Z7LzQ1dP9B74a9+/Ff8y3/5Y0iXuJxX6V3KIbcmIZMD8YoGZwXgnfLu+x/xv/zPPyZedbz98/e4vtzWemrVIQBotNy3EvIdhgheefftj1h2PV+79yp/8BuvEeKGy4tLttcTzy/WeKd87SuvsFgtkc6hGlkExXWB5CDSIZQcmCwdxZl3zgspOS4uJjZjZLn0TFPAebvGdxbyF2OooZCVX7lQt+c454JZQwg7BdNrGNVL7Ngz8J7hjBI11k6paQQ03rKY2MR1meGN7KbewzvLN0/Jyn9A8bLNoHoYBju3s1JBxdiGCGGa8jpxuwBKa+DX/JEmYlKmHSbvJt9PlRvZZi+j1j1FxBES+K5nsVziRIgx5dDbnPtHIZrJwDJ7W8oi91YciVTJh/rck3l/9S4b8ua9RV4ChAVZzvu7ZqNh52eIUr0qAr10u2C6Gcf921d2bwpRTUPiVs815aG2USSXRAELTYU4WShrDXUtZHsNiE/R3tvl0hv7fAYvK8Qz/NjxGrXv5hziOrt/KmvW5JDLNhJTWmIe92I80eb+M3pPbV5sUXqaAvfQYqa2rTllos5hdv5uJ2ZRnir5Sf6weBNNWSgRR5Y+URWnckF+hVnB3Mdpe97L0l/M67B8V2Ce5bSGHQNFba91NSX8EsxjbNi1Q+gRB92SavRIKWWPeZE1yXKyZ+6eHRlkSt+saFNLZklRi3eUynmQ2j6eDSapKprz+xaEXCOc86UzJ4jUdV1urc0z9udhmR+zP3Gvz2cHNBng7ba9jIs282tvjdZnfqksfmGPf0KdRRPabm9xV78/edNswobQWcGz/JPWqiA7k71aUWxaU1dQsaS8ZCmxH3PesFp4RJEA9qBKYz63WavA37EF5T8Kf1Sd/vPj21/qDQvttn2thCnM7yW2wdg9XAbHRZAUUiBy+JVDc22oskEyd+2OVyZqmkFqMvHuqjCyXD4TVB4RZbESlgeL5t7WmaldxDK32RTOOexV8jgkjLE0ToEuh2WR7D7iPRORPoeABk10Xghb26RdZwYClQ5HshwqLQNhPiiHN0UTs8prgug0F3C3cKYnj69ZLQfunEHnHZMqxA2RHiGRJFroqusZ09pIJrRHNec/ujKGKedOQqeATga3xOMk4ZMxTcao+M6z6DwSV0zBcuauNyMPn90wBuXeuSOMwNbqlY0eYxt01v4yp1QglIggFiCeKNd4UdzgIDmEkpdpeWpJs8daLHxYRZCUcGKegW6xolPP9ZVnCsonzwPn28St1RKhZ4qe0C9ICKdHC7bryLQ2l+DxSaR3g+XexQkNgaGLnJ0s+fv3P+Gd9z/g06cvCCHx53/+90hKFnKqubxDyvU5c3hdIZGxTcLCwUTN8q+ZWq564/P81OiQPiAuk5skRcTyxt544y4/+t1vs/CPSToZNb5YmKBqYft1gIU6axK++8YxK+94971LLtcTzgnDsudk4Xn19jGvnh3Td0Aa8VWYZSDoXA7lmo00rYWqyC0RMWKRLFs+l+BBtY6X88ImBa6niWkrLNUjbuRis+bZi4mbteBue5Lf0scFfgC8I3Q9ycEityYWlsN8/ygAEZcS3iuBgHiI4Yaf/sO7eOBmM2XQlGpouGnGrTzTTLLs8E5Y32x4/72H/Ot/83/RX3+Le/dvsVp2XGxu+PjxCx7cv8290xMClne4lYTL9RMVU0AcFk5YwnRJc5jcNCWePj1Hfc/qYMH1+VPEGVtnGLe131OyNVrYEQsLZqzytcj5l0F3XnE7f9ec8kZB2LugdomI4DQTjqE1L17r3JjBdfG8GdA30hPvfVUcVU2exhTRZCH8psgVBmjXGDoT7JCMzeNdAKMpOWWXmgFzBZDZA6CSlTbnsjxxxDHvMSESdPYqStOfHq1zXdXhdd6rPKCaCCqI8xl0C7lSKgkzcEnbR1Kb16iHef4hswJDIqXPLzMV90oyzO9r95m9j8UbOj/BVUBLZZbdJwwpLVMBcVZ+yHdmdI2FFfelMgZQCo/P/AzsjMmut7g1HpQbtPPQ2pBUaXNWiwIlVW5SjVK2R+f2N+R8JZoLFKeuAf4m9BDzAAAgAElEQVQllHB+ZgndbV+rYJ92vCpGKt0vNO9Tz5rfufW4idR1PKc07T2xvGz+WZwTSJ6njbwqz6l5pMxffa63KoNM2XkZqaydFoasSMaeQqynuS7L/TJGghnoAdRqsCZXQs5TNnaZIh9zqY8ynlrmfH6KNu3bcca0E3ie7o3Cp1Wp3jttR4Eu1+0MZN0HoKR4teNo9y1yBF4OG927pn6yO+935Gt1HDRrhC+PL/Lx67OhZiYqgVzOoRFeRfNSsnKUF1wrLIrSloVkyu46W8NZONRTZ6uGlxxqs6va2b/NgjJLcrHWlgmb6sLaUdzs2533K0QvLp9TN1CoVrhWwbR7utnyqKYMed/NIAaIIdci7HKAqmZroHMoBpAjZUOvN8/CdX7Xuc3Zwli8TIbR5rpazXs5Z7l/kGtFOaXrnFGW55AvG5OGTKGEmhTFvpirBKRTppAsrCrGull5IAXofZ8LLwdIQojK8YnQ9T3TaLlom/HKykxI5tfHE2PEdZ5EzsGRiHMJVQ/qYDT+RueVN9+6TYqg00hIYw5FFNI04Yeejz7b8OTpDc+fJf75H97hwB2hG0HDlk4cTJ7UBTRBp53F6GvAuR7BM6FMEnBhgcqGftlzcRn5xdPAatlx60Q49MKDkyVf/YMTUnDcrG8gOfPyIHTJkWSCSUw5dcCUiArdACGOhGkyVsZuIkgCOpz0TGNAM/tpO7fBGFp91+GdY6vKdDNx+fCGF9db/qc/+Qnvf/yM62ulXwycdspi2XHn1gH/1X/8W5zdOeXiycSnn75gdI7j0xXhasOnjzf8wY++x2atnF9G3nv0AYdnJ7z77oe8/8tPSb7DLQaOUwDXcbjqmUJk3ES8c8QY2G43KFoZC33f5NloyqHGLZiySStOUekzm23uJ5SDVU8XhJPjM+6cPSC9eEKM5jEvrGzOW4iwaASHeaGd8A/vJx7cPuK3fyvy0aMrPrsc+MbX7nP38JB7Zyv6wdZVGKMZKVTBW0kBA9g2J0toYlnrJXSwhE853+X1nL2NMSI+5+ZlI0ixWoOQQk/HIbePV3z07BnLi8jx4oA7R7D0W9JoeUq4yMILvfNcbzrWU2ZXTWaJViQTRQiHomhyxOTRztNphDHiPHzwiw94+uQzzi9H8N5ymrOiUSI+CuOxZHCa0oBjQgg8v7zgz3/6Lu+98x53z055/bX7hBh5+OFjvvPtN/hPf/+brMcbTs4OOb11wjQFdPKmvGsCCYRossH5mawE5wgx8fTpc5ITprDG8gk9QkS8GYZCCPjO00nxGKa5XiHUnN/qESoAqACrCoZ2gelLf7d7iewpI/l7Ia/jChdL2FsGSSKM07bOl6gwjluGYUHnO6YwMY4T/dAjzjOOYwWVLo9B3w/0vsMPnqTKdtzupEKUbVZhhyHR8v+yoKjt0/pmmiJpKgXSXY26L6/Ydb7JNZ6BfZn3RUktDJM488h1boFqR0qjAeHgEToGr5Z3DQ3TaQH1BaTOinZpiPceh6WKtArDPG4NYG2UzzJKM3jVuk/Xz3yZE7MHTWuprna8TVUoRiAyb3BbR9iMY20IZ44uiqV9OTKonWNtexuNeUehVEhavJPQFTKYbGTXbKArOX7ihSkVI77bHa/iScwKiLSenzIWmT28ehhRi9rADNdCzm1TfcnbXb31n7fWMq3prvKQlfjSQ0lfWocKdR7We5FDbRvHqEHO1nu7P4bkmq4ZhmoOiy8tKHtpZtCdQ9KxkNPa3jm3kG3zzrVt81wDK7mDSOXLcLl0Ta+7ilVKWsN4UzIW9KkolLO2V/FkmdsvGbYytm5U2NrfxVBQwuypSnYz59gtNTbnSr+Msy3cuTXI5Lk/hxNm5bI2jbZ8y46ntfmM/cd9eXyhjl/fs5hsszEdr/GOmS6SrWFKKLmIdUPIfxfFURuxUZTNHfsItLMuaqoTvizMmdxUy1ZOmxTcSK92nczfN8fnhoG0f++8Q9uO5l6qddKXTb1cVQk9svdllrHVZp2Fws6TKGtvtz3Zjii6t9Bni4/kRhhBRAS1sKCaAZTj7c3ylax92l7b9F2BSrlpLubNMRfdFp/DYij5iAkVq9vVqQcnPHwyMU0ji67jYNmRgKAWKpRhNCXfQzXkPdnYRJFohBkilPyYEOKcZ6re8hxJrJYe9Y7jowHn4NZptDYEct+rhXKSUKd1k7d564r7ywwYviM60OTxKgxeOFjBMEScdCY4o7JZj+Z9TAa4oxTIFkliBdpd6uo8wlmfLboFRysL11UcCVMkp6gWwpUnis+hTSUUqTDL2nAJvrd6e4dLz7e/csbZyZLLi8D55Q2v373N6fEBt08PGE6XaKd8+NEjYlRu3Tpm0MhnTy7QSfl/fvq3xMkRJvjls8f4x5/y+NmWKcG9kxVBPR7z4m7Hic6DLCR7PUEklxJQM2KE4j5FqdgvywspuAtyiJx5oEqZMsWAaYjCo8+e8967D/nBVw64urrOtShzSLwWL1HeZEWRTul7YUw9d26/wsH1wPmHn/H4kyv6s4HDYUE3KcsumVck7OVRymz1tv16D6UoL218M2LN16XyDxnkWaja1c0VId5w/17P8bMFT643PDg85ehgYPBKIDF0HVGTeTSc8MmnT1iPY32uWZ1dJjYyo0/SRMTmscshugrgFdfbd70wxySVRpd3wTzCKqASrO3JPvMK3/rG6xwtHWfHAjKgesKwUJ7fbEkhcKodXjuSjpgBIG8KhfSkgIySkqAJ7x2np4dMZY9wc41ca5OFhZXPWqNjyatTKcQN2YhQc4BMWBkl/L8/JLANSWu3hc/NP2r2piJj209b+e29R7OXOkULUyvP897TD70VhM9ePRRiDGY08wbWnbfQ52IArV4EodnfmvbQvkOTBy0OPFWGFDbG1CgUL2HEpm/a/hCRCqg1kxWlFLJRxIwERnZmXq+UWUDbtSJl+pVf6ovUDa8BuVSFuFUcd/0p7X1mMLu7i8/3Krm9+/t4BcL11oaGDagrxWinCsk1z1S7X2sT38f19flNf++0qRpm5zcs5Vy0zvdibC5yJtU6wHs33LlPAfFt6Y/SUNV5nFtM9qswUavIFeP6TiQ01LJBZb6+tEq0Vbraz0rfNd/l+VJIwe2E3OX7I9zAyAoBC77U0rdS0yYqZ0LtqXlOajmXHI6dz5nrJ7dpRHZulGme53mdlVqL7QBluLwzbr7WJLcP5z6d+7HtP5iXg8s1X+s63jN21X1pZwxn+V8aMRtIPk/28fJnu0uy3mM3L/JlGVK/+RVr5Mvji3P8+myozLmFZbOsU63ITSwHyea+VtlbBEYJgShzUhprhZRVBDubS93+qzApN5w3Cq3CoJyntGeU+np27i5g2Bdc9XWKsJPGH1I3grIAS7jQbOFpmaZaZTJl6eUzy0MqbFuAy983QZ/5Bpmifed9HSKNUpjftFjZ7A6zQgGKuKlaoVIil9DQ5v/GAzwPZr7W1edoUFQMEKhiBCvMoDPphHRW04wkSOd5fP6C9Tbw6tkZtxdLeqckGUh+KlIfDWUTDyQVki5RPIm15bvoAhHFMRJDMGr4Lm9Y2iFu4pNnG5bLFSeHK85OFkaQMo7WJucsPC9GtBM2USAqvY54Y6Ex0EnKzGwLLGuzI02B3gu3b/U4l+gwK29SSNEZwE5Y6FKynNPkM1GGE8tlVAXvGHphvR55/Oyaj55dMiYlboSz4xUP7h7w+itHBIzIxOWwSFHFFUtpJeoxy6VKxA3CgfP8wQ+/xs164i//5n3evr7kwe2B02PHyaGy2W6J05pBEv1yhSjcXG1ZHqx4/Y0lP3nvl6SgOBw//PabTJst77z3M8Zxw6JTSBHB41Pe2JKiDoZcBzNGCz0sYeDirCZeSvZ5FMkK5B4IiYL4CMUAkZeO5W/Cw08e8dN/fJ/f+c5bqM/rQy00uDAaeddZPqx39IsVV5srPv3kBa+/+SMOTzt+9vMf8/ZPP+H7X3+Nh5/eYbHq+N3fvMuiEyOHsRZTy5TU8CNXIx4MbUhFuloZ/hoQLZINDnk9NnKkG3qGhXDpE6PaePfLnseXE9faweGKkC5wsiCEG7quY1geMCarpSnJqHtik0mMKtusJCnGShnVEXF4cSwGz8nhgErOoS4WbmnbVl4hh0C6qXKWqEbSFFmsHCcnS+7cvcewWPH6NyMSEid3TtGwxSlM11ukVyT6vKQNkTnnM/tIyX1KaEx4Dw/unXI9Tmw3gaOuZxOSmbNK/zXlFsx4ohTFsbJe5BN2PRV5M8hejpeBu74EcigybE9BKmCx7h32tHyeeTTKtZL7Wa1B+X21nmu5mon1uM51/Tx+WORauHl/TTlXs2j8Tuh9Vw1EpZ7gDPxSA4SVEupVuqFOW3FILglSBrjsweq0yS+cN9lUicdqB1m/KBCVSK79qV2dR6nkRtLsL9oAe53Bt+yMgZLirneiYo1q+NzTSspYsg+Ed8fzVxkIPu+zskfXvc9ObDQJqXtqMU5A0+eUlb/flrYb9aX27ioA1v9dZ2kempQQk3ma1fJNO+/BOQZvbbDw5mKMmRdGVQYKTtDaiNp/zehUz3JtT+N13H+n5o3qd2We2jTUmeeBImO1WU27fVKUz/0+0522lh8FFZYHtGPZkN2Uc2uXVE0Tc6wWY0bpCcmM2Plmsz2lYtDSNvs/P1sk54/uclG8tBiRTF6XvY/OPNVzHFqeH5LmYPeGTXa/P5Q57NWauzu3Z6xu/8xj1I5A2cl07p5sAHl5XemOQl/eqfXP/Kq1WI76Xp83lb48vlDHP0FZbCVisziZlUFRas0bW5uNmz//boJkX1DPq7DGqefJtFNDp2nN/lGERzay1HsrFrPfKpGqJa3x88DE59tTynWtmEup0KVXW1FVAMvCtBCO8kpK0kIVnSj12VINAyq3mUk86t7USt5ymloo7P47FCuRlZewcD+RlC1+UHJkds4tG4q4nXvV/idfKwllQkUYg+Uida7DO8FJzPUcO8tbEcfRAayWPUeHvZ2DlYgIBSGpkLxY2CmCRkFLsrhkxViM6EYksPQg0oHPtvEEfSd8/NkVZ6eO1dLjBeJYmO5AvGSyDMstiFuFkFh46L2Q3MyIaC4LoZDkFSDaed/kTtlm5LvOBK9uQTpj0kVQIqI9is+hWHZzJzBNkafPb3j342esk6I3Ha/eDqyGjtdf0QqKjM49ZuDgrW5anIA2T9as+EmUZT8wbidurq4JMTFtr7nQS8LYc7hcom7i7ukR0+R4fLVmGwO3jk85OD3g7OSUaWud/rXX7zNuEwv/j2zGkdEpWyyvM6lgteqilQroelBlipCiAb6UUg65FlojRpm08+rJazyRwcysjNW1BESNKJnYRJt7VdCf+1wFpLNi9etNVrg6xrBlvFauN9dc3xyS3IqhsxIaVnZUiXWPn58tuZxKSydfNtp2c9xdePbD5TVZyi4Mi56jgwVPX2z59OmWGIXDZc/TpyPaefrlAr3BIgB0nvsnp0cWxljfMTNqdh7zgudwRBRR8+QEhc45bh0ecPfkAAu1nWY5W0z2uetKqLs5T/KYZObhMQYefvqEaTrDL5VlhCMHK3Esh47oAtN2Yq2J1dCbXIspK2s0uovm9mcFKpkH2E0wbie6kwPSmJWmAjh1Hl9HBthqxqsYY5bzmYCmITdTFEkWZrcvr+d77h5lPEuYZOkiu1+TP1/m3c5kVpLK7EVQy1cqaQ9zEXFb0+M40mufZYqBW8TlHHAlJlOMwjhZfVmxvMZi7NPS4HbCte81LzWUmQi6Ak/R9kraPbCmW5R1Kzavquc/yx1js817VOyxeo85jBvL9S5B06Xf2/4rM3DX61tSIdzOq/1/HntKfr1Udz1npR3FQ192uRkrNL+0fdkYtOcImLL0ykSZb7Dv+dn9rP2pdT62ZTGMcdyj4vA4YrC84JvNiKqyWAiI7QfelX4Wy5HfeecS7dTILNltgxlemra3r932zUtd/nKf2mdNqPDeRS/dS4vr4OV77o4HWR/f9QTO5yUKE3FtR+3fRvlt9orappbhv4jEWbtqcNXOasklPbS+ps3ZGfe1N53njo1Ewvq89Si/5MXPRmKaLpXcJ7MiOK8h6hPa5tbGvdRnsvNyUn9Y28t7lP5tjBA6P+XznCzt8ZIBJB+J/Qt+Fdr+8vj/+/gnlM6YN6SZMbR8VZKty5zSvIjJyozWMgYmn2RnsVTgl4tya5wFtMva36/KPTGdw5SfEANF/hXhYuGWLie5zxIopSastbl1cekXq4xoedc25yKDmiqY045yuGO1TCnTNZunIilMhZkNpfOO3rvM9Fe20jl1HZGsMNU3B9gBIbPAlvxuue+8I0bFkWsfCuCUcRpxYn3mnWffCjiPt9U1KgvfOYfrPSksEHG4zgSS9xba1PlFZlSPbIKx+71xdgsRx6fPbnj/oydcXlqJB4/He8F3wvLgkDglNEwsl57To0AKG04OjwlENumabYhsxi1MCd8tWE/mrbp7NPDqvRUhbhnDDTejEDsjihF6SIk0Zmu99GiKLNRyo0ZxXG1gO46oOJwkDofI0RJkYf2pmZ1VUkLVWchfnvtuMvbczq+Iqrn8kKLBE4IHn5AhhwZGuBkDB8dLfvibb/C7P3jdSmwwkJInpsh2e03SSNcNpAQvXlyACKfHxyyXiwyWbbxThN4NrNwV/mTJw8fPePbsnNdeOeTBg/u8eb9nu05453lwb8nltfLx00sub0Yu1zeowPPzNR98Knz08ClLJyy88N+//QuOhxXLlSNwxPlmZIHlYYYMdlVhGDpCiMQYzXsYjZ3U+97y19TqQBZDiC0jN1txcyyxppmqw2U2Ut8JLgjDcsXxrRNEOzR5m8vSAB+UyASiBJTr8Zr/7J9/i3Cl3P3aXTYy8R/+1nc4vX+f+wcTP/zuV7h99x4qyrMnj41sSFNjKZAcGqhZqQmzrKmbZgZaNVqhflhlgGLyLiWr4RZjZIqJq5stn3z0COk6jvstX331Ht9/44AHR8rFRaQTCF3P5eUN6+sN948dh4ueZxpmGUZCrQo7Q2cgPqpD1dNh9ehOTw753d96i+++dZv/9U8/qNT/KrkmY4gkTE4mUawGCkhaICnY3yqk4BhHx8/f/oR33n1Iv/CcPx/5+r1b/OhHb9ItBl599QF3zga6oEjaICr4XIDCwoUT6iI4T0rewPeUWHZwGSOX51teu3PCFK5sXWXgZ9LPQFjI5WtMEWwiMkr5JMjsimTFJpnCiO5xpe2CzTnyI8+plK3zkbxXNERARSJnTVJkzuUx5Wo2LDjvcc7V0hoN8mJ1sKKEPW63mwpux7InWLFHut6jCcbtaAYkhc73OdKjgEN7flXJi4aYN2Il17VFKfHgVcxn5TaFXNYjJUKwGq9dVwh48rNy7oIpiT1JlbCdcC6xHJZ5fwtoJr2x/tdcs3IOI90PhSypKwpzLnBeV0Vx2g/DK0cLlF/KhWoUmIIz2nMFqYRFLWVNKcFRn6H7c2ZmVJ3r/+Xct1YbEsmpH+w8uxzFgOacjYFrQo3xEFJivb6x6KOYcN7Te2PIjlNgaxTSrLGw767r6Jy38HWKIjNHcrjSH2Ch2c375Z7cU/qwMch5ky6vPfb6st7kJSxXwkqFiu2acSpTspbWcs38zUfhKKwKX1Utq2UjL8uiREoz3t7esaXgbMq8lLZpHu+iR5o4md+/pjs1WK84HVQTIQV7RMzLtrEdQOMoaYDVpIrESLWVt00s3SOSqwVoGR4EyeWg3a7yVtrDzMydmrXedtnO3G7HLxsRi7Fu9j6WU2bcvL8m2nmz78WniOdiAKw90o4jXx5f0OOfoCwy10oxbcqWqDYn0Arl+XPVOSkfigXjVzymKqVlYmal0wkvK4zlCUZ1bZN1Zv4SZK6tJAa0qM1vF8PL71rCE2qrmw14N9E5A4mieNJ+B+JzSF5MiHdMIbHdGACIYWIYem6fnuS2ZjGTpMmB1LnPch875/GdDWUJQxCMSAIc3hdPpYGYpCGHuRoAGPohC4YcFlJqhMmuMJHyS35+mAISFEJERFksHHjPx0+uef/DJ3z6VBGvxggogcOh54ffepWDg45Hz694+NkFj16sWR04VtrhO8EPwtE48vjRJeuriTt3D/iNb53i2HLIXc4vX/DT9z/lchuYJPH+O58SYof4juPDJf/it9/g4Djw2995QFLP9Vq53kSc39L3Dk9mYROMkEUTnQpbhR+/9wm/fPiCMDmgYzl4fvidB3z/67c4Wig3IfBse0kMwsotOTgQmCYLyxQHLoCDzZQ9o2rhQGYV7PCacNFjHl7bIMIUEQLqHEEgcgFusLnTeXpxxCkh3nN2+3YdjSlMVVE07CeMQdhGJV2O/PVPnvLw02ecX24InPPotUOGbmCxGLiK5xwcdGyicHmTcNJzuOrYhohGT5IFKW8Qh8vIvdNjLqMSxkiPx0tP8BP9MLDQkruRjUA5FzTFyDiZUp6ihaaFGEghEWK0chjarNk8r1IQ81IUYCOSmW8dp2dHvPL6HaZx06yD7PLNc7tu7epw0TNOW67GA44mx/XGPLNv3bnF8WrN8aJjKcpVnAyIlvzQVLxhRZMVild7R9A0iyMEy7V0zJT7FbRGAzIGBoWokeFw4OjWkoOV52ad+NG33uIXn1zyJz/+iItR+I1XFlw+HRlcT9c5FqsF3/7eN7jzpz/nww8eUUWNmrxTJ4ytJuQTQWGKnqSeqwt4+PEIKeK8eX1JqThyM2jWGdyKIsHubzlSxsp4M96gDu7eP+HgoEe54fTOKQ/euE13eMLZ2V0Oh45eJkK8MQUgknNUzRBVoiM0f6dJOT484mYrbKc4h1JlWvkMX01BqTJeq7wrpDBzKFru/8bAViV8S+QnDWCl4q0ygypgLN63QlZRdIh2m6iAKSPNEGJBm4gY82l2x80Py0aHGBumS/LeIKY8lNqMljtt5GCFrn8akxHWUJ6jGUS6CnhLm2p7y2cyEwGVFxLI3s/soXbemD+x0MYUzeiTcmkR7x0jcHE+8enHTzk5G/jq67fwDAzdEu9TjpXpwSmlBp1qNmA2IXPFKNuCVtVc4oq99QY177N2OZ+zbzfjsg9edxWhArBL1+wqMe0h0nSkNNgaGyPX5DjXQc2n77ZvX5nKBnMwAqpymXMsVFktFigWiVJzJVMihMk81NOEOg8JbraWL+y9w3lnZEFF4cD4fYoHstZCzspBahSqWbFmlgM6G2deDt21OV0gIVoiq/JqLMQzpf/Kgqs1HeauKe/XGnBmfaIs1IpKmrbMg9Ya1F9a+/tHyaFOGZ+2J9U1kqOxZBd7Sl5LuC4bNbTOCVUyA3iRTnsKVsYips/PJFcWVWbPiCnNIcD5Ou+KC2RWwFqNMzXjpVp4IBoZVdZFlvnFSGLjUyIJGnGVXsbb2txn/myeH61BZtegYH6gZruxt9DP9xZ/eXwxjl9fWUTn0IvKDNd6vOyoE2FP4O9bHHY/axe71vVfaH2rcqfz/SswqIsw7balyuw9dNpuCNIs4gIW9q6fk561/bjepgCv3Z16TuyXYm7KX3dOiJ2gyeGlo/ezmBqnmAV9FYlVWZ47TUkS+fjjK5bLJSenlkuXokO0FIaOWQksidy5J6TEzDedoBZqVRjG2nFRDPCWIs25wgVA3oyKtzQZOPCpAk8vMCw6ECWEwNA57p4d4YeeqBNM4LwROXQeDg48x8uOk6MFcYoEjUzJLN3LHug66Ds+6g3E3zpbcefskKOVI00T7vCAOBoxgBfoe8mlCyzG1DyeiscxLIxx7sHtQ7OaqpAw9tbPnl2xvpk4WHiGpeP0tlHCxzQBi7zZ2vhYSqIgmYin77MlPRqwyyZZks6KfJmHhdDFJlLx87js7bGwS+9tzscMFm1fsE0gxUhMES8963HLs+eXPH1xzXo7EnCcXy84Oezoh8T1zQZ1A4uFQzVwdTMSEjmkCcI0sUGYEDbjyAtZM40BUVj0vW1Uuabhcjlk1jpT3KzguJE+mEctk1skhSmHVTV7ve2BWvFC5kCBxu5o4a7C9eWGZ4/P0TcOScnK8ghG0F/1pAJuULyPPP70ivc+eM7i7j0265GHnz2ldwtu3xJcdNy6Fbj1YGWkLqlYcCu8aYRDAScZvJc1gDGxdn7GPrWAcVO+pigsDtBofbLoO3w/kK7WrENiG+ypJVQdbK0cDAvcauDDn3/C1c2Wzs/h6nklZ7noK8guFm8U4hR59OSKzc2U2Ymtd+3atCPHqH1vcqNY8CUDqHE0QqkpKkGFbvDIIPzy0yf0iy3erVieHbLIwFNJxmqpcz4obbiXU7ZJWS17lgvzQseQ5lI9uc6lFIBV5H0DQApgVy3flc+aL1qwLuXjsofs7jv1KPtM85XunKe7Jzff7gbVlZvNxB3z5fO+WTzWZeOyiAED02EKdF0J73eod3RdNvq1oEuZm5WBY/tR6SyBWv6qfRURk/EiQueE1PkamRIzOU+IOYdQBCuBEokx4FhQgk1DTDhNtgeUfcQ1wLEBqLWtdcXlNmoB102+YL1mJ7OL9utf5THeB/gzNrHr5hw9+6Ck0cz5y3k+tde1M0KkvkHlFsie3uRKfl4uJF+bOCMIbWTebEvI3ulMVN5lJlYbOkdyPns1Z7Zlp1azt6Sx1LqWqdlrYJ6PtIpFMxnaHxUe6A4eIL9TXWa1W7TiharXlbUpeyun3E/K3NSsqMjOvWqJjZf6bveoWIwib8pzKJ/UPp5XqtZhbdFbvWMey7p6ykd5Lpexrae7hkEW8zSCYaNUW8es39FsYfkdZy+h0lxiayeVubI7150KVWHP37kd5matU7kokapGXmbSeqdzbO8t7dlZV9rcs9zZOsJnb2eLH2dyn3mMdm5Vrv/y+MIevz4bKpotkFIHGzIxSyMMXJO/1x66u2POxXChhvaUEg4zcZ+i6mZKbwoOKxN63hTMwloWzWxSsnNndsLmzo1QmW0SPvAAACAASURBVNsy52XMm0sJbRGykpTPtw1S6Lu2jWVltlaTIq0TnYNu2VMsewAxjqDK8+dXnJ6e0PWmJLqCU1VxuXSJSkKJXF9FxBnpATKi9CQt2lywdldHqq91lDSZsil5LK2OmdFnzAru7oZb8oHqPTpTLqMYyL17e8XZrddIWE6iU8FJoO+tnMEUIlvtWeKJlyMXV9HyAsXuE6an3D+7w3e+dh9P4vrqmgT4PnL39pK7t19FfIfve37zK0cowsnxKUM3kMZr0MjV5Y0pNWJhf+OVcHI6MGenJCIjEVivLc/htdMzXj89w3fK8VnPdgr84zuPefToOc/9gjunK16/d0LfJ2LY5hqa5oU08yG4JHg2dN3A9bWy3QSGhePkVm/AaiyMsdYOo2GnSk8vc/1L2zQcXWbbS2E0K6ZI2X2wfDIBAi5tOVycIJL4j35wj6dvHPD3v3zK+cWaH33/AbqdcAGO7t3GHwywveDO7SPe/+VTLp9d8MPvvcHprQPCdot4T9d5rq49IcLx2rE6WLCdEttxoqdjCiErs9iGXskE5jUdbanknEsL90atLmFZg3WrTpn4Qy3csGw4XQYZTx49592ffYT/vd+i2MfzjpzlTvGcGznKSpQXl4Ef/93bPPjKfS6vLvm7dz7mL//uIceHHb/73bf47re/wh+99j1ErknkGqEZfUcsPy5l2SBqimmG2jg8VuolcXW9xfUDnXMMrslTNtRDRVMKTJFpvWF9szGl18Of/PRDjg9u8YNvvc53Xr/D1aOPoE9snm+495U7DCcH/O//7m/46OELemfFwWMGd0VREG3Co7JtYnAWwvaTt39hnnuxnMzdTOQGUJYxUZMLFQQmix64uba/P/z4BV3vGfqBTz57wd/+4wecHB6y/KOek9V9RMF3KXvGEpGE0z7f3uV2W5mTbUwcHSw5uFxzcXXDNCVjBhYIubRQVyj4syt0bq4Qa23AWVEsob810FKLCgct0Y0pW1ogaj5V61jV9AMsHHD3mFXuuT1KKUhvzyrpAK2ykuV/YWL0NcauyltNJXTZPtvcjPS9Vu+TiOA7I5jyMnsZLNQ21XlRNuNYHp2oTNyl9a2CIAlczmV0TiyHG1tX0nkSHSENFA9JF4X+bMnJ6g6LxSIbCEaiJMvzjpB0m9dq6S2pe+esfM1zr37mXF5jc/vsBdQidKouYTl+JQqhegmlqALNPl+9K42Sp5oZxcljlT08Qt0XdzyRklWaYqVgni/lBC3jWD+Zx99XDDEbtqqyhjae9VkplzyHC7cCCiKK85aT7IdFDS93zJ726umROTSw+voUqDK7yJFZBaklQZp+U7Qas2ZvYu3O+h51TVbMNOegSjWattrlHBlQ1purfdIoTTsKCs19ZefvfXxZZXGeYvOcl8qMWlFfUdIka2ylid61mUvsSwOf57hWbJyNpqrUfHK1PYUsu83gkmWTmyMCVPfCspu1Y3OktL/8D6haXdJsJVDZ9VqX1eAwXB7TPLY7aqDOIeKqLt93ztXf98qXdVbxonM760HIMq7qnTmdTG29lXnajtGXxxfv+PXrLCrV4th8WgVIK2T2LXowW1ygURDLpC3/uHJ+EciYgJ33nYwB2mdI/b/+JnsTsNlw2ROGzSn1/528QyET0szKYjlStDCFziW0hNk2imd5TrX8p/bv/A5CZc3bbNcc65ExgkZTTHy11Ak4n2V14hvfvI/3jileEWLCu4SyLRDIFmV+Xim5UPKnnHOZ3CO/s3M1zCfluk6Fur7tp5gmfNfNLLZ5YztYdgzDAjf0LFToVYm6YTslbmKC3nH57JoPHr7gr/7uEyYiZ8sDem+enVW3ZuFPiAFcLxweLeiHJc4vWPUJDWY5FQfffusMXMfNjWOzViKOYekYbq7p+hXPQuTiZsPlhXJ6b4WjI2wDURLqEjfbxJNHkTA6pq2SdKIfIl/72i0ODjp+8M27/M637xP7wcpubG1bCwJjGEE8iCep2EaQ85xk4Xn89IonjzfcOltw55UT0mjndF7Y6JbilWqPAsLK5BYxFaasnWr5b8CAE2d5RTox9I7bhyu+ceervDhf82wdSZvEb3/3Dp988IKL88i9e6/jjhc8fP9nnN2+zc1m4vmLZ7xy94hX7t0i3UysDgZWBz1X45pxhL/9yUdcbwJPnq/ZbgLOLQhxwzSNeV8ouQ3kMEOzyLebfDf0uGTeku12tJA3X+SBbUxuT1GUPM1FLW9rdbDAuUwmNFXmo925qWpjoT23bp1w6+SQoFvGsAHME/b/svcmzZYcWXrYd9w94t435YihANQ8dbO6m61mS6RoMpNpoZ1opo2M/AH6ETJt9AO01lK/QGtuuCFNksmMRmoykc1u1MAaMCQKQCIz33TvjXD3o8UZ3CPuzWKr0CZigYAh33sxuh8/fubh81c73E97bDYBm+0V9ngJChniCRMhRjzRqjwigRFQeUKo6pEPAaUQ7g4Vv/zoGpvzC1ydJ7z1QJuqFxXoNN/EBa5Qcagzbvf3mHb3ON8M+PjVHcbxMR6djXiwCXgxZcQLKVwzxIDNmITeAJi1sXtDE7WNc+7kB5lDIKDkjFc3t9pUXCljn79jMpgTVRMmuStpLsrpfGCENGJ/P6GUivNzQtnP2E2M87OAzdmAzTZhf3OLq2GDRJJ/W8Ha+qUTXLiaDocxRQQKOEwzahXFLIHU26DWfxVQe48UrSVyF/mkCY8ZLx1OPp/GQEy57PuYWRKaYtfyO36l8aueLq4F7AbWjkdxUwSpa72wiGyBps8GKagFln6TrC0cUtH8tBTViBfl1UHaKJApM7aeRGAUrTCtRYDQQjpFAZJ82spCd6SXpaRzDGkQGMVWEA1gjGmDB5fnmKYZNzfXCCFgGKIKznDBtsFB5lipwdZDIZ1a2PxbGK7rZB3/J3VVGcf3yABV+NlcWQ2L2uoxe+ifGZtsDUyPMSXUV90MC92+aNcAyWslnzsArWC5Cjc1g8dCyXQMWuIM+8uFZ4QgmbvMCLF6ekme5daq2ob1gq3OU1wa6OQioSqLkFK0Zxehuvp/1eqito6n+iH2Xv/eY9nkkTZbu25tlboMJVWeyPGh3b9SWFaHRbWowOYKjVwkn4sCFS43LgZm35M/pM+kwcykUnKjufG/Xrky/K4mGzMvn/blp6P9Ia+ifqQn5rxUkqHzFj4se07qBLSwbagRvc2fGo6bsmuXdP1arYrmKa7aLsfDVm3u3Ko0t1oaOmv1HTWQk+NmPTLGfX18lY4vkbMYWhiUhq2wlYxUxivNXMXKYPKIPuwCinkAhamYJ1HvrKFpbCYiMAN15dImguUTEWkYZMXCCitfZQ2nkncWaz7f3eMjbPgtjjluM6AQju4nACkG3Zy1WQddEGvfWSvPYqkzQgMQBcQY8M1vvodSMvI8yZiYkVm8iody0AIEwHa8ANG1Pj+A6Bw5ikWeOaplmBFJcshy6a1+DC6d5Ujvtc0sy8knNzJRkhYLcRC4WtXHqWA33YMIuGHx+qSYkChgxIhAwN/73rv4T3/ybfzX/+BPQMQY6ELgGghTvcPhwLh9dSu9ucIWFQW3+x1++qtbbLdnON8kjKFi8woYU0KKhESEzbgR69rFCIoRV3GL8+053n6DMEBxlsS+luIWodziZv8ZDoUxXJxjO44I84w6FSAOyBxxqAWIN6gloFataEoFIOlLmYII9XNlhDEh8BnKVPCNJwlvPrzEMATsbna4v5/BGHB2thHLGqDtJdaAlX+kZLYog1IYSSy60hDbQoJFoRyHLQ50ifnxLVIc8U/+2S/x0599jo9f3ODNRw/x858+x3Ax4vy9K3yODHx+j7eePMAblyP+wx//BFOp+Bd/+SE+/PADvPfOA+x3ezx/dY1xe46XN7coqHh1e4vnr3aIaYMJB/GQFhlrCNGNHHMpLtgFigBmUCIMiJhzwf3hXvd1Y3JjikCSYktS+EboRilSvIe54uoq4c03z1FLxjwdQEXxNHQCJ4TBZiLMY8Sn0y0mFPzlv/kFnj2/we7mgEcXW1w+2OD/ev9DPL+b8ed//gPJvzoUmVMwxm5KDWS9uUoRFRBqjZIrVGdMJYNpB0ZFrhtM+RybgRCHIDkwlnsTJF/zfPMI24snODsHSkiYaAbxJb759hPMN8/x2Yf3CMMWqBFU71BzwXyYUecMcMGQRuRSwKWowSECtWi9DXblo1bpqxhVuK9cNKdqKWg1ubdTVBiIcYvC0rsxpoTz7QbbM2BICW++9QRxiPjolx+AMOCtJ+dIsWAk4OHFBe64Yj9npFiQkoTGhQA1UEmhILO+c8mYDjsQMS6urkBJlRGSMH3LA+LOuOdDhtDjFq51LET2RgTnWUcKXRPevQgNGzRUGe8VlA5yTRhuozIB037K3R1TMQUFqsywKZ+M3vNZpAIaNpsNAEaIG91jWfqPVuCwk8qYkQJiCBLuD0aeqxeUgoYuxxCQBkLOQXPeitL5Ni7JwUpeaIS0WfJciuz3WYVGAoYgDWdynkAAHj96gDkX5Jx9qhSbp4F9DU4L+8t1ERlCziscmZeGjbbI8gyZd1m+ZziGtpy2QroG8neKaZUXpvu81NaDUo/a4RO69XWmyaw0mtptp1p91KZMUSe8u6LV4Rlz1b1jcxJ5oXCE5aMxGV8XT6vlKIoBDi5vMaRQDluYeFfxvGgl1jafNiYbl+TYUrcEZhhsy1IRHZe4myx3ckT1Z5cKZKnHeGHr0XszexyoTXvXsVJ7apXvZ/uSoJEFMAVNlVABPvq9CnjmAcy96GMxD12v1HXKoofPc3uXGZ96XDMnhqylTtO1quUa9LEhi/NaSKunSVaQypTuWspx3qmu08KwBnhE3XEIt3j9mRk1C+6aE6VYDYdubFXzPSUFoukDxk+JpC7BipB+fXyFjt8/DLUjKqRVS6Wptm6+SkAQYmQEtQv2EWRRiyCHxT7SogtuZkFjus2Ks4idV8FO9BoVHNVK7JZ0s2r5a3tb1VoIaV4+vSpzVQtJq8zVP2GXm4fFNlsnBsiYoAnRTkFkQ4uQLMRcqkpKMntKA4xhsCqLhaWCoRR4qAgDPMzUK/cxacECgDmDWKqdNrbJEKuiwUCIXKlZ7uOWv2IEyEJQYfMFt0p/6MKOa5FCCCEgUARzQCXGvu5BCJjuZ8QDI0YGMCDwQQl+AEKR9gW1Ka0IEYEZ55uENFQMQ5H8nTQIgaQMK9DBlQCqYB4whoSwiSCSPM5KRfCNIwZEPLk8x9M//I6sV0xinVeCV1FxqIpbeaNORCXMlRAwIHIQjyNrMYrKYMj40yZiDDInroxxjGBYCI/hTydEWFVhJlFolZGoCQXFijZ1MBeGB3z8yXP85oMbbN6egLrFP/0/P8AHz54j54pnX9zhk5cHXF4QNtuIcPEpnpyNuD4HPkyEX5yfYy7Av/63z7ApAUgRr66v8erlNcJwhvPLS7y8zthPjHEMkoeUxSs6pAhQQEhJijQVMQ6U6hPUsYogUmuRHL8E21UIFDCOCcyzzpNEKNUWBLJgFUNKONtuAMXHMQ5inChFhCndGwBU0Z4QC+NwmBEzMPIMTgFpMwAMfOetx/iDH76Hs/OE69/eIYaNvqcuDDvmgSA0oS+GAYyKIVSkiwE/+eF7QFBpIhMKM4LYazx0MjMwlYoHI/DxR8/w859/gsMMXI0DSp7wxpNLPHlDmtzfvLrBJp55YRnb+2b1VxYrxgQtMCQbXcLKpFce1KMXsT0bUErG/nZW6tcd1OhQT9hKmTW6o6LWGYepYhhH5Cnj2UefIUWhbSmNeOvtSySKSAjI9zMGAEO4QOAKrhM4kKyThrcvJFECtmcbjOOEm9tJ8vRgkSvUUU0NCl4oeD0fONYhjLa5IoHjo6f1/ZjIPF2qE1i7tqXacfoQhYUWoO0VR5uTItWCNzQeZcJWdQNkqNV1S4l4gRaqAUwB3e0nxBgwpkHhWDFpASauMzILL7YwwNBFjYh3Tj2O1rfRm4QzyPq8qUGx5BnFsTFgzrPyQg3D05BwAwR3hl0Dis1zEQLZyIcqeqz3sgrRXSSBC7jUKv0Covx1RcD6VTOJwAT1UjQcc2VE8Od6JbUbs42J7D+yYiXkiqvcLHyxV0rsGV/nBhKYx8XajgSIQW4RbsmQPFBmDTlUzxdDQpF1pk0hga+7RXXUagacogatpdLVZK+2Rp4tZ1WDVTHhTvl2Parjb2Q4Z+vZyT+L72B98OKcvKIzCvTr1a+LXXOHRX+vzEMUZurwq3uHt3rT+23teT1mXk6l+7Va5WBV7mze3MHBPd79/PkUHAzve1jYnNqXlwYX2yMqW9kZu8eWmy2ax/aa3lg0GoV6w5fsYYtMMDwzfA8U257nPqxaYc7ag9lpX4sk+uvQ1q+Pfz/H7x+GCsAtHoAsuq0/AZk1ZyEYEdXdtNhQgibNQ85+rzGOJiywEhsjrnVR2nixQcwCTe1p+33R/Je6L5ARaUso18IlIUiDVWNmRO5Z9BGurC5mEWSgJfXDpi/koqi1NpB6BojUsilKY1PKCLvdAbUyxjEhxqiW/ojzIckYmLXlgIzfejAyog4iSwP5cCZQ16p8Vp2MbdzchPeUklca9NVhETxdWNNz03SQxtLDIMyPgRATuEpF10DSewxEoCRVQAvEus1ZliHgAGtsHxLAhbUfZADqpHMmvPN4A0slsGI6U6nKjAHUCQTG5cUFfvXRS/yLv/gI91PFxfYMf//PvourDXA2zEBg7PYVsQKVgijecQ8JQyREjLo+M2AGjwoUBggRMSQpZY7ZlTkgItaKmCZUGpGLwDPGiBCk6A3VChSgBhN0DT/Nqti2iBftRUApJquoAlOBkkW4CZFwdjni4dMNhniG/+Vfvo/3f/kBSs7YMrCbRzz83hXu7zKuX1Wk4YDdZcIzZA0zblbBmzrjo3/53IslEO1wdrZHYSnYsykRRBFnaYOKooV1VEHOs4ZGESgGzU9se4uZvBT+4XAvhVpY9vI8z5BtIPiishUKILkcLP07hzQqfovi2Sy3ndCLijpVzPcVb1xdIdeKDz+5xau7DNSKOd/idgdcvvkOxqtH2GwScjlgE8+gIrwIfZW9ip3tZ0ZEihG57IzrAhBCSgiCS4GQEmOeDwgwY4l4KIgCcg547/vfwG4M2JwxqGSUnHF32IFiwGaQ3BiKATUm1BBRXKgkZC4oRQYZg/YyRPDKg+ZRqCyennEzYJ4nHHa7DufacdrDA4WxzAtMmOeKPGnrj8MBFAkzBtwfrrH7VcEwbnDx02f4/PaAR9uE73/zIcYhaisGACx5j2bNT6S9SpmRAjDPM65v7jBsRtS72xb6Dm0SH4IKm6q1dULsQmjqtMa1gtZPvl9X0vsaDbTQU1IctZAqe27ZpN7kHEGJzhR5UvbRay6ANR5qfKuqAG58h1VZzH1F1aP3N96XS8WcD1IYK0WMm9GrqFKAtPPQdhg1F+z2e9RStHUSmvC70NhYSVVnLTXjozARUA1u0AWkRJf1ijX1YDF+nZvxn9ZOQxRUWt3bDquFoNhBpOFvdREF07fqObYIdO+UmPEWBWCKSFdywQoiAcucWblmilIf4mkGj2OhXtIomkDdlJSGw32BE8+XNVwnSN2CMMgQagGXoB4cuTel5OMBE2otYgCigFqKj0m8+DIGi3Rpy7+KQtAol6ZoG62UudcOLn1ItRnBa21Kmz2/DmFdFD4ymel14ZgnlMXlPS1cVjNq1KjOvs/UjLF8Fm3/rcd6ykt8FCarilAxpV5xtWgbE8HuxYy6fxvsj+brT67XRuHLDX9NsSRePtMb4RagZsM3KNzJlcfaG+59Q6gH219vOFU1ysyUYgvl7rzISjNa+DmU/6+z6b8+virHl6iGipMWWT/Xc0Hjjev77PDNtdxwS4t3f03+PhUuYXvJKmvZIEjVzX4sx6GkSpRMrzUF8yjQejkkEza8cFRHwLT+4ZK/mwDA7J6DRlDgShxg3sSmzBW1yNj4A5HmSqqCq5YaCeNToqa5V5nz4vtEBhsLGVDYknlr+IhgiQVWmYy4HYQIVwYVE+ICSmVMU8YQA1KS3CMS1yNA5nIRIZSpAp7crQMgCS0JLuwZ4TdBWMZWYA25ne+CAEyFMWwi3nrjHNNUMY5bpDSDKaDo6wpngAPmIiFVlQtARQnYIHBRz0ozVGjxABXMjXHXwBBh2PDIub4yV21pwlGUoY6ptvDJBTpKrzji3tir+CEnclFcywX39xN293vkqeIw7cE1I4ARQkJICUSEmSsOXBFLQcksbU06PBpCwIyCAyzMSISIyhkpBZQStPccEJLm2QaVp8gMLK3wVclNiCKC9J7UtQzOdNqka1XmBnLhxSyPAKsnM2CeWlh2gx133FeKuNwfCh5cDtgOwO01ME9SHGg+zGAQrm9e4fPPXmDaZwxxg8oZZknWLdmUK2tHYBU0oaiuCfqZzUtgc7QCW0qQSHC5UkGtBfOcMc9FKqNatTqKiEkKC5mHiUi8svOcsdvda4uPHkk6L4fmqPRMOs+SxzjPM2o+VhRPH0bI1Ddp7w3QtZDwaO8ukgtqYex2B+z2EyoxhjEAsWqEVnCWILgmcHPhmiuSFlQCmic6WLiX/e+CZp8j8zumYRuqn/N6j/GCk3TndR29CAlcsTBv4/KB48/2n+bFPSth2IDDvCgpbx4HyXnCUrokgUenp/n3jPSwhiFXNiFdCuIQBSk2RQWkIWlkRkvADUhE9u7mCXGWT7Y/ACCgyXhtbl6l1U/ozlgoR7x6ag3X47WR0yaYStjlWvReHy0c0ga/kk9cbmnT6Pm0KS2e/7hCAOa20v2MTvHQNne9X+G0lt/7L9j4q/aqlfQDeA7Ysoenepc8+lSpZIhaDZ1bQUGnMRqtYCGM1BQgo4GeKrNQBP2TCwVn4e13iPyuzdrmfEpJWsRC+DyX9yxe0l1rIoRV/zS4yHs8Crz7Sr82r3052j0ur+oD3oLE1lY9d7ZGROSizmK+/Zt/1xx9btw9ZLyrG0uHWGtcXB7yrOdm616oHWyYmp9VPmt5reZpNkWaRaZzQZpRO2VR6Ecnm+sY1/L418dX6/gSnsWqSNIorCdMa7GP5vaHMxeQEY/G3QxBneGtlTgK6E85slG7t4+9djl08T5yi7uOcpG47heUKZgn1IU2ALy6eWFhxYpNdJvSx4PGgPsx9/Hji9A34awYN+axs1AToGaxhFbIJg0hIuciPboAjNoWQYgSAZxQ9G8biIVVHA47UCDEEDGMAxiSd0YE98RaXkPOXX8rnVgIokTM06xwSZhzwX53wGYcMHBCiCKQ1FlgEIKG9VWFY0xqPc8AJ2fKFQyEAgoBuZA0sTVGVEVwdfzw4keEu90OVxcRf/9vf0PhGXGYD2BmTDWJwMKMShUUCRFmYTUinMEcPCQuhIhaxCIPZkjIJAEkW4ismACJp4dRfFxcsnqCAEJFrmq6UEYNw55uboJCWuWzs7XKOskzN3c77O8nHKYJz1/e4cXtHg/GGQ8vBmzSiLvbCfMYcDcV/PyDa6QkzcFv5h3uZsZ2GxBowCYNUpQpAhwGXJwFbYEh7TjmfMBUIoqW8edaMFdR2GJMXqEvhgQPtGJGJLVictIQUYBLRS0ZwxjdyBEjCd5V81wTypRRtFiEVYVLA2EYAw77g+NmH87ElSXsiRkIjFzEW5lLxatXB0yVERJQSkSiisP1NV59Ln01U9hICJ2H/xIIEubKVFFJRKwICYENuv8LifLMRsMAoBbkqnlQLtc0EZBLwctPb/D5B9eYdhlxG8EYkMKA3aHghgoYsqe4suBTKSjzLAYAF9SFRhCz5rb2Qprg0v3tTre7eL5Ja+e6YMpNIOpIg9Nsgaww/kABtWZXPAhAihmEiCEE3N7vQRW43F7gwYMz7T0pOcXEFYVsvbTysvIIImAgQkoRQxpRivQoFCO8bBwx4lnP3KUQt/QyMJrm0mle6G7p+RI6VmHz1/PWlL01E28KQL+iRyIOdRzOYLschr6fFw+bJ6k/mJQGkmlvTQh0D4ILg8tcJcvlrKUgFzGElGBVryUsNecseY7jRqJW3LBloGPJX2RGqVpAySbDbVK9MM+19ztV5+89X7TrxsuJeVnZxN7lIFrxdxdGu3eaQNrhiCnUNsLWCaBbkNqtg+ZWiQdK1v60YrQW0pf3tbC+JZYwmufMjMRVU0YsmsjfYrhK5JEfQQ0qrFEPkv4h3kTrTsuaEzrXSb3JssdSGmA1HRJJGoH36wsWUdHaHhhIiEwpUHCF2hW1M9yUa6FaTqcaptwhykpDX6eswBXW/p4+5HO5CDbGJi/ZufU+RVdozU0GZgQyPHbZzRCkLtdiafZp7+fjMTLEg9g7U6rd1+GaGNyo+w4vvtPI2gr/ejmz3d02FXOXG0o+Rl+qHl4r+rh8H3m/SY7L/W1GLfNeilG4+ruass0OXx8jGs2q3dx+B2p8fXwFji9VDdWQwTxUDFq4vNt9nSGBAClNrxXYlBAuGK4xBCyfbaGSq4zD7uWkjKdnMKbwGS6GfuOvkLdnvo3Auz37GBAmZfQEjCRUFWg5f+iIrwnAcv24WE6MEZEIuRbEkNxqI7fF9o1O2YwhYD/NmOcinolAnisFEuUrxlbhTL4bUErG4SClzTfjgM1m6zM2+YQUxuJdM8VW/i+VEa1UszJRChUxVpydD9IaJBYvulEyEMKAiIDK0vXbiiChshQTsnWLAKOAawaF5AWPKtAqu3qyuQqUUQXQecI0A/Nsa1GkGA9ZrQcCEFBpRohZvY1aaa4UcCgI5g0tQEnK2Dkr/gqhlzBlFVaiCO1FJPhGmMHSRzMywJI7GWLR/WLWwbZRzENl53LNmOYMhoRqWCjwfj/h5fUN7nb3uJ8mhDRgezHirRqxjSPuakadM+7mGc8j481HD3BxlvBiv0fZZbzcMxIlnMcNhgRsLyLSOGLDFYgyh1qB3W4GoyAkQhoDSqmYHRO7qAAAIABJREFU56JhUBLCC5YeT5Kf0HAkBMntrFwRKYjCydr7icmrEMYk4YopROTMKFNWZhRceq9VBN8YqYW16IdiCBpylsEMJGKMA+P5zYTrA+Nmt8dcMzIXjOMG+/sJD6/O8fTxQ2w2G9zOE4aYMNcZDA2bUS+MCGbVNoIwY2hOkecKGaMnoAYwCmoogmNqbfVy5DVgoAFjGFDB2JUZjC1qmfHixTXmbQTVAFQxCgUwxhhxeX6GFCM4dEYMItRQWynyrkI1BQvRDaAQVbFchlCZUWylo4CCVLUTgU/WionUg6F0KwCFClIKuN9nHA4F+cCYdoy7uwqqGZuNhO0Si9JngiQ4djSMMeUZJReEQMi5egVZCR1jgKJTc1OK2vj1UKGLlIbzCQ3NDe7d+ZZO4C/SdVpwIXj11tWxPtUUIHun0ag2Bjvryn3/bP9um5M+3+RBPp4797Sd2vNB+rUyi3IoFbC7SBVeNtK2iqLe3gZN8BVa1pTnll5ieNaKVLDuk6BKvlUeBYzvU2OdHT9bw1bOqlJjxuHUlISai+OEjweai1ctR9bgK9cJ3JRzV34FjvYK47v2nCuhJgTbivWCsY36JJ6Y2a/JMbp0+t5eDWjzAMPvjTEgxijROgwxsHJBrRn7GSgFWviuIOeitFpaZ6XU8kILdK8N0kYrWjl0gzFBagaQFljrZhEQGkxJW7con8soCK5EkCj1rBVZj0HSAaeD3wkx6/j2puwsoktW1+V9rND3IcuTbDjki9D25gm6Yedf751rx1FfQZUTmVb7toOLKXOnvt/OtR3RbCU9nmABy0bvjinM6zyNfl7X3qwshvcuDbusUl1ecYONIvNxTq5sLDGUrA0xpzy5Xx9fleP3VhYFCVsStatfi9XumbAmv2p32WohkWhJwHKvBkXSEqntsNAucqK9du0Lkw4MDXmkow2R9d0hnA7LcmbvDLeNh1TRI66SV0cm8LfiLpXZE8xDJYdPb9Ux72Xl0m9/GV8RgbfWIvluMDm15TJa01eiikCSpzEOCcMwAigo5SDkkQFClmJDU/V8S7HKSQjn1dWV5k8EYa5aVl16LpqVtaBWljwI39QE5oC5SG5hJAs3qxjiAI5i1cyFQUWyuobEYFSJ3dcebAgFmaXwSUwJXAuYCwJLHztgAJcAZsncClyhCasINCqRMU8ogSpQNwMGiuKVYUYMEbwpSsS0GiMHDX9VD5b1f4tRxggGpwiOEtIbBw3TgoZMooLCjKQ4UIvk26UwgpHAtBfFA4MUxtFYspTEA9njthU36sOeChdwrQhhwDiMyHWWIjLzhBAiHjy8wjBucLXb49nHn+Hliz3y+QZTPSCHGTlkDAxsA2OMDC575MJ4cL7Bfh6QVCjYbBKGMeDBVvC2Rql6WGtAwAZnW0blCYUZdWagBgxBBf9AUvSCmwGhCV4qmlUNaSbZv+Nmg9u7O8mPMtmSCSkkzNMECsB4EVEyYXfIqBCL+O31Dp98/Bzxh2+jFEZA9fxbaON3g+c0EfZ7YKrX+M/+7F38D3/1CZ4/v8OjMyDnO5QU8Z//F/8J/t6f/QgvvvgApe4BvoD0zCwoLAVeNK1SdiAVTDkjarsUVos+MWEIA7hkUbADAxwwZ7E09Ok2gQjz4RpXj57i7TcHvPl0i19/dg8Oe5T5gE+efYqrsxHvvPsEBa/ApYLqjDGOePvpI/x0+AyYB1QWod/CsYmNqatgFEgriDLABVkFf1dgesKnXgVLOGoKShJaQCI4sGZOxhCk2BOAIZPsc87AUPHBp79Brde4uDzHD7/zGA8uzvHO0yd4eDlAijsZTQZQMgozYhK+EFLCZrPFdjvi5UsRZslbq1TtD2jCvXMcFzA8QsNzcnqhaUXoXQDvTIC1iey9YVAjzqXa5L9DkhXqhgbrE/f0HocjL0j/rg6f3etk/9fawkaLevw0nLnkbHqP8zKbWYgRMYqnn2vFOAodKiWjVvUea7heKaxFiZS3Ke+2onXUNq8DsLSSkSpMdlXTuzVoguYSFfu5A5ZuILmzlbXyIlcNwVcFuCj/S0l4SBQ+lAZpOQOW1AGiRppM6VvAujZh1+Al6RjkHrmlR6Zp/XatFoGdNZD3tAn9hkUISXEQpZsn1Shde2uDoPeULGstbRwkhN2MA0MEhgThWUygeAaCGPdKqciz0OE+LHma5FzALCROC/KBtX0CNbWVXPYg11kIENpiOGZRBwQx8gR9l663KaG8nKpAipdz7RWjJm84Ai2gRWiylRuhTH5TI0VLE4DKV12YMbXcW1ciTyi4sv6dIUZlz34cNn9T5gFWoyeQO+WtKa9qwDAFk9tcAEhfZZhh0pSx7ptmNNE9Td1YJNRV6Tcfy8K2J2yiJqv38zV64y4YIiQ02doLYJF6vSF71mkvB8FbAoDohn4lWt3XugThr4+v3PElchbF6t5v2UU4oFsfls8YJ/Xm6GrBAJFveDm93Az2M2iooVs4VPBpFlr5bqVuE3ajJJJKhXJ7UuKw3LA+Zic8TqFg5N+VP1NaIQ1RfaMZcbRX2VTRvqNQ9De3f2V8mgsNiqFrEGxDY6dMohTa2NQazNFUVFiTcU7R58BkX+JFU2gR6IMzoxqCh9yF0FuTG3wkR1K9mWx5FQVDkmb1XkmLpRAHkfbXIwJUyE1uOZaQT5YWt84uyAguM1ifMfgTAaRVMI0hmbXTmJgVVjHxUBRgYwfm5QhO5C0xzBlcTI5fAYSk72vLzUDsvcgM0KCrpeXfvWiMTKzH9z4E2Yh/tHliQAUhIajwHhEp4Owi4jvfegoQ4erhBr99do1pOiAfbsE5ImAEaJJeeyxFEqZ5Rs47UIhI51ciVEWSFgWIUlUPUQsIQcKCAa/ECpAqeIZ8rDmLjJa23zN8tTzaGpEIlFKUIrqQFigApaIWxjAMALHkVTKhaD+5n/z4Xfzdv/sjXN98gLkUKSqjnL/kqp4vVZSIMdcD3n7wBF+8UKGFGJwTaiEMmw2+9WSLbz8MePlZAZeEGk3osV1ttfxMmQIIVqreWvnIXDNnQ1Rn3DFEVz7YBBFI4Z/DXUHZZVxuB2GXNeD+UJA5gWICCqHyiBhFKSx5jwcXolzvpiwjM8+3SQXcRGByRt2EFydEMsjGlo2UdAKgXCxagVFOEKnhh8SjT4FQSUSwEBgxM8oMcI14dHEpDdkzI1LEhiLuNAy+Oq2SDzIzRgxIHFDKjBTUe+UGMQAhKN02A9ZqDoAX4/LQNDTlyqfKjdJ6aL/9TXpuwdUcJbpfjg/7hKkSvS5xdJ+Ga/Xw5sU9S+XKhMuFd6BrFm+LG0Ba8Z9cnzbhr5gyo+kLRof6Ko2lVmQtEkMUEENCCAmlAoc8OR90BQVLUQ8+Rps/OT01/OsLnNg71rB2GKKNnwIjMhCSpBSEFLHfHTDnjFKBUnTfl4r9fi8GyyFgGCq2m0ukYUSMCeCCyhkhiSJGyo8ExZvnm3VzG+wtRLIbWq8jeJpGiLLns3n4dUJSOMfeZ6HVJ3Cjw4QmbXT463KVevoNX2prYeCyTJ6dfxGAYTD8DVKZmhm1KM9Wg32PYroCMKUWRWHQFTuxPbRcNVkPoClFrLRJ8p5N6AI81ULnYptCeKDlzsNlDRg9szEaPTO+a8Z4boad5imDGpw6+dEJXoDzaYJ7gA2nm/LW5MNFuhCMS8gz7kYJ1A2UXAFnXVvDd3u2cvPBavYf2BfjlPfdsERCiWFRXrpOTmRsD9LqSVYVeoXcplb7OLu9amu5IHpsI26wMDmHCRIRY6kFynDY4Ws0rryGun59fBWOL1XgZnkY1lNnSfdt7YzOaRG1nEa9wTnKv9MazLaZ5Rlj/2SI5+ZzVkGI28ZkhjxtLQyOWDX6ktCh0/+cUvV/roiqwYJadZwjocaebeNZCnQOCrc0uT1sCUvSr6rVrM8FpY7w6azaWgDN4rTmfDpubjt44Rk5skz5rJsQ4GXeKWpFU8ACUaonQ7fAnlahVtcvkM/WGaj14+ng5z8c1D2+OBVenHehkDQkxODlwhPB+jAaEYb+1nsiTCztVs9xkf2JVQ6Or0UTVnvGsz6a4qhKNKl4prnCZ6MUyylaoGIcBy2CwojaScGrvVEQr0KKyDmjr4RbiqzRrM6pkLp5kY3YOKKM3NfFUZ698IqdMpyuKrmaxdEKarR8B32V/hJjaGHaBBwKYxMTnjy+xNtPL/HsF3twFYNQ0JBgUS7kq8yEUivmnHF5eYk4Rod/BTBnYNgEnA+Ms5TxfFZ6oAyQ0bz/lZuxxekb7DsNJFaAxPa03W68GhCctxDqnCcMA/DO24/x/rMdYim4ubnH7X3Gg8szDFGKUVCUXnrTXHGxJQxDQC5V5xqc8cr7l3u1R7sV2QKBPNS2ZC+12yjEcmP6myoAYkbEYjeqcAbkzNgdCg6HgpIHVDUy5Co9YpNJLEZbunXJrKGvwYpbUQubdaSihpeEXj7pxnJ8boGUBhT9xQQyJ/XcCWl2zuk89w/r93jxe/+J9VBMgfU39YTf3tW9w5WVbgsaYjUFsvOaOUE0gb4jOt37xXCzBhLBvHhSvEqMOLUUlFxAsbWusndVMt7Tv4wb39Lxm9fE/hbBvbTnbC8Z7e94XBte+34KCXmepeKyC9/2v8CrVEadBZ8O0wRgQowaUZSr9lwOTvv7cSwWUH96lVUTLbjNs1UNJx9DU5T0DLcPLHH0+OM9jixudeQif4nBNNvwSMV8TfUJMTjPk69VL5pGUauus3quK3vBkTYW+dNyLEUmMF7Hq7nYMBtvaPwOWhW5ww+wtlcx4zB1cmCb5prddKBswOT+vJrwzQBg5/R3N+L52LuXnzhOhbja2vZzdWPHYoC2B/qVNieEmadI5ZjeAMSdh7p7P68A68+yk9Y17TkZ4t3B12m9PdvTDZOL2pMdPAhrptNLWyYGm5a8LO7nbMA90sc06evjq3J8iZzFE2zQlbz+fAu3E0VOrcWMrjUAiUCpm9cJy/EXxGPod0DacxhjhHh9zPIhjN88Hj1yy4OVim/4lutgczDXPuANWqmFkxqDtt8BEVx7EYMBoMt76b/DtLIsElzBlDHBC5kU5i4Ps4GVOMAcC0HDPfqeNiEYExcmECFWeQsthW5SsFdfaeCxhWQJryTqKIvPn5y4FVRvnWJhD7lOCLwUMNxTpQ7Q3rO2hFEDjFm0zfvn8nF3X6HGyIXwrnOzfBEg+TLQ3Ktj6tSYaiuCZG+z3kK1FvGsuGDHHsZEJ96zGIiv+7F12Q6G5OdJCR9pch+iMNssEUM4O38D7//lv8Wnn77Ay7s9mAPGDePy4RabywC+m1BmBnEElxFn4zmeXg2YzjaY54KX17eoIMREiBGYxgGbccB2I+OU0GLLzSMfL1duxN3G2gtP3ayb1ZlRSxbBIBDOL86lWNI0AcSYa8acGWdDxBAi9ociYwdQ6oSULpGYkG9fIU8zQthITp8Kix5KCSiQGMgFdQSKV2kCcizYhxkXlw8QNlL9tWpIX5OFuOF59Zpv65XsF0sZNbn9hVmLQTGDYqMVzBWlMHIlPHg44k/+6Lv453/1BVBe4YNf/xb/96ML3O3fxNNHZxgTgBRxeyvtYb71nTfwrW99hl8/u0GsEp4kHl2lY7ane3SznyYLqLBBkRBSxJAG3OZ7LfAkz5tMXBmgahV+BX6xp3v6f0VAzRkhBezmjA8++QIf/fYl/oM/+i6++c6Ap/OE+bYijAlDqNhQFeMES37vgzTgg+fX+ODFS6SUUFAx5xmgAVGBGVC8zUY1r+5aEdLZVsuHt4m7FGLeio7eoGt5YOunfMj9yoG0EEULrTpCgaVkdsS/1FzZCZpNQVkqEe3nIuSta2BnAmqo/fvhSthCSHd6LUcIyY00VXvqEsxTGNRrqN+vUs3WaF3QNIhokShoyqB3g7VQVS+2I4MpGqoO3wfsebeLVrOayuFwdlYvdNMUu8QzNpuIcdT+s4AaoQKuLi/EgFOBnCtSHPHy1Q3u7+4xjBEXF1tYKjR5qJNErtSq/Qap8c4YpQWFhH23JvYmZ7TFb3KJ+ZZCMBpP3T3B+coCRxQPJKqnO9fNv93bFtmo7npd7IG+svmxAiH/r3mWPKuRLfpc0N7aTEvVwRU6Nvmnk3m6LwYQ8pwdvmKgI4Cjfk2U2qKpOcyhwY7NiNfnvbaqo+yKMvn8TWHvTb7Gq40muIm7Fp+1hwn3e8dD1C1NpFsgWsrDvbd/YSzpZDjPEfa1tLQkGbvQ4tbT2kdu69iNp/swrBppE+ZMoOzQR9dzHXVh8GN00/P3ruYHo7ft/QblvvWJw1uHucScFl6PiKOxfH18tY6/Ec/ikukBba+0kDo7FgzN+IErNUrAT1hB/Hn9nt0fFsRJiRBXzbcLTfnoxgNAwy1t/C1sqZ+ThzNZSqVOrPGGJngcW3t6IcVHd/xbR5z7FjNWAECYoLw7HMFZFR/NWShVktlrlVCjGCNCJLd6auaGMGSl8MEYUTdfEIEs4d2ZTvVr9m0jiqY02jhCjF2/qnVQ1/KwtTFB6iivpVPi3RNlgnlHpE0tY6s2tiI83HlJpCGAFWPpBLiV4koEC+ptOGLzrmL180qFgHvM/H6YaIBujDAuB/bcjyWETEipKkzXOkMKfETkIvkmwxi1D2AEQgJFQsmM86srUCIMSStxgkA1YDpUcMkgDkiRAErIRYrscIhivKCAyoRaihYwYa946w20lqZ+AEs4H2kqKyE6xojNZsRmc479bof9bie3VWA/ZZxrr85aNfS1AJGkqM/5NuHiIiJQQaAqhYQA8ZKwzD8EEXhjqNgEwlQSuCYpugDJussFePPxI7z73lu4enSJD0vx/C9bHqtuqgurBW6WnmIDjf0g5c2N1ihDdYLYbp4PEWUmHO5mhFwRArArGb/8+LeofMAbVw9wfrXB959eYd7tAYp45/vfwRv/6jk4/AY5V0SCepY74cQG1st8R4PWfJ1SUdRgBqKmkNhc/HndU9QLlo0OAAIvCgHTnHE/z9gdMh5//BLjdoPvvMMINeNskzSXOwMhonJEKUCMA549u8ZvP73F9773BopW7wwhasEUo3UylpYX1dFdGx+1sdVOITFlvlYJT+sbzfeivv3sPYAmcPX9+3pF7uhYCeV+rt/nPTqcFJKWdNA9jqt3L3gr2KuQs5+Dww/MrvTlmh0WAJqREfA9JOGZARwZzAWHwwwiYNwMGFJCDBGFJazcYDMMGtIfsCi25GNVZb+d7BSLTn6w/ogSAQK00F35eahWyC0AUUJmibLyayEeEt4uOdlXl1tt/zMhpYSziwGcq1bjJsxVaJ5V+ya0fEnlPhpW6xZuWE52Pz/zXFmczzoPq08/OcaSlZLMKxzvcKGXV45UobUSYPuiGmtseX0G524IMG8hrXC0D7k8Gjnz0dmFF10BVTTEGVgqG1IJXVpqcahNHujaehEBtZhCyi0dqYV3HRlb2GlCf60fd9OnlvNU/NN76/ohE/SU7hhc2e5dwc2VomCpNE3h8tsX31YIrtmu739eKJ8uw4SwhAVBC7RBczfhMiMYK5pvkT5a7RQCV1pVg5Z3k97Dnpvtynm3KU7Rth4/12lnXyuMX93j91YWe8F4LTjYOQ91WykBgJUNVnLKXfU2eZG8oVdM5IRU1lPyaFbgBZEKhIDoHjSp6BX9PiJCcTLVKamROoVQ8ub0kwjpdyiw/fmOefQCiHub/J/euk0dzdFRrV4Soow3xdhZn5RYcQVV8UKmGDGerZbUzH4qLBkx6BUX+y5DrMmwCq79GMXG341dCtoAcEu/hTMCcGInMG/Cleib7f1BYs7AnXm5h/Ui3MEMBBbyQ7TyDAoL9gpeCzjA2bcpvYEtmBSL893Hj8bk49FCAGRNrHG6si3QNypufFtyAMiV2GU4LAEcgGQK5yAN0atYiWMM2G63KDzh/CLh7H7As8924BLx6sUdhmFAxIAhjdgddohU8eCyYrMhxEjY7/ZgaCGIOCCNg7SvGBJSjAhBKyqui2rAUJJ8Lir/OYNjFZTEoyOVM0uuiHEQJWDOyLkg0IRSq/cYJFQkFIQ6gImQU0amGTEQtkPC7d0r/NX7v8YP33uETTrHYa6IaQMrvAJiUIQ3m6614lALPv/kt3j/L36OVzcHoAakKiWTeGI8/81zXM47zKUg6jNmuHCjT7AgoeACzGJ1FwzU9hlEyFZpq9QKFNaqz4JzueyxGRO+8e238M3Hb+AXH95iHBgXlwlP37zE3/rxWwgomPASu5sCutng3R9+D//xH7+Nf/o/E252BblGxCptXxaNrA116XgbCJ6KkJhzwZwLhkHawVieUQxqBPFXktJE8UAbXoMBimKo4CK5bjECYwLG84iPPvs1Xtx8hoEO+OMfvos3330LQzlgt98hV4g3smSkcIEhBDw4P8Pbjx8DhxmbMCgND2BiTHP2htNSUIqkTSugrTaE36yFp260ME26kfml8rUQYHqBxs8HrIWcUwdb9++jC6YIrJW/Dof8tz7Mj92bhe5c7SqK2/gb/T0hpEGK4WQ2hbDzXDD7fq61StsTFiONGQAvr7ZgZhwOexwO9wAYgZLTYGbGfn8va6SKnH2jbw9llTerCq89TKkzbPaGwKJra/mqxKMonZoXW3kSr2cgBEoAAcMAMBXs9zeIMeLqckTFiHnOuL25E7hqQHXVGJ11w3VmRqa8FMYXK3SCXxntQJcL2OvH3LidoORKUGb4C8Ro01INDCDkSgYvZCaBZz8Hav/74+rVqo129+SD9D2m1C/1pFN4dawkViwVAuaMXLNU4IHyWTXqA1U8vcEUD1XIa5uLpQsFLUi3UDgUUGuFYy0/WkGbvuCS/96tq1VE798JHMPD0hGNMVZommAnq1j6iqUPUjEllFQSXFNuaGqT0QpZJ43/74WVhg/d2pfpOC7fDdehGUR7+Bn+iJKo6Vlq6GlytkPaEoo8ao6V5/mYX4MvvRHeUb3nn0cj//r4Kh1fyrPoFq4VAelzAE5dk59YSDILpLLKfk7kWm8w8Rw1C6pvFNG23MJjxVRER9IQAwaIpGwFwUvBABCF1Qg7EYE9zUmUS7j+YdTBrto8jPGcPtxrJyPReTUFymGygFEvBBA8oLZjRrKZtVG3jsMvr94DOl4PwBQXtG8tmFULWTXSYBqPCSsmHJn13t/rSNAg5oTS4+KaCuKqiAKVaI07Ng0VyIncWk/d80XD3JbP9u1Kum91rO71obBNQe2FHj3RPR86itcIe2NeNlJuXxak9FEwWwE5CXvKtaJkBki8xFJcpmKaDggx4fbuBve7O2y3W6QwAokBzghUkJKsLZeKPM+Y5hlTFuXQiozkPCtuB0xTBWLCsNU9bUUQaodwViCkVchv0HcrPHwuIuha1caueim14gDMEPchSBRLFmuxlM5JmKeMN958A9/67lu4uCLsXmQA0ldUkFXYF0FL96vgl+KAy6dvYrj8BClKS4bMwExASAGb8y0251tYtpoYUUxEl7kaU+fFTBeItWBy/jyvRaiW51krkKcMDoxNAi4fRNAwoDDjo2fXqDXiT3/8En/wg6eYbjcYIxBTws2rl/jF+5/isGeETFoVWDzCgkdN4DcUPBJEXF9pnnmuVt3SHmOzmQHgRgN0bmbwkW+LpyBoH0ewVm8mgCniblfx/q9eArzF1aM3MFJBGkQwDKJtopaCu7sZu51UXJ6m1g/Ww7Ioeqix5OGyfqczbK3m2J3x39fhYMvnTgsudr73LL720HUn5tXpjgasBKXXflf/ZWaUTgFY09k2vvYNwzW5n1SYa2F8S2GNO1wxr5LCulZv1ZOzVpEOEeMmtGcY3tdtGBJqZW3XgK6Xn7MN92ZwZVUIGu2zcMClxKlzBjq86MIRAyGihdZmK1+r6RAxJJSiTeu1MFCKo96vPMHhuayqTNSvBDoagUYSu955ZiiTnq+S9tHD22DuW1QQ2g0A7cWWRrM02BkM7A3mxZJLayXRaHBTcjQYuget4sYS5L0eQieuy58NLsuvNnnHisYRpIJ06Yw1AEkoPVSGKUWrbgbAqoOv8NJ45iIewORCHdPa+NJ08HWUU8vrXe9FoxGLtXIYwvf5ScA4/IxeauE1dN/R8biXnRpuo/fkGe6o93QhlnRGBlP4LYzUYbSiNf2QbQ0cMt27e7cHu9DZPW/v7ImurU9305KO1ja2Do4LYfYYlF8fX5HjS7TOOFYKlvJ1/0djQP1lY2LrkAwGA5aLtCrCUtS6XBx5lVjo7UH7EIKLN513VYjES1C5SjgOt+o1xXimEolamlJAkFA+9zwtiDZ800pVr9fBC91z+n+b1hJivgHrcgN1eXgusLlSUv384uj2Yugs4zZ+mXxZWi07CmutNsgHqlYqZQT2kFezN+mf+n1vhMiEl+KEwjxsTYjShaD+aWM87R0ytvaRU/jYH/25ShZSqiFp7ons2UJXiY20iXWTtKUHYwitdxgBZrE1vA6dtdIEht4wYMxIcLSN0/L/JEcGSEnCo3KexdIPwn6/Q4qXyJlAYcDDBxHTNIOGgFCBbWIpow4JCS4zcLfLiOmAs82AEIFxiNhPBblmBJacvVJmgM46KGvOmvWQsPAwy8VZCCsCO5c/RfpTiyYpLlkfRvEqppS0em5F2kqV2TzPKActEU8F+1wxc8HlxRmePniCnz/7AGnYCvwdtrJetUqV1XEbMQ7Av/p/foX3f/YxynzAQFI4pTLw6O0rnJ0x8nynHlSoYBN8bTwfhjuG3OMUsNobtrASycCw9bbIBgIgYXWZpEgMdnvMZeeW7KuLKzx9/AApzfjisxs8enimPdECzkfGO29uUDmhBmkdIlUtaCEILRTGE/SgeaDEM58ru4IHaK4i9Y8oZ9fiJ82bz2Au4ExIcSMCnpTQBZWCOCRwAm6mV/jki4Lz8APEMWBigLL0hqREoAgcMGNfZ2Rtq5MhYbY8y1xiEHtCLeqNItIWGQ2yvSJsE7duZfGXAAAgAElEQVQqlguVfjE3pS8dB2oenl6A7Ajxa2i8vY9Zq//y+poJSRYx0BGD/gWrDxCZUtCidJrgaYJlo3GGr/13Gw9p4YcmfZr64L0KYQxKXpaL5E+HGBV/qnti2Jq+B/E+BgoIcVlhWxCydkUshK7GEDQnwNpOtH28kAjYZ4HZ4BBMERP9Ulo9Gc1Wvm4SKQsdrQBQpd1MMA+K8RsGjOYD3BXhCp2RgBYC8VLBU/lDZYJiDeaNTvpSt3DvVlCa23q0CUO8acX3alP8urV1VOUOD5bo1Iao+KF9jR0vXC5oSm1PQ5bfOD4MhsyarcDsfNHDiSEGJcnXLhrFIe9LKWEzDjjbXgEkrcNsaKVWaJUsMGorLKdDkRZWLfTRq1coTyASDyADXiyGOrgDYhw3o1RTjPoQ9bXC2PhC/7PBnVrVT0N/cFv7zgAP6vCJbW5lER5qOpmJVm0dbLE7A1DXzqW9t+GT+gTbo/06On6tz/e42Z8zGNk+6685KJZj6L7Vftof7f6vj6/e8TeSs+iW2gX2rQmLWccszA5OLJZoZAoA44hJ6yM94lF/BzXr/WJzr8fRPywTgIsTnWUGNidePI4jjPbrFZ3d0Z9/Hf4v5s3mEWzzNS+c/d2/iY1xrb1vjCMq0ApdWUgTHXOUpivKCHprvV4/PRNuSqMThtfv+XXohPJB/SY6WJLOsvfIdudpLeLZcI6p4GtDxrxwEVbEk3yZBU3tIi1e3Q/XBC6bjwzl2EO5JKRNhO3y0Bf4TBDBizSUEVwRYkJKUQQSVY6IC2qZMWy2GIiQUoB5U0MwAYaRMyOcWbiWCi9MXfhMkKI3KiwaexFPPuD70kNQ1vuLurPcY7P2zQuINYrHoUpfTSpVlFQAMUVRCIq8q6KCKWAYIsYIoBaUSkj2dsM1auMhIqQxYtgSbm9vcXe3AzMQOGI7bjCeBzx9dAmuGYdd9nVpjKvbZ8agbY3XjJAbpjaprAnv8rMXOXRfBal2WjJwmKv0U0zB+5zd3u+BA+HifEAtFQmMcdji2998jMcPN3h5y+B5UpykI0/WAudWfzd5SKUjhifWtrkeP7Og0av3e76LCXRM0vw9RVycD7i6HPHwcgRSxIu7ezGGMKnxAdjPFbss/WURJMojePSIjYkbrNHyiHrPzmkArGhCJ0gZsVrziPWfS49Fv9YnPoc111q/uadapwe+DG3scZz9XCnVQ9/J9zL78yeF+y5BS1BA9zkB6AKajbYuWF+vPHOFNdX2+bJG7VALNXWvmO0dNprSKbvKI0PU8en8zDDVw4P8ey130IrJsO2FYEpd80R3OxNAXeagdfPr4SZ/L1eK7R3cnvHnVzDSKbSLptj78+Q82Xk80eL97bBIgNes6+IeoTmuK/dK0mKscNltLVPZs75XTtIF47/9i7u5ccuFZZYokJqlrVa1Ru76CIWIlNQI6XAy/tylCzVgtk91I1/wAr9oMkPPr5pRs1vVTuxrYb7Ldy0+d1qppob3uirtM0cPN8WxCw7zMbTvtUgQ+Za922Cta/Q76FKb4yk6voaHvvcEFWvGtN/xKSxv+Wvc+v/xxq+P/7+PL5Wz2B9EtCpAKYgYwnKjWR4jYOcJQO3CPFcbkxkUVpuuE+pBVuJGQjQDw0NSreQlc6sS5sI8CZORs1qXq7NI+49A6mlrmY7o/9XS/coFQdS/p1nw/bsut/STpEYluv0bnImTC2T2NnNaVW6CAoAuv4ZcoWqwlwH0Zb6JWv6pyVTex2wBi06b6YYt69MTWUj1R7118R5Y3pbBgfwhos56241/cXQWbbtnKUwYzGkBR5unE2HLselaZ3Qf0XvC6u+oxZeMop4QZDoYGwF3AQTclrdnRCowtWqrwiwZUlCFANSaxQsXz1FqRYyEi8stbm93OBwOOOwmhJSRvDlzwnYziEeyVmxDxFRm7KeEzcAY0ogQJMdUwsvEm0NMqIkQdzPiEJCiFLmplT2/pVaWPRWKC3ymUNnc7V/PiVI4hQCMm4hxExFCQoxmba7Y7SrmecKOCDmLZVoUVMJAI/7g+9/Ct9+7wsuXH4JpQKlBcJ0ZTAVEFRQGzKWgTBmf3824m4HndzNEpUqYK/C977yLP/87f4gfvfsA+TDh5S67V8IZPDV8Es9CJ8g1aUO3RMNtVneSZvxp8/PqOMNMCApPcAVSwrg9w/n5BoUDIgMf/fY5Pv3iGlSB/+gn38W734zIVJFLxu31hAeXl/jeOw/wi48Zz59PSIFQKIBKywF3+sYqrHbKUOz2jVXUi978i63lrSjXQc+pAC70qA/xJhANAILYMbRPFocACgnzNIPmjOmOcX0R8ezzWzx58gAJBSUAzAE1F+R5xm8+vMbn1wfkv50lX62aFzNqv8+qbXiUfnT58Fgo+irM9MqWnHSYuOSLJgwuDXENjL1i1QTH6sKZH4YLRq9PlabtcEWEw7qgcb0QZwJ+f96MfAsFpTNIrQVJcC/qLRUny7+0Hnf+DhM8FVZW14lWhTmEdAk9DtRCzPvwTaPDjedKq5equWhNlpd5xSh8jTrAl7n3puo7DKxV5piCRBOlaHhdO34UAKrwvrkkAczBFTVbXWO25pl0Yt3+NmWsdrikNM/e48KxCfadAE+AGkKW2sUxHzF+b7jQiE7jH0sc8LlCYFtrZ0xZKxAGP+VBwmts/RvedNjqP5f8sjOK6Zlgff6YECIhqYRZtZ9iiQWEPncOyFVykvf7G0D7qPbzIUA91V2RK1dy10LJkkAzSzX53hvnc6zN0F243+H9Xur/ZiyWTmmk5zLaEDQEuR8WkXEFKapHMMMEAFb8crrVy20nTeILmcNy6l3/7A0WbCtp91s0V3+9ozHd/KXnssJsIdv7WRwNDDjqL7vE1fbdXmb7nTru18dX4vgb67N4FKLlCBM7ARsQgrvEJtbcAkJA10lY30OuLDpitauqaBCIhfybIrIu2kggyXkJ7AV1SDeoWyOdMcB3TaCIgCr5NT4GeXlTuiQ0xAjnkZLTbQqb0zGccHTNcnJAWGwuIhJvE4RxBXTFVRgd0YHpczI+fU9fIIaIEEpTmmXOYRFC2Y9t7Vlhvd9ySIypEyQRnSCwrVp2m6wv1yo7fA2HEMhh2VunXxdu2kKE0aRdP8xKaX+aAgdXqOX91XGnL2MAQPtGEriYj4HBXI/g1IwhzQu5wAn9vSqzkrEY0xNwSIinXK+VEJMMKsYtpEdWRgjSL8ws5cMYwTGgkAhNIQmzZkRUJsyHGWeD9B7c3e/lHkhYzFw0LJsIdWagTBhywjgkUDSBvIV2sa3Xmit08Friei9uCIMu2kIljQmxSuhRqRk3txMqMoDszC8y4dHVOc7PRxxe3KByReGMQXvF5VzBnDFuRtze3eHu7g7vf/Bb/OzZLV59eodnL26QMSNH4NHTS/zpH/8Ib19k5PtPcZgOOBvPmlLfMVcGdA2qKk+n8pmWQj3BLNikinYr4mNHrQWZdigYcHFOePpowA6MS4oYKYC44jBPYA5479tv48UXL3D76gb3r/a4vd/hdl8w52oythgTui/04sV6iSTlm72wq9Gsqh4+ozm+z6nhL3Xr6VuvBkDDgWVPFxQUMALiEIC54Je/+hTv/+Y53n10hb/zJz/AN94apRfrVBESIQXG9e09Xt3sUeestkMCtEG1rEORYmV1aQDr95Stga8HjG4o/edG4xbeI7Q5sp0zmt0pAb13oyvA6OegRiFrxdG0mtOHRHm0hTsl3EHHVkrR9jXyt7SwWHnvFkLx+gguWDYjTweLVUsCyWESvlBLXeAEc/MYhcZqVmOR3p2eD09NQRCjQ0CB7K85F815ZAxDREoBKSWht0FazZRilE4FzQoUzW+UqqwFAxIY1QshxTCoME4IQYreVGYEZkQKGuav4e6Q9lHUW7w7fLE+scZnbOsvo586POx6yTpcjjH35NHg3HhIv66Ld/brbSjn3ROW+ZK999Dwy3gZdB9IkRbhg32LkP75U3utG7zikyowUfZrjBGJAE7SNqRJa8IL57ngfn9AKRUHzkp34XiRYgQFKb4l+7WT6VZQbfvXvLsWXh1c/mGGdtvu6Dea6GTFcPr31drPl3UfmVLeRR0RWTmK1fqY904VMSZYUUS5zWTfANawbfOCvw5zjF65sreo8ryU1Uwy83Gy/9Pd1vBcuHU9eV97wXEEwuIONmMy+zbpDcgi6zSP+F9rg3x9/Hs5vpSyuFaKFoqOIa3m4hgSWHjE4mAhjGyWbDQiLUVDmkcxIEjhjWDEWJ4vVgY8+Aul5QAAqZhI0gehdB/tkDcEaxbeFTMpVcLiTAnEkvhbhIqNQwxE7fxyjo3Q0msUMfuubf6U2t8C077apirgJsg5U9H3oRFUV4iUuYYQFww+xlWVLFTkNaHDcn2FCNvcxasbA3XCqfUbKYAKeLx4mp0w2XgXCkYpILUiQ4llCCQW7O5Nrqxxl7NllUo7ZVP6+6kg5PIAoyAbtNr8GN5rrnFKTaN1XLb8V7QkfJW2G5wsr6cRRye6yhdM4bBnpMIfO5GVqrwRTBWVdkAMmDMwX0+IYcQ4EOYBACdplYKCXc54++oh5sfAJ88+wK4wtoi42x9wyBMYhEcPrzAzYS4ZtUhT5CkDNSZwBcZckVGwrQEhCvOyATMCUMQYEENCYPGWmfBmClNlAqp42EopiCyehVIlb5GZUbIoYilFpO2ITanIGSgZACIobPHf/nf/Fb731kNcf/Yc17fXGOIZqFQcMqNSBmIE0QbXdzPoySNgc4GX77/Ay5cz7ss9QDNyjnj89DH+9I++hfcezdi/eol5R4h0gcM0rSydxYWG3jNeayuJxcwLj2NPF4qer1WCrcwrY3gs3twz7HLE47cD/uE/+nP8b//Hr8EVmMsErsBf/OxDvP+rj/HFzSt8/9038N7Th+DhgPOHF/jv/5v/Ev/4n/1r/I//0z/H7pBxARIsDp2wDlW0fcBKjxOaF1JpFmv0RNTnGFpvSAUriioSmHEpSG6nTF769EVKamGXKslcqngDNgHbYUCqhP/1f/83+NlPf4Pv/uhH+MZbV/jD7zzAdhvwappx2N3i4WbAO994jM93e4SwAXPFnA9C40KSRuqoHsJmhZMM79b934zO2JwEN0Wtbv1Tm1dA1jw4bbSq01zNQNLm7spSlOanRXPOQhCPdx95c0q4Fzok1MfohOGa8DqXNsGcVdFp9AkdLoItGkH5pZdebEyBucfddqnYCXayJjzDeVvD96Y06I2QfFfxFnY8KkhofBOkbQG4fRMiI4QYMGpeX84Z8ywtOpj3IJIw9RgjYgiopWKeZ1VS2b1V9ywGLxtjDAkxBGxGMdTknAHlndY39ubuHsOYcHVxiRQDSpEQaCuE495jsiiQDg4w2tzW1ZQOw5UFCqIJyKXqOqvyKEssz+bKHZxNAV3yYK610RNfe7tHvadm+GaRt9ZHZe7WZWn1IFOgqMkatleK9qCsXn24eat7oa7xSKswC+R5Ri4FHrrMcJxlrqilYsozamFERGzGAVdProRnQ9awlNJ6RnovysbfuwGgz72jVfuS0u2dytmVFJOZHNbc3iBrpPUwOtNRBR8ZEeWqB5Xah3TjoW00/Ujtnqslq2zZFETHrX4Nlb70MiMYyLpGDGltIY5X8rVlZq/H0cuTLbJA7vT3nwCM7A2Fs1hUEECt/60vg8gLXYKOyj2Cc0I3aofvWKP718dX6Pi9lUVDztdZAiprYjEBphh0T6/uJlds2laEJqVLbgRW1xfj6H5vm3ZtGToeI3X/Vg1JaMUbxDOjRbx9UEvPGvk57mhDb4VfW+GO8gVPjek115fKW1OymNGUNCM8RBKSCw0FRiNI4NZfiyFCiwmx9qwx95400mrBufuewNDf2IHIQlJVgDbjQXe9rQ+jeT7Jw+qEabP2xGrvkqlYUZm2nqR6qvTvVO8ES4899z6v4OgLTA0y/TI4nJmbV5TNumhrsQ4P0rEtUGZJFGXtWuhNXyHXiGkkUzCkD1UMESkmECouzs8ABu6nDAK0fxojBGAYhenud3NbCyLsDwe8ellVYJUcEgZAQYSmFCIqMbgwqkZxtXYqbfymMDGzC8+8XtvAqDW7cAEAVAkpJu9zWXLBbjfhcJCqrQwhHsyMXDJuXhTsLzKuUBEqgwYpbIBQgVJRckGpjGk64Je/+A0+eXGDv/zFp/j083uEPGOqBYWBcdzgG28+xptvnOGT/Usc7ifEMMA1KwhDtUqxbnjpBObj0Od2zfHQcbo3MHWhR5WRIkAo2N0x9jXhbNxgd5jEewZCKYQhBfz4b30T777xCE8enuP+7gtQYdzd3uL27g6VpeKthLXa15eYbSKncmedU7cHWGkfM8whEUg9+2h4bjf3NHYhWK1pugsShBSBlBjTXLF9MOJbP3iEh2dnqIUxH2acb7c4v7pEzqKQp0jIuudTiiL4lG56ntd2eh0M7ioerUSttSeig5DyjTVPMeHO8LyPdqhFBE/rWbjOIe/H2fNMEbaCf71RHfM46JhJhN3mEbUxY7EuCvLTwtZKYOvXLZjS4gSp0XUGe1GQJc9d86dGrxw3umdM6fKnjCdwn9Mm6xZjkogJnVspGaVk9XIRQohiNERGqEHrJQdsWQrkZGv7oVV1ocIxRVHylRKiFIAPFTfYI4agRmLqDJS0aHnEWMoXggsyS+vp7LuA4cqLGVUab4TXXViHL5qX0vboqYO5SnQLH3u+XudVXp6l1feW9GqR0+oEcPW3/OGcft1P07/U8bRACUOSfrj2bFUnQC0Flar3/rMP3t/fL2bRaKvSpV7e0lSjJpOIwujrdiSDroGka2eKzAnacnKO3dnlGjTpkrv3YbVGjKY42f30/7L3Hs2SJEma2Kdm7hGPZWZlkayunmk2PTO9wxaLhQhEVkAOAAQHiOwB/xA/AEeI4AAcgAvIDjCzsovh09O0sngleSwi3MwUByWm5hGvalAjkK5Deknli/BwYkRN9VNiqt7b8a2n5nfwLhPgjkA64shH9x/xuVXLHr7WxnR99Yj1jrzaNFzsx5oHvjm+ncc/SVmM4eIBInYQShTWxhhut9aHiMydHQUNB6vPP46QxlCL3raRWCN5GoCz/XCdAbfGyLkv4g4qzGoCZy5RWyRSDyj10LUevnXsVYxj0N/lTXywj/YuByGrPluexBYU2TVYZDC40nAfglLgYoD6fqahHUHQGEgY2gZTluway1hHrhja3JiVqqkXa82nK9oRHaiO6WctzBWcdE+CeVoY0DqQhBV4iYNneE494N072dsHoHsvnVmTcz0+MXFO0ytjg2JitFBDKek6GQAiC1AQNK9W8pRwfnGGyoy7/TVAYqGt7vUgbDYT0m6B5QDhJlbeu1pwfi5109AkLEb+ivLQKovCyAJek4sxXd/WVw7jG/uJPn+y3yx7Xyh1LzpBwm0O+wWHvXjzk1SbB0OzFFeSD1S11JRyicSg2mTfG8u4vfzsczz/8At8+PEr3NxUnCMDk7QuZcL5ZoPzLSGlBuYFgHrJIt9Yge6BpwQh+YDs06mlgQTMGGafk4bm7+8a7g+9ZimBgQa0AvCc8N67T/De+0/w9pMLvHqxAPs9Pv38Je73CxqzZAxNOj6rtkc+Yuu+MZAZvpBZfyRgCFk1iz7bvdS9oiP/IX/H8B3w7NBZgfihAi0nXF5O2M4ZrRQgVWRKmDcbNKrivU2BipxfytrrbYyRCtaGEzXGnI91ns+rK2IH1v2LVzkPWF/nf1iNjl1+DW3g8XsElHYwVixCBiEgxnhxJLATwoIjwx44s9MJ9YkbdM/Y1/G1gWuaEkjxvmP5a141Oyf7sjXUjrsXNmfxSALQEgpS2qe2JlsjKGGeZ6ScwWhqDCRkypZUFaU27OngSqgXnW82P1LqhZHQGrDbLyAiTJnECDeJ5OyKL9Q4aLXyOPSHvc/uodZ11g0E43f95m0bRxnhnuPD20OdzsKvvRj8MCGRp1mI4an3HQONwcihF/KaWGnsX+QH3VAjNO/1rh0XkOO7RAQr4togRoLDcvCxEJGQXI6YcbljLt33H+gsfo88LAzNChcdj4Ivobg4Yj8xzqB5jSNfGcWJfeMu18P7aLgmtMPPrLBGxB9AT5S3FlLeH7t2Nbfr90VDgg4Er36zz9SZydGjBsPHQ3Q9dv7N8S09vnmCG0QwomDM7Aouf3qaZ2NIFhYx7jcDbMkNy0SVlpwlfpCMwRIglZU6X3TrfRBg44LpYROusIa6RNM0ixtdw0SWQ8Hd/R0uLy4xz5Li2xbmEMbJLF6XLPkZOy+S3vRwJCmD0ZWldLQ2bCGL85G0EC+cN5w6mHuCmvUh8oFGhtAYlUgYp/82zkkKi9pj/9f1BbG+pn9Orojp89nqZHZGOfZZ2unKkgo9onifgRMT3mEAEIA4A3BvnGb2i/fDEnd0xuxhvEFZj0YOuc9q8TVQg9SFciNAB969HEdvZ+yx98G9E2M/QndB6OFy8h4RlrVWHA73ePT4EvM2Y7PMABGKhl3Xxtjt7lGXPc63E842Ew67grIALRHO5q2Ez0GeCRZlo1HDvJGaZAQJl97ovgobLwOXBO2frqWEJIomApCSjoImCY2rLnn7PpX73YJlWSTEiCTTK0BAYdCUcfX4Av/899/D248YX37cUDhj0xjMM0pbkCYJO0tIuDzf4jsfPMPLfcL2+YJS7oGlgJCRCdjv7vHxrz7Cp+80LLs9KGVN9W51Gvs8dvoKfyhS7mnZN1rfLUV9UH70c0IFcUZtBLp/gXkGpjKhlgVEDbUW7O4T/tf/+S/wH/+LH+LyD76HP/yjnwD1Hjf/x99hymdAI7HOk5bRsNc6kAi4UQ8JK1KznDY16X5V4+OUpVQFQcA3Kf158q4AsDo+CODDlgTJ91oBbsDF2QV++Ytb/OWfP8d3nz3C7/7gCbZnW+z3Dbf3d0BKSAzs9/doDaAsdGlgz/YNRwPV0ZgD4unQpD0d51r7KtZHioJkpSD1n1jKx4RfnH9S5z8ArxJqdN3Xwq/saKqkuzfjJGAfwXyXazzyQXRebrxap3rVfwxjFyWLJTRiKOjkFJ7e5eaqhT4Wsq+r7320xvpWAG1XL0nTlIbI+UIiOddY4kDmaYvZrgWj1AWoBYReyqpofdFpIpxtJpyfaTIUiHfwsIixrHHD/W6PlBiz7mdsTbafLLWicEPRmsyDMmJbHzQU0fh78rwkPcLErgtwHBHcd+FCShgr2a5zGw0RozLSP3D8xWgk5IcYjVwdv6xlePSGmSKMNXkMOgUd/Y2iXVmqXs6RiD3xC4dH+zowembJw7DZzN7WWpvIkL4/xInbEnX5GA0jKg2Lxjrz8rKtSXC4FkefO70e4xbARWAfu/DELvwJ4w/estVJO2tzIdtfWAXwsL8P8CRw/fbojCDdDWSGjODQCPzC5qMf/Zyt0bVXGbAtRx3HRH40KInAg+ddeWU+cfWb49t0/JPqLHZhuWI+EFDJDBewtFoPa4blG4qpL86+HU0BqFnOlPl2b0YMmQpcbgARHeZG5ckEzlIWpKSJXUgSb1zmK8zTpJa84vfF7KHSzqSxnqIcRzhiBl8K4YXSiraSEjYmUdGyfiAkXTlmXP07DWMr69LCS3zg/b3rOTCGSlbeJACKfin1x5zykgZ6sD1EMRx3DOPr7ahrLmqqSQRi1MfIBbCOMYXP1sAh9GPoqzFcdrnYaWglaDtX1jGx2n4wO4EXXhZA2LO9HivYfUzXTRrH1whfkjXMeUapTVPlq1cuZbx+/RqJtkDKONtucDhoIggW4DdPWRJG5ISaRZmjJHmDKWfJQlcqSm3I2TIVhvpl3LPE2X5Dxw4KjKyPbEJtJVhM4MjYZMmIVyVstBbZg7QcCorOBeeGxJN6I4Gz7RYzGqg13RNBqJjFM1j2aFobsZQDPn35An/59x/i55++Qm07zJmwXwjb3ECNcXl+hu+8/zYuL7e4vdmgtQUpT2i8B0M9bqHdFu0gtGz/dJqNx+Bts/v9hEleiAeXAWACM+HsfMIH77+P3/vxM/z1z15gd3+PiYBCDa3t8T/+b/8eL29u8Hp/wO/8F38MpjO8/1tbvPV0RsoSYpc1fGstbk/RVw/l7yFzzNRpGpL8Y0pKLy2EchqwYvPQAMx934uxZwJ7fVaGJNPJOWG7mTFvtnj//cd48uQcpRH2C2OzzZimc9TS0FqVhFmWKMqBeOBrptSuxp5cLpAa/YZZOOKZdlhtuFPzGf81nnHquv7uY9owivBnNh8l/WkNstb3xvs7Txva4jyzP2d1ql9Kx2MSQ2tH4n2oXdJs82adsCEON9l+T8D2WzFqPa6vOM+z7L1OecxSHNrLrK53UroVIYO06PCoUWGaxPBk+4YTZczzDGZGacqHSlHlIWGihClvVXdn/a1npfZ3G8QPyrTLJpK1mAhdYTK6Mc+WCgg6Eb55NPS+5Po8yWldlUcTY8C7q5Km5A/KIp2415qjoaKDt9pkJffbrI6hYxXDD+DeCRV0aoJA9n2e0NrFgJU+QTOjQ9/D61t6sgMgMJHuMe2Gid6m4zG1C8ZcER3HdKxlmMnO9Wc9FHHgl2Pkvx170NCcqJiv71l1BJbptUfu9CinjpF13Yb98/7clXIrc88A6+Ycp7/jxRujhY6HkyLLcrI55qHH9/Xfxuz3/f5TjOTN8W04vrGyWGs9ygQZaxeJ0ARqKX5uUIUGiw78nFknAd2XBA3lbOxZzgZhbJ7KWChsfQ1OCzMzFFtoily0ACT13igRDmWPRBKiInsa4EJusKYO47FiWC5M1QPI3WM0tK8jOn/+wKQ846kxEIRU4/IiX3oBPCqbAbc49mGvE9meE8uE1hzsdYWuZwglRUXtoXV9gqlauGnO2fsj1q3mRoLetr5p3YSHA7V2HIoq8m3smwN94Y3DHYaHPJSHpd/xuWsPuIHPOG1Cp0kz6QkNprwCXQCk3ADp/DYGdHwAACAASURBVJgAsHTlPTOt9IVhiQpkF1nDfpFGNxaPd84J83bC848/QykS3pOJsezucHtX0JBxdn6ORhnb6RptWVC4iYcHQFkqXt3eSxmFpF6kIiU2mCrODFAtBTsCpikhzVLqAprEqdWmay+hh5ZTF+BGz62DE6Ks94qw4NZQFsZyaOAFmDChoCChoM0AZaCWhquLM2Teo+6BDWWUekAiYMqXkviAAdCMz2/u8Wf/sMMnn+1BpaG1grzJ2JeGeTrDs6dXeHQGfPThp9jtq4KSCiKJUoADmV5rztBahzsdPJw+7B7A5HX0WNr81yZ1x3ICdvcTtnRAwgGlCrNoJIXE+XaHd997Gz/6yY+Rz94G0PDBH2Y8+9NPsaF/g32tYO4JeWKzXPQqjRNBQo1VgfOWNkaFhJjlJAlpcm6opYlKQ0LXSbEwEBNL6egwKf9vul/a6rkCYAK1hKXucHt9j1/+4iO0D97Fk8tn2C0LXn34Cvd3B1xcXiDnjKUcpFojk4SlEiCVNdWbYkCD5L0Ohgka2tYB2Zrvd7kTQSANv68NRQ4a1fN2EvxFcXbKkMAB9BkiJfPur98Vb27j81YAUN7H6kE4oRSs6DXnDCubccpTIizWjFxw4M4sYel9DKXTqvccKTOxPz6mSqRZoxqmvBnkdK0ShlxrxW63Q2sNm81GFMecQEiYsmRAxiRzDRaM0UpBwgQmYCHCoTXU3R5okknTsmvbnsc8aWi9tu1QqpTwubvtoasKzrOt50Sg3BULy+gsJ/o4Gc5h5tHrZKN2UrM+nvto94a/kzt/feAQau1yNIYSypYbDPPR6XlF765wkDcmluPwZ1OXdye9/Y4xOlghkiy2ZmBpzChF99tbEhunUfa4iehMJ91CQLkjH28jW1tH5cwaZO2z8lEur4Zmhy/6k2eaDU/2mnGhz82vswQuMXP+qfWuVBMwn3sEYVirz2xMoBW99mrFCH3tCic87Ff5chovHd697jszzJy+PtjXf5x7Ct+ND8Q2pXD/ilbeHN/K45+2Z9EFkh6BqZkiJdFA/ZqhCoYpjnqN/U1JQ0wtO2oiASA4VgLHl/d3x7/rzwYiLE22h+RRb3cXtOpzsvaFNlg2qWZKzIn3xqxc3k4aLTOwMXKMGRWLU111CfTwb+7B6Ielwu5WP2/OAJJOKtudF/h1fRP/saIWr3MPQ3wHjIbGtkAFrAsshiQyMcF3ekQCMoa3bVRKV5/De7tSHQHPwwK9W3e1vehjbha+OA56mf7tFrVhPGLGdp+fJmLS9pqkjKx7YWttOL+8xM31gtYYF+czrs7fwn55idokLf08ZVycneHRo4LbL677O9U8TDmDIN5I0eskeUKbJhl2sGRXK4w6JVCzcEX4WulHDFDtnXYhwaIgaZlGtFaxXwr2hwVFs7MhNQl9UwPMxXaL3/3Bd7GdFxzudyiFRWFVDFNTAaWGzz67xvPPrvGzD7/Ezd3eYJLXSK0MPL64wJOrC8wZ2O2KhsQyxHtr+wVHsG08R+bkFJhfLST9HCMe4uRz+IdIIgtaJaQpI6UZiRKYyI0wRAnYEn79yQv8xV//Cv/Z65dIzPjikxd49WKHwkDLjMwxvL9jwEh38RjhCZz3MljqeU4Z85TB9YBMhEYhWMroWFIhh/dEgR+yEypba7VioYZDrfjosy8xTxk//sH7ePToEZb9gs0Z4/xCthzc7xYA27H9/vI+1r7ny9Ykd55kM0FBgRrb2ZWjcb9f50k+TpGfxUGIYxqUxlhvsN/fZYl5CU4pa6cn7CsQFCmd0epk/C2OGTM87Jr7o4P+50oxm8EttmPFw/0eXtMAfBWAle/qI44SogQsYMokEaGy77qXmpuk3lgG6kGjFQCd44QGyURcubq8EeGWPPSwBQWPkkWJ6NYQMPKUbIOLjglpJkvJotlqQ19gcf5E/nsWSaVH1j65hGGzt4Uw6kBz8Qgk5TyPIaGcdAQghqHX+yPNn9QO+l9XHNnnY2wSD59YG8bo/PL4naEdmpSmoXuKkxmG9ZqmSmImgFJSz7C8UWQE+/9dn9WIlBM4y/QmDsrhGoPEKKwRixx7Er1vK74+KI/rZTy44Awr9Arfw42mrA0yROeOj3yQABAMG6Ht1gX95FDKlHslzNFgJEazh41WUbLZ99jXGPK/agHrHlvbB4/jsUVQNt8c387jnxyG+pBCZnH9RlH+20APffGlIJCH8EZSD5BBmyN6igoKueAahVFnfIQIpCKYV+HRGAwJy8s56T6Z3AFt6FtcbERSCyuG2xy3T18+YA4bg9i17jk83dvArIMgAqBbI8dzfU/IiYdxZ/h+2kLQ0JneceqIeIzM9xSTNUAT91cy9wQ3DtdN+V51OCqKp7062hENI45hGT5PDp7CXdRAOFYWbHA647NERZ2GxhkKYMzOuDXteOylXav9pPraSCvECTVmmWVCqQVtqdhuL3F/d4dSDqi14upii3neoO4rpkTYbiacbzM2s0ZJO/joAsOz9BFwOBTkiVCnGZSASgSqIqhmqaKOhKz4y/YnNbDWYDT/m8+vMQAFCrU0lCbhtLvDAXd3e+wOkrpcvFCy1zSDsDTC47Mt/tWf/Aj3r7/Azd0dClWkNKFUqbF4c3sD1IqPP/0Sf/uLL/F3v/oU1zd3IGbk1JCYgCLK4o9+/B383u+8j7Op4ZPdHjlvMCcJrV0rgpG+LDyaLXHJwM/69QQe1thgVKCuJJigdCCzFNyi4Sc//g4+/nyP55/caC05Cd3ExYT9/ga3n30C7O7RzhJ+9rc/x0cffo5WgJkyKqrSJDmNHtOb8q2U0bhKvTl0PSnylZxEaZP6esFDOPDUMM++R5CdpbVWAQsnzLKyDksFU8MvP3mNVoHf+d7bqHiKX/36JW5e7XCx2WKpksaKW4/cYEdywpuFXFd8AvAafGChT81wcYJvRE5rK73pEqTQTdY1KPecUu6S8d+V1uXqNUc+YW9T3tJsAnTsfH0iyIljXrfWK6OctaFigid18YspzrKcs9c3w6RxnFb9PdV/KwbuivQJI2Xo4fBMQPcMGm/WBUZEoETIWJd00iQnCp5LLViaJvSSTa7WInlrA5hTKKlCnvyLPTJC5wLV92A5r9XDsIcZ1+RdFPaGEizm1OihqRLjWCNIZN8Dp/TAqyEbsf+xgTTi6pMGh8CX+kEIbs4j5Yn9fBtpIDy785dxLbTuRjtxdLkoik1zBe6gYailFDB3PJkTawiyYkEy5b1nq7dtI42rzGmtiEr3MC46ZmaAjkbqof8Y5fE4BjZJNH4FjnAkAJUpAXdp8jjjMfY+oKtgg8LL7O8wz2005tslVh/VDCPug43tc3qzNRinR9aSGd4CKxnY4KiQUuj76DBaK6B+rnWcsF5f4/HQ+TfHb/r45nUWv84IYDJ3DeyjB82F47jIzUrnQD1wxzXrNOOhA3zuxHh6Q35UakeFIHFgfLboG9A09M4BghYa5qoLjHo46hgGCRd+62EbvFs2GMkGTn5oaL60RInqo8C24NgETmckERIYa5VRNotyV/JNvsf2+bBjNc1HU9mFnV1g8xABawTZUUjF7z42cbyGZ0TwdILhaWsJjL7nIrzDxgkrb7g+072fWLeL+yvCSztTPA51i/dbM0aaMwETb7TJNfoBwAlMIQRSS020WtG4alITacdhWbDbS3uaZjeVddVQloKcScM/rc09GUU2z1JrIKslxQRKrMBH7mvK9G0fMoN7qI8JthUdOg8IikWtFaXUAeybzmqh4ZQyNpsN3n60xe3tDvf7g+45Ua8VZZR9AVcpUF9s+HjRqe4AsYHwzjtXePfpOYjFW4okKfe/zgwyhiV+xTyvZjP+7tZcP6fzT4TaGPf7PX70/bfxVz/9HOmnJEll2HA342xOeHIxA2lGnRNevXqF+/tbJAYyEoqHQX9lT1wpXOt94NA6XSvMotgTIPuFhnWA7rWx+ceo3LjSFu4BCJkSlgVYasO+FFzfLbjbAbwQeBFaJZDTQzfsse7PGQHPqfE1Fuq84OT67H0yg4F3ZbxwAGfUGyR/jD4GfqSciPt14YGrz/3ZqjOZCjFcvhYVUb51ViVtNfAbDY5moOiKaJgnBLEZzsmlMSyuN6YrP21186nuju+iMJ5R6WFmuJAjCxuN4Wq9T5QIkb+LHBMlY0rJQ6ubKXJKr1YMvpmsNWVGvYeNu0HHDC8WQZSThPA6xzDlSQk1rgn33vQuroZGpU2QjeGxw7kTbOdImQPYt5mErEYnH3JK8W8P/GZtOeU5FHKTRbOe295rU/QkmsLDmkWyoIasrrbtpbJEztTWZZVst0mYNJGglfex3824YiUjRF5G3jPSsszNqCi70WOFL9ZDfdKA4/90TBmvI1uvDm1GhevoWWEI13Nyqk3e/oBhsOY/BKzXdlQO9eSIx3szjto3yMWv0QdcSvJ4ZjQ8fJ1S8eb4TR7fPAy1wdNFm1XwKERUV5DwflKGChcuHK4B6wbkwJxcWAyWOASpGpU9YUidaXVlpgur2LDOzMTrJT96kdMGHPYL5nkTGGNQsnwBsz8qhf0dA3MlwDbJH4Xtxo9R6Hs/A1h1DwVgILdhZdmBZS/UsScNUSOGZQJ0rzBGBckbUkMafx/LzpBd+LE8tytY+gizLjto6+MbrZPmRbSp9BDOgfl3Bi7D2EGCXQr71V7rQjwCqfF6a4v/1XvX3kIKyQ3suaYgdRypXqMWPI9BGMTPxlkp9E9Oh4Y1WxY9eYy3g2QPIbMUKm5VsmfO8wa1VeQpY5qB/aGgLAcQGq4uz/DivqKiohYraA6kLJ501G4VV+gkQt0SODB6ZRi3nCu9U7NBl/C1pgkldMgbM2qRInnLwlhKxW5/QCkCMlNOkumOCdSA1DaoKMgXF7h8/DYev3WF/fIKpQEZE9BkX13lgrPtFiURPrn+BH/z04+w21UBisygQiBqaHMG1Yx3H13i8cUZ9ktF5YwJDIYWQA7032l4XOfdY3cs0I6s7uH8yb8QxUfWWkZdJrz/g6f4oz9+hf/zb36NTz+7xXlKKKXi9uUtfv7Ll7jYvsDHz3c4xzX+7P/+9/jFh89RSLLfppY0j8IpWCmvSRAaatVC9DpUNjpOgHh2E7T+Zi88juFa7StXWPHulPTZxqsbg6Yeat1aEa80L2ioaCnh6ul38N3v/zaunlzj5WHB5TYjEaEcClKeRWHVbKzm2bf1Z2tsAEAk6zWCQUVQ3gMBh3ZO+zHW7D4xv/0DD+d5AGPGc8yjFw0J2jz9G/m1tVUOM6CM9Q1P+Zes7ycabOs4jRdEsNwVGPkvhjVG6DYqwo50Q/ss4QsN9/aDB7pHeGZ8LPlckMs8yVQqvLYpPdZaJBQ0S7BoooSLs3PMeUblBdFwKOPTUIslQ2mO5/OU0FrV5yfkKYts8/Bpjj0AWL3lTOghp32kzMgdZRunDuCHeVI+4gYFyDR5MlAEObUyeHdFNmRV16zUnax6XU57xvD6qEt4gzp/s9myIHxeGdRMBxlw0dA2IO4rdnznkEv6Pk0aeq8RXaVIGGrlpnkgTIYAy9LArYLbXjFF8xJMpJEQglEs4RbL+NtUWiQEh+0f2hGrMYuwnh0/jJOmxgT4NRiu6nPVaSOiNdbspUFBdR6lZ83YEOZYLg35G0Cwfeq9XZCaw6SZzI3HaXtjK8VIPGKhrhzq2jQ6ttKXw77u4zHx3AQrOvH5Ju2n41mtSzqw0NU6eXN8q45vrCwm3Syr6xNGOE4UDr4r+pJgdAVvxYwBJFsc1M9bTDYHogcgoZKsFnjqcM+VD/eeDeJd7g3KCwzaMoORejgsJARi2Ejs/xo40T/UfwUw9K17GA28+PK3lqwO1mL01FNrm5Jm4w2AHRSpoHdJYwKRxqdzbxdR3KthDdJ+cdIacPa7jXqCxc+zzpFl6TbPXMTS7P/InKSVMWGwlhkRpXE01hZ7JiC1OIYIU8H9uyWricq8lQcI9KWj3Y3Z6/nQwV7vHjWLMOlvXWiG+9bga+CCYo1ugVOuDRHxWotWs5BdSfyUUHkBcwERY7u9wpQLaHeP7fmZZIwDY94QtudZrLUM1CYNlzqDDUU9nJMy/LpUVDqApwkpT0JnJIkAMgGsAKK1hmzagwKNPGUpddAMmAJMjMICrHb7A5bDgv1hwaEo8HcDgfR7yoQDE1o5oCx3uFv2eDItQAMqSGq3QpJabDYzbu4O+PzVa9zf32spRq2HlljzDkhfU2sopeHQyGt1uRHKDDrradLfDMB7CXU2k0wnQAcOwapPSguDdwYhe66uNyLgFz//HF++uBHPLQt/rblhS4xPPr9Go+f46Od/g/fO9/iHn3+GL768QwIhg1HcunB8OJujcIIlzNMS9TGxesjgc5Kz1J5jBdwWys4s42Cgh6bex8hzLIkGQTy4jIylZbSpopYFn3/+Cn//979ExoIvP/sSd7cLHj+6wuV2xt01g3JdlQTqKdxdFijNeEF0VdIMhNutLayxzsMNGHVj42rinRYcRPucBTpZlS1qTVZIVAL6/qRuvGOWEGdCkJmAy6Jk5ARIOLc+58ggwP2c8LwYGmltCJ7BICsjD457Pe1Z3fPyMIIbuFy8rGvGQS6Qt3dQppspsGERUhwX8SwlzaoL1kymVRTB60NBmhLyPCGljKLlMAiMKWVsN1sAEqp6qIskl4LQTZ6S0FUTnlbL4hLeaFroS5PjRGNnawGMsygzxi+4S069DIBuBwhYSX53ld2730c1oh6VviR7gAGSUIy+yb1PAwOwEGEyWSrzb/jgeC66bG1NMpfa+8doHBuhzlzczBiUZpnbQBMW4gog1hG2eSaVq1NWfGIYBwBTFnlVZHxbrUADSmUJKT70pHzTZlJDZh9DVv7fWMKWjTFmkCcwlHYZaAlJazhGHvng9vH2dWVeTXbYYevADSWIco/RU7xKm3riMKEnQOWVy4ym61JukYR6hJ5IJ+IQpRifD8UtupffcEu8x3is9ZJaV6zXJOBjFRToyFv8WsQfzVCuq2YlcFf65pvjW3R88z2Lk1j4AWMOTcGs1bZLau2Z/Z5IGGN4yWnAbIw3paTKBgbipsCkbM9J8ueEzKQ0qmXRqxbPxb9f+TnxyXuOKyd6Q13wWJPgDCZeah4/NQrHvZsc80fB5K5ey3EU/Lw9q4d1bvv7Tx6B+ScCIEV0T1mgDE4Ma93xQR8X6SYhDY0f+wwApVaYpyCORbxutMh38EdBnQtQaNWtDvrZOkICxDoo4VFgAFaETassRvrsDyenTUn9LwzR4vOt7SPNWAKeQb88MS/iKakotQ5ri5Aw5xlvPX0Lh6Xg+vUtZlrw7jtXePHqBvd3d9gQ8NbZFXb311jqAZmAzQaoSKjLJHtsk6zFxiJ4M01AShAlWkIRUyIsZp0HgzIhp4SMjGmanT5ud3uxELOkNj/sJZlE4YJlKdjf71CrZHUVYaiWRQCJJA37bd1hThmtLLi/22F/13Dx9hnu7hfcLwfkxJiwBW02uEqEOu3xxz96ikcXM/7irz/Hxx+9Ej4EwoEXoGxwcdHwgw/O8N5VxWF3kMQJNINRAK6oHqXWFX8Bk9mVKKDXvT9SLFbgoc+5znVrw1VSP5VBnJBoAk/ARz//EPevFqBKuxo1pArU8zNsqYLqNf7mL57jV9tbtOsZ0yL13iolcKpD+Rgn78AjoHRtIcV9LXWiayBJ6pOkADqRKlrGl/Q30tvcg8JAWRbtZw8bg4KaUhq4NtTzhLbPeOcKuDyb8fiC8NZlxWe/2uN8Jrzz6Byb7Rn2hwPOpg0sZSspqCIFZEY0tlZ9T4+GEvKwRvucnGJ74lWz0Djdt7i6UPaxa3/lgyuqHfT1Y10GiBAAmIEksO9xj6UC5BbxzpuHsuLYaj8+3yIeQlKvrqENSqHJobW32/saeeSpwVjfYyIt7I/0cUfXY1zRBmHNy61d0TjY9BnrrSTW/s126xhiWRaUUrDbHcRotZmRp4ycMlqtuL/fybuJkadJM+4uqK1ou63ge8Jms3W6avpj05DIWve6X7F7D82z56WHdMGlTO5tyZZBVUG1JPtQHsga6hqiknycoVkuXU4QaiUACTmdCQ22BsYC5orWRHEwbOPr3MorxGfb96CktygjG0syGqhyQV3piJk3SZWRTidR+xDiF1GrxncONICuPMGU1saoB42YSCFrrxqSLQER50kMMWpoKIuWQakVt3cVgGx1AAhTnjCljLOLc4Ay8px7O4vwrKZ7KQeAGT5bZnaABfeSzIMZLoQPybWJCFUNB4bT1uvM6aWaMYZ1m4nNW0LKG2V14uWN+5sBYY+tyX51WQvkMhXo8z0odaz4gfp51vke1pny7qHCAcPn3w0moKOMv0eKor8/0EeYdyemN8e3+vjGyuJhv2hNwh5kV4cUyhVEzRdZLCtxZAGBMYYHXsYcBIws2Kb8o6f2jiYssbTEb1Ex7F7HU6/qQjV+NsEBQDwWFJUAY7ZjPBMp8zhSfPj4PaZw2I2JIKA69IF0LFS69LFbh8zEe6hndBTGxSGpBfl1XUmTtqWY2jhuavS4e4aHt3B85xjyYkfcbzkM0AlDwaljzWzjOaLeh/QQDZ28t49LpEu9IKBt/Y0Y4Ae0Xj9Wv9P44VQ/DHAfw1kLx7IJIzeGAJDC1QpGKCUsLWO3EFLe4mwuaKnhyWNJI7+BAAlupEmRGqYkNUHdeZ4YDLHA5imDKMuuPpaaaACQckWCJBZgajifxQBh1vEGCScSpbCDI2ZGSrofsgFVQ6ITOqAU8JNhIYeH5YAvvniFw3uXMLnCLaFSAy8Nv3y9w0cvXuOvf/Yan7+4wc393sMxcybkacYPvv82vvvBFa7Oz1AKUAsp6Ta0JKCCWT2NroDI1zbwjH8kYaHztfXasB96iBOjYkFKhEePz7BrDWdnCUTic0opodaC+fISb73zBD/8/Xew3Ge8/cEjnN/con3xSlOA0AnaiWLZAIe14/g60YHYl+RiJY9YgC8NbV97wWnEiOHJzAI5OVXkJiGuhzKDd8CnX9zg6mrCq/09NpszzBP7eyWqoz+UNTmGr8mg1MterdUYRIXlgTXnl4aBMmA38CIDtkrP4HU/x+u8yHj8KYQGWu7NrB5ufpA3BCU1EJV5LuzvKZzFsY3roYkYLd7sPPFhWj/2Mtri7YCQ6PgJQlfklx9F39iY6+ttj1eAlhr6qmPYxHhmY5OnCTNIAHeVpCeVKsAS1ZLIIiI4KCwZUr2BYApMV1jZI+wlC7tmTVev1zgmGBUowgDafWjjPY3HKeB4fTcaiHHG9p93TCHKnB3ZsQjFpaBClyCKhSVUIyJXkl1VVDzg2Z+T0pgqQaTKEdEpw0X3uo9yzK4zeg13kN0Xr5e/65JscdvxaMyAG/mmeUJqDZwzJi05ZcYrZjHy1KVIdJKGrJIaglVnDnPV+9ij0yjwb5uyVXiu8SOGhwYz4cTkB0+cZQhlkvoYBEBloy9Tv358jiRkrMqz2j9K3/IQ3SiWjp780MmOKYcTJ/rlP69CoNdRU248+/8gX98cv5njGyuLy1LU+kxeyN6kkE4/AKDUBTkTUg6vGqUVQAl5EguenwoKpoEGO8eACAUiWPkLO5LuWVrj/kE5ok6kDx2RYaytcv1dcKVRLtR/ghdp7X00F/w6W+fa+ySFZ0fGTNAQ1ZWy2L5CyQII2fqRFLTzOL6gXmPQ9qK0ZsDM2mT9N3HSw2s708cxk3Et15gsDz8hjMtXKYty/2mGwvYYfnhGo9eyPyc0+ohg+l+z3IukOdEGw7Q4Mdexjau+DN7mU/3Sd1rIIIGGcN5Fa04SAWki3O8SbveMmTY42xQJY8kZCxiXU8J+SVg4qZCryEn3AbGBDEltXooInskTyjAKMqhZIW1I1ExqADZgiPJnkQC1VjQNB5W9Rk1DU5NmZVVLNNu8d64BZAWVBaUs+OLFayz7M91/QgBnFK4o+4ZffPISP/3oNf7yH17i7uYWS12cxjNJge/f++FT/LOffBfnZxscFkarSRqPhjZVEFWAJYuizbbNuySYegjII8zxMLMDUI8AMJ6TZcMAL2BkPHp8gUKE7TZpqRgpSl5KQZ4mXDx+hO/94C18+XnD42eXmJ9vYBbpbqBYgYlwJgJTE+D+v7bHIsGZJdOknyeJ7DDPldGi83zSEC3qnsZuTBZFkdGw5YaUE+6WjENjfPHqBhefJ1zfH/DW4y2IKpaliPJECcwFHcQ3yc6qBkoHUQ92f4RXp6avL70O6CMscyMUjCeOo7kGTQa60bcadQWEAPi4y/dMGeaNWlvjKQLKsOajotiNDrFP5Od70yLaDg1GOD0oigPxjsdXYDp7Twyd7M//Sla34pUMC882QOnrSN/ft4dImB6lhHmWfdyLZliWGqoAzROge71qrWL4TSJDJ5LQ0tZaxxWB/7siZfIyiaxwL5Dyx9Y48DLj3XLzWjG2PvoIMZwubO6il9k5DOn+WjCKesK0oJj0LxUbEi8ZJD8FDEGAKX1Gh33+0NcXW+SHJRQz/tWVau+XYZpIw6zKrGay50AIR7oGmSfUxnkM248016fGha4oi4nALKGkqgP6UiwLo5WKZVnQwEickXPWcPqukD0MB1XBMt7mmbGNb7CvcW9WckI4jSOZcYR51INblLbMLGHjCKzHrkc4OPQcjHg2PccdM76/7qN97qHSnTcejYrTaJcl6zdFTnSsKMbzX4393hy/+eMbK4tnV2ceN5+1LqK7zyl58e6LfCaeBSsGTTT8b6EHzIwpT8cKBI/ELtbEBNiGZj2ixcSAr9yA1TUIzzoh9FfXikckqWK1ssgEhg5EpswYUozKRUGZSKAUlIUIWgLSmuZ5eL4vSYptJognRn4PofrhVxnrCmX6er4H8abx+X2AwkdavfP4YxSSzkxTt8oDfVgSdeH4dUdUvgyQDHPGnVFZCNh6+EHs57rA1nsVBPbA0XAYkDMQpC/iwG1tHsmFzEFsXgAAIABJREFUvbY37MvowzUKbgNE8BBTm52eYMBoR+qPmghUYVgrwA21Lri9v8enL17hyeUW33/2BFMmPP/sBUq5x5/8yffw4vUdXry6xy8/ugElQJw4kg6+ccOyCGtPWzVUQMBHSgk0WbkxWQvbzQYExn5fpe8poRTGUhq4ESoD97d77A8HAGYpVbDc2LYLOr0wJOvwhggVBZtNxtXFGbYXhLtSkKmCKaEw8Or1S7RK+NtffIgPP7vF7e0tlmUP4km97IzbQ0U9FHz6/BYfPD3g3bMD5hmYt5DELAwQZzBmze4aQ/ii8UtbrhPxVTzDZ1jn0PGig2//x/kUpQnLvoFog1LuUUtDrjKe+7aAEvDRp69wf1vxv/z3/xcuLnf4+V/9Ei8/u0ZCRm4Ni1qVjwzYWJ9j01l8ma/ktyj7TRIOHSpjnjNqlVRmeUo9NExpWZJM5FEbYEZi8kLoLW3BRJhTQ5oyKO1xOW/xh7/3Af74D76DP/vzD/HTT26B+Qa/+1vvYJ4lFDDlCWZYk7AsA7LQBd7G9WpIPXxfc6wxwsQUHMniQCmE4amCImMo681KKfX7ndFp+CGHRFBxFqwRIz+NgNja5VEDITkNs+1j1x74Rw5lIUbwGg198TcjlGMfwOroxNsH8MQt7SuetQajvqdrRajOV8M9XRKNXuym++g9kRIzuEmt2WkzI6eMi0sJ0ay1onLD/m6HhAUAIW8mpHlGSlLK53BYZP5sK4fJx2CQJR2LVivq0t+bJjWWU6+v2A0Kvd9rkGyj4f20chLchDdqofhWG5aygFm281ACDnUHBuP8cot5miTLtZYOsXaTWrEnNdB3WdUH17GK0a/uvRXeLIa/pJlH5dYWFBJe0a6SuystOkYIRmSTaRRWoioJHs5otBBHT8d3QED2br+0t8fdrf5MDQWeEvJ8rsZMSWxWawXXFQ+xPmjm+D5kDNtLQ/JzJ9auF+ojeDS6Y8Qwvf2M7p00ehAvuBghqvIo6YPshRX805pkRLc5ifuyrQ2nDl/WR78HmaW08pCzpBsFbZ7j89VzrbkQWDHtKW+0/Y2K95vj23t8Y2WxLIsybJJkEiaciEDQvRiAeiKOrSiDYNNDPGpBsJvFYSCkbuHyZw3f/ObViROublechpfK+QcUywhA4m/j9eSL2r2FUUnASbmLzjAkRsR8HacXEjtjI9vcrGAljp8MlYDfZoJXLfPEPPQl6tfZx2QFOBgelto9kydaR8c/sdOHMMWgsn0F+D5mtHRiLtfhr4NCOF4IBPqJjNMSyay9uWCgJWOC3hl/Nlmbjmj1BIwaBsQexeM5DXsl8BBqLdfqHpbEyCl78oXHVzMozZiuD8iJME0bnG8TnlydodQneHm3x3YqmPIeNAG1JA/XURURTD37Khok4laNF+ZRsqyEgK5tjQfPyKiloi4FpUkIWcqQ4u4QkL3U2hOfmND0PidQy6i0iIBPl0jTOXLKKI1QG4FbASbGs/eeYEkz/mAh/NbNHdK//Qf8/c8+QylNlZSEnBhLrfj1F5/h6YcX+O33LrHJomw3LeLtAB19rwhAuif59Cr9Ou93n6cR3At7VG7jOpWWQWkLHj95hE9fyN4+uaeAidGKKCnzTDigAvUMt/dNvKScUUHIKL5XamjriXOyhLWfrgTLKDADrTJKKQAklBfoGVE94QxDtusQNKS+oZSiGV7ldwMJknuhAJRwtzTwbsF8tsGUN9jdEW5fJfzJT76Ltz+4Q84Z97s72StbCrwEjs0DkScliuO8Vpb01aBBOet/3UADjdRT0HUkMfTB1v8us5RXqEGBOxH39x21c/VwNK8lGfH06K3o7bTGjH1c9c/Bm/GP8P51774OnHHIdg50w1e4zTy+D8myCJ4FfPJw3taYtd3a7f7SlVEvPtvlOQsxEjG4MpayoFAFUkJOYmDYnm0dnyylouwPYsiAhK9SSqgs4auHpejLuufPlBVSo3FSo0HfRy79ksRGZDOgtBZ4qn6wGqdipOg77lOaxGI5GdtlbNoGtQL7Q1NeIbJ8d1uxo4qchD6nLP0VLMNqgw5ewqBMMDcYw+i8yBeN8EjY3jTDb3CjvhGIfWzhHHQ9hqJf/vx1VlWP3LUIMts3OFCHvcPOutQdEZWV8Bi25NjYN91+0TyTOREhIaHq9kWT84ZP1HTrmCeuU169dsiFYH+jMshwRdPH2h6h0XmuvLLud2UbS125qsiDMNYk1/35tlLWobHxCLot8JVy7KtlXO9/b2c8N4zGV/CZqHDGz2+Ob+fxjZXFumRJFKEEKmnWpZCzMZecM5Iyc8+8F1KDiQKZYNk5xaJmqbh7fSNndiwZF4tmUhw9i3CGblafddz5+nrfN2C8xg476deGcFKExRJ+P0XoHovuj+rgwDNhUn9Gb19yS9JKX3OwNugXpEzf+xlaMCjoNh4RNAITWfKI2C4DfWF8aQQd0Wq2NgR4SFKYO+lKAD/DOJ8CV6sxjVZfHDObeI5X7enjgYfHJ7RGAIIoRib4nabWuIvIFW+KbT/BkHlk7S6kh3E26yFbZsosglTBOoFE0Fr2NCYs+3sshx1evD7g9nqPV6/vcHaR8MXrL/H849f4u5/eYkoVoIZEGS2JktsYvjGfASCJF2eaJszzjHkjQi5rgfVpmpC1YDIISLMKelScX26wbTNqA7gB+/0BSylYFrF+t90BFWrNtTAdfY6ExBbwzJg4Ydnf4fp2g9t9w/7A2GTC2aMtkDL+/C/ucF1ucItb7Hc73O2rGkKq7LVl4Onjp/itZ+/ij37yLr779iWevjUBaDjsq2Z5LbJHhLX8h5MiSyURZtjeEV1cR3M5TmwE9Ubv3BlT6GunQQmJ4zxjt1vwW8+e4Cc/eYZ9Aj59foezNONQgGkDLHWH/+5/+FO83le8fLXDoQEgCe/MD7WOugnF1mip5iGw87As5poRkgUwk2QllLI7vewPWX+MgjXtvaxzzThMQDZPIABC1pDLAyolZD7HzV3B//Sn/w/+7O9+gf/mP/8X+G//q/8At3c3+Hf/7meY5o16S+L+6g7fozfarGPR+ECwLIpx3a+Urd6FbnzUflV9+BBxQKTnhwYN/Me2CAwWd+aTsgGQiHZTNeyG0YiqgN4NG/HaDsYkL4D2wX8+VuKsVYT1ezDwQbt2VDBP9CEqq4MiOfJe68voDQotCkq4PasrIKvEL2ZsZIAoy6ta0z2Msk5ra+BSsbds3izJqqTMgoS3LousvVZl7IzfXVxeuKeulMU5dWu9BBDVhkSEzebMu7E/7E/gYmGu6/OWJIkbD2GvFD1XiZAyIc0bnCXG4yzr8LBvKGqwa6XhsC+oS8WyAHu+l3nT56WkmVx13VqElG3VadxccbB56qJR9rV376FGgQ24wEJmba5DeDubxznUVWSEdajjCni4pe1OcO+64iPS3wYa4f6MEfMAZqmW3xvYIls4i2lUt28w6fMTAVaDOACEHp48esSNBzIfe9adtzom0oy0jiEMMynGa5YLg5A1ss6MEX0fLPmzuTXJ16E5PrxUUMAlpxwdRzToczb+HX6z87bm9EKnFR2raGjw8V+PS1/czh6ODP5fqcC+OX7TxzfPhupplRlmXatVhRT3xSCfo8BAYFANUKtuyhrKBAtWC5ZsBIGii3oAL+i0Sp7yuIss/+uL2BYHHV3bOxgJHb44BgInIHq51sTuIYWEANpkPCy9dlxf5FlNe0jGYA06dSjDjJ68mEBu+OzjOgrpmjDU2TJBEq9JtmE79COOz6BpwebotCLmDPW4I6EDp0IX+nEydBinx2m4dv25E0NvpwvI3q4H20Krd7oAW4Mt+12E1BhYs7pEQYZ/Y0sWo54zmCVWmHRrFftaUAmYtxlnJSMnAeyPrx6jvZfx1tUT3Nze4+XNHT795Y2JeXQxIGosGJoZtSGlAixjWOUCAucmyWpgKdxlHEopoR4aY79fUKsAGUbPohbp2vvs46ZtqBX3dzt8+NEX+P33zoCLCe2+4lAq7soBt7t7/PSjj7G7X3D9co9USdJ8QxSYZ29f4J/94Cl+8sP3cbVJAKoWeQbQXPyrfsgu/Ic55N44MzjhgXnr67QDX0Zfz3698R1WTqdgrrWGTc4oB5YssiR8VMrMMnaHht2moPCCXSkAA5NuuDlBZadpi49b7+BOAZfRuQAlVsBpDyDMm43uSa3eD1ur3WATny7JkFAbCFKXUUq+VBz2B3z2JeOvfvox/sM//D62c0POk4Kfcamu+3N6beqqIv90AgR1DwEhrtGV0tYv0OewjtGASntLOFAHh6c9oCgCGjHHw5vDfHZuNv5+/LyHtlE8FJHSW308ot/kGBXPMGi6zs14PAqIr3vjqHCGL0py+h4dcLKBN45G8P1ornxoOQ1A8EtqSRN+C39dNKOvrwHNjwDWSA4CLHy4ccN+v1uNw5joxmVzGJsoblZQwk946Y2W0LIaLBtgRq1sWycTIdGEWhNaZSzqCTRAH8E8a+1CUxrtfNwDz8yo0fhBJMrJqbnhMO+I2y3s8yj74xj5Zx0jN/4M09z3gdr4GQ8e6MZ5tz+sK5xKCGSJZshg3TFSOMEFVi2Hj0tsY5TlRismRzvY1c5yH5cOExztwvY0t6IhphxkLwCcyphr4CvijtW6tzUYOjGMd/z9CG+HsTAeZy5h9pvHIev3jobxkemIR31s6zfnQW+O//+Pb+5ZrIz9/h6UgM2cQYlQas9kx8zYH8Qyl3MaijuDZJ+LWEmkMG7KyQPirF5OCpw1Z8n4VZVp5GCF8/DEgdC5ow0KBBsYWcy5xf0yfYbd2oUfhwUpJ473pMU+9j2B4TYSxWuIMaeutwAW0gjdj9ItqXLDCOoAgHqq0b4fSe8FezCGCAgYM4WDqhaKvxLEQh735kjfR63IGbJ3b2QaA4DhcWzHeQpMPeKMB1W/hxmKCVyCZiQ8qbwPnToGVKQzYPMTn33qGMAeC0343NrzRuFAsEQi6NcF6MqhviYBWOoixhmWgsVMjExiWSwKEKZ5wjbPuGp3uNg2LDvGbsfgtMHbjzM++PEVfvbh5yi/bmjtGo3FI2mzzqwBqU2yGhNVUG0arURCQ5SQa0UiRmmSiGbRPR8pJSyloJaGZalYlor9bnFlPE6rhbP2vsONC20hFGZQkr1yHz3/BL9+doln715gcyDc3i14dMa4vn6Nn//iI9zdFuAg1ulEhD1XnM0bPHt2hfefbXB3uEVOW3ACGE0BX0NjQkIDqIhvLi5r40Os6cgRhe2JkPORpLw/bHvhwvy6DkFGC6Tp+Al39wtefXmLu+t7pARRqhLhsDCoJrx1zriYZswkJT/MqNFAXj6HDQRELG0NI4zLK9Aw2ACi8qIENEkmqR6IvrfI+QuRY6CcLR19C2MAgCQFfwUDRea18A6ZGJebDYgy/u1f/hTf++Ad/O733sXZ2RY3N3fYbHKvf0rQZEkN0LTv3vwV2GUOPBc9zG7gP1gd/mMoMW9zphoHQVN7BXq1GYwPj560r+AaY0MCzRhN+O8Dfwknj9qOICTCKZNdD5z3/qx+i0bQh4BkPOcKzmhxOdk0Zjpq0GhACmH96PI9Pqy1qh4yuDJq2XKZJORV6gSS14SOoJghHpysbnWJaoKWCLLXiIHAFKqUSMpIqGyyvW9EhClnz9jLDRL2GJQDE7rCS0zedqXOgLcb0wAAFXwQmjfjkyiKneZIPZDEwMTi6ay1umdKkqP0MHtm8Wa6wmc1d8JcW6vlNLssZe8GBT2IQtZo9rlT6gj9UaUtKlD6t5ZxrazpDtT30nXcYUzLrjHeyjquce2lY4Cncxg9qgzLIyNKdRvCbjsdDudMGXLW3ud1gItBgZJXVO0ToVSp+2klVpalqnEgozXgsEgt5fOLLbbzBG4NKYcIAQpjTz1/hY3XkfLYE/v7OK/HfB0dcfLwa20kpO894iyMubbDDGTe4vi+o0QTb45v0/FPyIYqYHCaEzazbCyvmlZZUr4DjRdkAoiSFOwGnGnmKSMhg3kCN0meIGEeEv6UkqaNVkJOGj5iySidv3EPWbWCuY07E5ZrO3da6wvH4lwIvoOMkcEcKYwnDvco2rEWjA4mtE3xQlvoAIC0YnCM0aKq4ZKZQMRYy+BjHEF+ngiegKILNW1LO05fLYmLRBSc8qSetGKvcM36Hhd4NI7C2OZTB4drOos2ZdiGMdLAyacoLUbg6c/n1bmHGzMAx3Q05uh1iOxkEETR+27PMouiGgBRasGk9UoNNJilVOg/4Ww+R1oqbqqUrbjeVdzdH3Bzc415Zrx6fYdPPnuJL764Fm9V6ZEBfW9Lkz2Sui8nT2koyyL4QrOjloIGoLLRkBZFzoR2qLIGVSmcs4TNttUYRmVZwpsIVh0h54zNZsbjqzPsywGHZYPzy3PM24Qt7zHXhquLBLSM21px4IqcEmojYEo4LBUff3GD/adf4re/8xQ//O5TZEpopei4pkjxq+k1jUln1QTugPs67Q590n/oIdqm/hzpcgNqQgXjUO8xbQrOzzJubhlIrPMk77+5Psg+LM4KOqok6G3Unx0bEtdfJOOv0WEYDK6sBj72pC3MjN3uXni8Gu8MVa5TxA/LPGUQAWVfkN1a3jDnhLOzGXe7HZj3YJQOZkfLEQChJcqsiqt0KoYwyv+WLh++hoa+HdFg5BF9zfUhC+1w9hB4d1orq8bbjbccK1h9jEb+SboGwkTYW/opCjIAgS5NFh61ZXzkP+pQHiMK2+k1e9xuael6zk61Z6iVgzh3few4/H7qaI1BIqgGRSvaZIi0fiH3+bPi66UUcG3Imssqp4ScZV0tpaKUhuWwQEL9hc5TykBKmFLGPE+YN1MfFJZoCMKEloCl1M4MgkGhe8xsTLSQiq9PDnUNzSspZbSYySPjLTySMqT8RwLmNA/8zDK8mkHFlF4bb1dOGw8GISBsRzE6g2TNNqOe8M7k7woUcUQf9gRzIhgPisuJwD3TLHcc6fPN/fE2t/46MmVWVbEhg5dOvE2T6iRm+3bsw/0FR8oqeptijcFYB9KHa02yq2c1fX6tTedHjA5C80JPrTYpw8aCs+9u70GpiUycZ59P8g7a/yrRAjY2GbY+FIboJTRc8o9SFAMu6ph4fI+/Q5/ZcwIdG6K6s+FhnPXm+M0e3zzBTVl0vwWBOYFbwpyzY6x5Bs7mGZQmAcaqCCIJ+KqHHUzYZkpoewmtkn2PVTyIiaSA7uEg99oCBSDlHgwQdDTeLcJ2Cw1/mTudW+0lQDbzW00jGOh3XNcXQSLZDG/MYvBqRoCE8F7TwPw7axMpPL9bVmO7hMmOwtWYhVvcGgFaHJjRNF21PgvkSVAaNWXw8iLSLA3mgTTlYVB4wvhUhkNsQOrZkg6qYsaOW9AkLFCZSSw6HNmBMROi7L/3MePhQgJATG4MGOYVhBqKZycFr/G6tZXQz4XPnhZbNCNP8pB4DAEZ6Yq6EncEr06BQng2Or/WZJvTHDwkKFEeyspUZtRWZK14Su0GogWPL66wzIzLC9lz8/IVYb/f4/LRhM38CFeXM379xafY1wNqmjE1xsQVFVLWIDMhawKEWnvGv2nKmFLCPG+QUsJmIoAbipbaKKXqXsUG4oZMhM08wezUCUBtY+FwmzfA6KZhmRPmlkFccXEG/P7vvot//jvPsJknUCZcnTds3gGePn2GQ73Dr7+8xd/+6hXubwmpCX22w4JfPP8SN6/3+E/+ox/inYtzTAeZn0ZAI0kwUXTsknrFonA8DvUBuhLIPl+nvNdREK4VFlMI2AFKA3jBfkm4fHyFd995B88/P+Dl9WtsNCveWU44227wnQ/ewc3tHW6+uJaMpS3p/lZJcFMD4eUso8qBF0gyopGnsQIgAqs3Xfi5eJlt73k3Xvh6q022e06Sgn6v3u9JE2E3AAsXJGQkFlCVNg213uOtR28hTwm39/d49eU93n70Lh69dYW8JRzuKqZpA7CA9NY6X5znyZWKsJL7mk/AOsDb8YcqvO7FSR082t5we26OBiQWT3uz8Qv0sFaEWJUG33tmwNsyTa6BVxMv7CqGQ/8YKrav4zv75YEWgdU7RNYE1dLPR5kpf464Fnqo+jr5SIh2YTNckcsBGC915aKPs02GGIdC0XV9risI6/XnCp/Iumkm5++EPua2w8xnsEntV5973cs5+XusRQ1lUV6fCNvthPPzDZgZy1LQuKGWilYL9mXvSp81aZ5n4ZPzjJyAs/PsYr9WRm1VFZ6k68F7p2OtHhmTw8o7WPmNmYdMbpCXcGLZe92AAx9AJJhpnidMU9bEYkW9nbLlp7aelMemvWrIuUUaVUl9LTUqc6ddwybMEtUCMChT6MkYidFzVfQsq/0HC68MPAUYlLJABM6fCeM+UOmbOgmaKFVmdGIiNNJ7m7adZMQt0RC3hhbW2UNG5taslJJ0ymoPD0I8bDOMfbM1mBRvTbmvOebZFUhmIG2yY5LzswlPHm8BEgPGfncHELBjyWUANq+3lprh2vMBBEjq9jdXMXlIwuXKsPKTyF8Fm44YxjGkPtP5Mvd6ysyM0iw0W+bNk/P44zsu7z7tN8e38fjGyuLjJ+doralHT2q3idUmZOACwEUyoyJb5kxblRpC0hhVk24IUQaArxYIAXNwbYRg4SAICiM6kXNfiPDfu0JwUnHo2mUHUyoETdB2RtMJ3NpMREOmvsGaa4vLGIj3TxUVZxq2wkOIAUzBNAURAQEpOIEpjnKvZ7hklqxkGnagU2OiXvdJxnGzPpK3sa0WsIUK+3x0HG1P9eb1Ag9hPgZGFG7UCsg2pnG+mAXINmgITscWYYhHsNPftwZB/TjyjK76gjDXds2abvwzQvpqKLUYuA70aMpzL+PR6fMUOLT3dMW2t2n0WClgIslwuuz3UuCcWa3rwPn5Bg2ixB1qAWnYuO2bE+ufmYBUsGmiElmnjKwhJqUAXqvL2yiGBvNIQQtBt2prqwsixwzGJ3QwUhVQcbY5x2Y6w/4AnM1nePLoDIUZL272eP7ZLV68vMZf/vRLfHm7YH/HoCrjNxPwzpML/Ms//h4enW9xfj5h3sxoZIqqhkNRg3lzPeR6Zcg4dZxSFB46PFyso/VA3wamJhAS5pxQDozr1zvc3R5E6UUGMfD40RneefsM33lvi7tzgGrF9e0e17d7GzgQAYktjFIBFHhIEONT4N/Jkggaq9Pyc2Y068nIOplRoFGASwE146EI4JyQSOqZEZIAy3aOuuxwd12w2SbUxiil4lD2eP7JK2QC3r/aiGJxVIIA3bCjvM3oiDjyHfbkVsYJja/Zvmw2Ru4TYvRptMl+P3S9MoUavkRHiqO3MYKwoHSeopO+82cFkFwxskE35Yy/kt5i6H3kN/aM+J5jiz7G36Kco+NrfM+9tpfD3yi7+liYrKHjtvU39/u82afGJrR91e7RjNCVQxFp0rrj17J6fOS62sz7V1X5knDVeZp7aRltioWettZQa9VMwgBBspNmNbblnJGh6VaWBbWJ4ZYog9VdeBSRBFmPZElSIjaKIDsoOMwS9dFBvHoCE4NZ6FdCxgWPVd1zntVj31T2zoa5IP01A21KCZNFeZnBs8leR9Z7E5ErTazzRDRIK2+3y0bHWafmPaj/1NftWqmLv5M0sMun8PC4vl1J4XF818qqjaNhG2vVcMT1SaPRI9K1rCe5WxIOyVjnZOPNTk9NMRGRGB3kIcKoKrPUUgZ59EbO8FIvhqP8vd6eEGY/tK23qy8SOrouJjb0eQzXiCGlOb+2NWtrLB4xeuGreNub4zd/fGNl8e7+HlPSYtZKMDllAUG+XgiVFwdLptgBQCxAT0Qo3LTwM/t1tTa0pWHK5qGQ7I2UCNk3DMshXkE4AzkV4jN4AQFfMDFvHGtogAMiWK2boFi5IOs24fUev74g7dr+ziigySVuB9THG8ql791LMTJIOaX17rJHjiuogiSYADyjWB+HIHT0HyIcRbON7ScHjClegK4cdoFmY6yAC8GXar8587deq0BvHZBo/YhulQrjumYwnTmu94xEAcQAi+ezg4hxuLsgMI+K1fVkv2gN3jywMWqC4xAdgZ91OMZDRzSgWJdqq2F/ijJogmQwpXNsGuPs4sJDjHKuyPM9Hl0Rbg8ZxAfUBilLwQSQ7EfMFVIHtDUc7hYkIuQsoKlMC8yLA85u0QcBh/1eLLylobaGXW0otTOZyn3PQsfBBgLE0jvjCsQH7A57fPk646Nfv8R3//V/ineenuGTLz7Bi/0t/uaTj/Dxp9f49JZxf0doCyOhoTBwfnGBx289wfc/eIKnV8CrFzvcZgBpwpwzMs0ewmYsxPenrgDNevyPwMMJgXvKIm3y0n7zrNEk+6ZS3uBud4e6K6gLY0ozNnNDOQALLdgvBdfXB2D/GufnhA/ef4L82Wvc3u2OQ8+NV0WgH3/XayxM2M8F3alpOLJnNk0TQEBlSbyQySqSMqacUVuRPbTB0OBKXWvIzJgYqLni/Mklnrz1Fpalgu7v8fTxU/zX/+W/xL/6k2fY3d7hk+c3oJy8fpytPzMqUG+xnztSaNfKot/Rr/UspGzWfrh3oUJowhSdZnJCFYFIAyNfCecBwLxxA984XuvB/NbpSDvrivEJ/nCK90U6NdWJgvHvpMdcbh7WpNHB+tn22YyjLEPTES33d5rSGVvJZPvCjoGjDNmKd67qWj7k+YmePjeCkiVGD3JgDVjZJM4orxliyIhj3422SUJYp0lrtIoH73x7ppmrGfvDAaUU7HZ7APCSGzlnTPM5MgOlHNBa8aiRqCwORm3qOQXWc9XvG8fHQZffSXCpRqpEa+RAStnxS3JoYtKcsN1sAIv4qtU9asb+sq51hvDwVptNMmC4zJL6rcbZ+YT2g9Hnt9OctN2/fwWv9fkzOoGWAUNfZ+3UbSt+7u0J7+qKYL9nrS/Sim6P1rpOS9Px9+uszeilMcRIQZ4VfbOZkbN44w89+VUTAAAgAElEQVSHBQAwb88ApdXWGg57e29PaGSlt3rekICD9DsN36A0EpVm9utSsjB/u1p+t+0zg6Hbn5qiWjiOGSw6BG+Ob/HxjZVFC8F0WEJaHwaAaVc5JbFmAUfKojMyEzzKtLz4DkiFLfumcmEi7Ezb3xUO0gQIzlDJW3h0dCtPb4+3Tb82VRyIxCMxiP1B1q4lnwmlkfn742m4FP3K+GF1iQlY6oCw7xVSyx+SenRUUHP3LtjDrfaPC8nhTcHCt2rHkRI+3AUMRTKH3zt4o/AjQ4E6dWZpl3u37JrQ16P2fYWedVoJMzB32pMUrV3GEKtb02L7Qw/JQgt7yu9IKF25SEeg5FQjjryeX9MnEY7kxg2jvSmLQeewNCxLw7Iwrs43yLSgNUucALifY5gks3xagXW7vgGNtExOdQFw2JtVXQRzoowpE6hVg60Cwld0ZWMgpyUTaIOE+dwvC16+uEate3z58g73dwt21wv2t1USI3BT+pZALUmlUoFGWA6MpoqwleeJdahYw+BOotZxNoLAjOd6D756btbfRaWSaIyGhgJODY+uLvD2W1c4//wGL292osQwYX8oErq/MG72jGlbcX2/oCKu4Q4MI1iPiiN8pjo/wWo9O+hWfs4MVDXkyR6dXuwh8nJ5phmSqPNfWw+qRqYkHCYlSXtzKAUfPn+Bj797jhmth2cr/Rqrl0d4CjTYmortENrtq3LF0X1VR3pzrwJ1vmeJg1zJC2Dc+uUA/ASvXgPLU5EskS5OSqcgt77ekBTEof7b5cSoSBy1LzzkaF2ytVWNfa4ERw9ivJ77ejLFMRYdp3A9h5dIo0Lfu2x/iPetw73Xv8fgWzs/8lRt4vEr/bA9aYGh9ndQ6C+JJ7wAcE9mAqYpRkvJI0TRaqAk+7JzIrQUE9D0tpoC21ftStEKBz1Ai0HQ96PpFh7Psta3nvS1anyKJXrADR68WpMyeFLK2fi7cGKR2dTXXOiHjXmXf504zLjRSeR4nuOk0eq34bph3enfKHIJwTju3HOQ7SdGNIITnNoUvHZM9LUj/1iZkLX6NGJkjDycgVrUi72IAyANZed6rgkpZdcj6k4fDxFTn58eGdXxnzktjg2k45oLKFXnusdErUv7rO98c3z7jm+sLG7nje1HRmcgfY+aZelKddY7wnIgGiyGYviQUA1qvUaWha9ZGCqDvfxF9ndZOmktSGtKBY3ZTv1dwWI1AvUVUw0LWTG+KzXhaZ1R0dGPQ7/dOm4JHiwkCgE8hGE6pSz4v8IVZNy5A1mGhs9onbEIIKJHj7mfjUt46JeBJ0V8pMDjyKIbcSiNzM4s8ifBid7cToW6RaUzTtGxy7W/2z/ySSY9tCEIIu/7SJ7DfhTZO6XM2fcjGYbQ0Bulu7gnj3TsHDARVCj7h/6M2P8gaIj6/g2z6BltmiXWe0KE1ha0VrAUsXjPSeqLlWWP/f6Aw1Lx5PICKd+Bm3nsRe0gSlJ6xovSJ8ybhDwIHXYSrJVxWA6uQJSD0Mk8TyDKmKcMJkJd9jIvTcJAWbKED4oaGQJvezRiEG0AytiXBb/+1afYbDNu9zssu4LyuqHcAvVQ0KqGXNEEUEMtB7R6QKuM+x0AJKQ0SdQDGmotuqcCqlwTPGvEijaMg5yCDn0ZHNP1ACaO7rMAT+1wW1BY9hu/9+5jPHv2BFcffYnlo4ptysg8Y1kKatmhMOPwesHBMunpMyeivqYV/Pg6bRZOrLRodIu+xMT4Jny7AUjcUBskwyNLsWiQ1Nishb2kRvdAdT4gIc3KK/S/iiZzyrKvuJU9GleUVnB/W/C//5u/x3bT8IPvPsF75+dSC1KZT7eGV+8jBevXCIZUCY+LaeAN5HuQjdxsIEYeC7/GOUQwUDpgQlAYVnN8TAIjo4zevq87xmgYA28c+MRw8fhY6s845ZEbXzQ8Zvjc5ddabnDUDY/KXXXeJ3th+6tW7bCHrLS2hwCpjcO6kLv3g1kVlVGJWCuMDx0SidH3qcfkXK1JMi8rWM+1oVXgUE1GEKbtRpRB9TTWUoZQ1WmasN1ukdKkIZ66J7IFjxAgMlKxihkure2DguRZe4PsNKXk/2XvTXpkSZI0sU9Uzdw9It6eWZlZS1f1NGZ6OCRAYA4EhheC/50nggMSIGYOXGamp5auNZe3RIS7m6kKD7KoqJrFq2J2A5WHZ1X5IsLdTE1VVFTkE1FREdgCFTlQS0HKWc+863myZM4lhumComu/lHYWkog0gow0DNIOqpA6egjgimIYQPkDdRvR1Y4l2M9xCocJin/TPq4Yd1kbxbbt2JEi7z8aHlsHDNHhib9k3RL17+XeEIc5B0Ae2UApdcmNqmY6r1oPuFYtT7UWOYfKjBpUFyXCdGi1Gn1J6bhq3el7WG9byaAOQup3e/0ZT/YDINDPnrU12r3KcKVmu3kSG366fnDX9zYWr8u6ya5lCkyKOIv3IKeiBb2TC3eCekT0wVpYZUxjHmMgK0jegXqWRBWSlEE+LKudsxLBWcGoCpIiG4+hBe2SVdV2TPV1T/BxXIQeXjbl7TPc/2IhBv4Gd3fZjwZETEEZMGvNsD8rhkoCa+FmOX4WgEW4RLBoDrME2JZjp1Dhk9m1UUyJdEBkoJ9aWg7EiAA/RQXnizYQ9uy2MbNYKaUTTBriLwYbKVhz8Y6u/9YT3ngKOCaibGQk9jnxO0n+sVZFxiZRgqWBH1cgVPWsn9GgzTWRJCExKFpNMa4RuLT9kq3H3ACZORraeFMiEGdUtMxqh+MBAHDiGasbc4zDNOP2MKOsK/4hf4dTZjCuKJmwJhlkrgDnGwmBLAvO5ytOpwMWFpCTKeF4nEGJcL1eZGxTxZQTpmmSvhbG+XzFtay4XoUFSkFYgU72nuYsJuvxIIf9qSy4Pq741R+v+NU//h7/0//wt5juTvhf/o//in/47h5ff/0ej5cFxMCcGUiSbvyrn32F15+/xG//9B7recG//e9/gZs5ITN7rbICBmrcIdJ5duXXwiydVzaOEgOtI59hw09tjNqegk0wodYMmmXH4btv3+J4qHj5+ghGAaWKuUh4JyXg9nnCizd3+PbbM5ZrBa+SXIb5UQ2exjvuYAi9BZGHpTaDUcG+8V8i0JSQdC1OOYOTgNnlGkAjxImRpow0z1ivuqusGW5qqQoGJEHTzMBaC671im+vFdfrir/5mzf41//Nj/Hlm1d4kzPmSqCUUBcrwURYC9wA2hg6ROHcX1sXXOv+/QiAVT2dKTgrLKGUTSr7upM+xHlta7Nfs9YdM3aN1oA5SMnfHQ1Ge7rJ+dZeTKS2B46tPTcygpFjxnDOLYFYc276W/3XaFQmajJwz2iLJqPtNpi0Nx1GpBken8LYkQbB0OydNs3ASOpYFmUPUB0g6qi7nwCjO2SWv33OGUXP4ibDMwFDWIh0SgmUM9JBjSM1/Gq5YllZjbIJ05TATPKzitPqw/sLGOJcSyljmieknAAru8F2/nAnvM/7r/2tkljExJbzuSefUadjZUzTpIaoyAcikrOWJFnqzYiZ59mNFjA0SQ9jLaufxyaCJ8QxnZYSIZsRRqSnSFpinKbfZAy+G2b8OhoY2J/D0XCLc7+38xxxkcm9fSMpXv1uLqXU3+9G39D3Ya1GY58AIEum/86xx3KEI56fJSJMU4uwOGIGkWS9raqXazV5L8akvgYmEf2oiDtuTL9t+7rZydfvowFbaktURLYG1DDc25F1B76kW3RHQL9ZE+Tvp+sHeX1vY7HWAk4ChNtZXtk5kOQMkgEMnBW0xBUHR4vMLeQBzC30A2hCsrTsXSMA0E1uVS4MRpIYb7DvfCYH2AEMEEl9QumFt2uFYPd2puzv0ZttAoXVs2gL3S9V2GIAjpQMytcMRm1/TGe9edL6bAYFeuHTj4FgLnffIfKU03ChS2qctZ2v2OEmIAxcdt9Tu8fmRCWyKpZmrbkScBuYwq6HCBGyn6qULPFOE7x9v7pLDyY0wbm9k9EO9Xd0RRB61GjbvANx7FHgqTFoN1kdiIYedSx6WN7BqT7Ne7vhtZt/37mn5ilklh2jRA3IcJXU7QlApoSUC+Y54eb2iL/7+Qv84du3+OWHIg4d4/mZMCXG6eYGj2fC9fyI63X1EKPKwHUp6pDIbqCUClFyq5ydLKsALSGNzjs68m0mgwBkBgCpH5gggGOqBc+evQbRLb79+g/45o9/xPJ4xd3pgIeHBWsBCicp+k6EF6c7fPb8Ob78/BaP54LH81tQyThNJwF9UwPBBmRColml9f6a2wL1HmyOO0q27mhkQoisFFaR0Pu6MoAZ8+EGx/kYnEUquSpQrgVTPiJB98gYYBJH0VPb7k0x25rfva2/l0U2FH2xOHTYZXGF7vgxsC4LCkPAKlHYaYCAUtKdZGbkuSKnGxBn3MwT3t1f8B/+429x//MVx198judzbuWTpowKtBIB1FLV206L0zkSfUP/fnzdOkSQ1YyuTSWGSzNglN1PARva6Q/7uiSidmhqYCD2FtrzTWsN4w13s54Pc6dq3RqWowxB5yrZGlzMrUB756zt2moRQpzQvbO2SXA50Qm6J3RaeImD1EAV7z/pYV0z4jq54kpmmKOPvLORyoB5cLwE+W26yXblPBGTOrVtblPKSNn6IjQgNSS5FiQCjgdJ5lRKwVoWrMuiPUjO78bzJW4h7ZKrN6hcN2mLor9SSAzYjGI5Ox1K4ahuWtc1GAWqm6gZdw74SUucwZy9BAeFQ3LbfYeH/gyqNMrMpicD/wzGjUfnjHQA/Jyg6XujSVt+zcRh70zEd6Gv4ffGMuRJthqLPyUfTO+YQ2Bok+AbIP34elwSHRYS5inHWw6H1u9i9TXRKTj5Wbd93XWqdLSwvmtGfer15K7TPo4trKPQOmy0H416+HT91a/vbSzmdAPj9GSyQas4pyQCJbPsfJhXhADYYVsvTJska5hg6z7FOJlHVYsx64MiELQfbMZNWAMG5qNgcExP7XBuZXhyi5SE1ZMq0kqmr3qvdjQWu35G4lDztvpH1qnN1TxbEQyQGc4YhOVgrHq4inruzCDrvWxaHDwhCNy2QFPXT2i6fdJEAX2nux3OaOx091H/qwIFEY7xnGV7LCrD6Glkrlt62/sDlHImU1BF4bkncR2kHzZ7TViGHQgVcMtaQJSQc/KsimYIG7iAedcM5BegO8tKBpiol5lqZLryoiBomX3nwJRLC4sJoTZEYFTUGtpIUpg6pYybE1CvQCkJP/sXP8L//Crhf/3f/oDf/eERX3/zAYSKVAuuiVEfWGqGUQIohd+B9aplbQxs2xqBAie2nTU93xhASySbgbx+hSRcV6HTSY5a4mFZ8e//wz/g//qvv8aHh7f4+tt7PK4J6+WMqjtu3gYn/OqX36A+Lvj5Zz/Hz3/6DBOtuvMOLwwvyZ/EwCpAo3tbiYHG1uctE7XxwR0a8d72XYKftTY28OQtGbUA03zEsgC/++0f8fvf/xHiPEmoKYnSrxUf3q84P15wWVYtuC3lbAiQjKSBzg7s2JwIKue4JRED4M4lQuCvKmUxcrIwULk/54RnL+4ASGRJKUVCjvMMqpLY4rIuumYZKCsqEZaasBbCelkx0xlzyjgcEvjAKOuE4+mIw+kEZAltW2sFlyYvGWKwaOK/Jsvb8umuzmjnHsKwpobud/DIZj3IK7iMcwCben7oZHNc5sCGfxqPULvdM6paXY/QlLVv8iw01Q+X3QgVwwJdco3ufRyM4a5fJj/aCoj6LmtZo7i76XLV1n+NdDaDWf5zI4DCC0cDYLjG9VbDmAD4GXKAfbo8N47hYPMC2KtdZ211MYd/N32J746TobSotWqNxaS7OASuk9yrGchTApAIUyKkWfbdVg0tTEX1umbbLVouxtzfBHi9SDsPv7drZrV7YwSDOFYkwiXlCdCcBsbKeRbt9/j42MC+ro/DYUbc/a6arMqdKrpTVZmBqTkPnU4skmOsrzvK1aq8Y5sChLzRb053W4/RSDRsoN97KZwwRwxTz+ROuG7uA19UZd5ObnBP20j70MruGMc2+tutLrgT/onn5T4pr8GYJjtKYcmPbL02wzARYOYkQGA9XpIoOukV6wHNt+KLt+G2GF5upUkGAnrfXT5QyNBsAmAQ3Bb47HTbB8mfrh/A9b2NRU/SzkAturuoMqJqOFHHJC7UVfBoOxZiaJ6u/iUqjLlK4hYK2VLRFrmwdQPgxvRjym+TByLsWhiNhWIgLLSoOttS1O8Gj0oMx0U0NDqDrf0SbKXOg7ZdJgqpKSyhPeXagRBujXd9IF+aDRhswUxY8+O3m77ZMxba1u+scP+UAwu5gckOPW+v5p2ufkOnzM39xibSFahHQY74dwMNO28zTNPxn51Ws0vOaXEHkHpakIKUAMj88Dv193q/ASeC0YYgZUIioNJ1kMI5V2bumiWwGCAkSaXcOZOSAIUkNOdacTkXJGYcjjMOhws0ChUMCYes5drsf+VP8dzK7qJgJ52bJDX5Qob8niyBHsYWTjlSnvTvWWpzZlVylbEy8PV3H3C+JlzLBecr43ItOF9WlCq7lsnCqStwc3fE8xcnnI4JU10wzTcASHlJduS4ViA1QGurL/bdvORtKHvKP8xjJ2fCHy7zmpPK5x0ALEFQBoCC6/WC63URiBhkI0jOiDIvmkLdGtoL/IHjYZnHZvTV2suzvZ9myLfwd/YyGNM8g2vFgQFME/IsmykHOoBLBS7AWldMOeGQDihcMVPCNB+Q8wkJjNvDDCLGs+c3eP36FX721WucpgyqRc6ggX3te3iTg8S9sTbw3k/Qzu32Gdvuvps29rWuQWsv0rpRu4eJ/VtsaXbG2gDu5Xfyp2lsI/4evKEx/KvxlgHRfqjdrlwAgq6SY4fje5VR92BbZzAOeLE3kMlH1pBoT8vx3XF8Y0RP/1lIItZI0I/Bx9E+ozh7QzK2JzXE0A8ONLDPU5LSQtaI0MdKiUnjVTOPgoA5J4foiSCh2wriKzO4FD9ryGpEtjx8cQJ7hwQZcSIdIcanpkCWNZUkNyUReT6Iy9LCXaUca8U0yZq3nSQzZq2wvJ1BTSbYfPzkJR1auSubi+18Gp3jOooYYrw+ps394aA/nSNDm/vrt53Dtf6N9LU5GFbMTh+e/kzahbMhxz5wjx+js9WcOp3jKOoU17Ut8ioi2EiNTRs6evlyFJz7mHYztsYCw0q3L+JursiwmAMfkQ6frh/c9b2NxVLOYfHr7p5BbGbwYtNe27Y1TAk1xrOzL51HxLKf+qKV0Cc3igKAld9IXWVhcRHU9mPJPA2S7rHt4DDc7Sdvbd4sBACZYhl6vfSZluzEYvdtiE8sKBlMM5QDgHRF0NHY6i89LTbjd957DgYhAQStaUk6FyYTBk9QgwchXIPsL3+ovd0I5Rlqh/BK+zp+Ri3BBPcSxf+wMLNe4rBLRpf5DRWAS1RYrGEx1I3VnmsGeg/TooIadZvF5cu5iv6cQxToMYy0dq3bRImTxTzG3e6n86XRqe34sBo67X3wDhJaQp4OOJPsBlnY5XyUkO0P9w/47a8/4He/f8DlvGA+JJSSsRRGIjFerJA1qyeTLJbHp6qBJ1aARB4yJfSwcFtPVe4gB80QDUoGYGSu4JrBrEkGGFgr8PrVKxyPBb/+3R/w22/eAjVpHcKKicTbeXs74d/925/j5z95iTd3FY/vzsDtrSzzxDB3iWVGBUiTYJSgpCPojgzY1kRUeM433YAaz/QP6I6B38/gugBpwnX9gDfPX+Lu7hbT6QaMR9nh19JDZC9iPZPCBhY7snq3reeWyXZSmbqWAtnDbSwkOFdSm9uc2aTJuXMBjXOekAHMxwnT3REpEQoKvvn6Aw7HjOfPT3j58oTLWjDRhNevnuPh8QFAwRdvXuLFq5dY1wWHmXB+vOL5szv89Gdf4u6UwPePWOuKtUy6DpSnumE9gcB4vEtBMQJxBvq0Z7nbSITujDDgoXUmNErpS2fI1A7C2ZESOdhXKsOnUMfVQt702acSeKkU6Xc2OIQDyieiL0agaezXngPQFeQej0Y0OdhTPO7GNn1lFCLnO32B077xO7rneQhv3TeomzxuY7G52qjMvv2uQ+1vZ4nuO8MPsYZcnD/4zwZVBJPITo18KHOgfEPkNRvt3J/wlejtNJEkAkxZ1qnuHhqoNtnvipI0w7yNhmMfNVQbzXlbU0LWua9VwkpLrVhXcQraGVqQhjQmAmnyL9SCZS3IKWHKyTFOCnJOURkSsnOKYyXunWMt9L/Na5hlcfolm8+6nVvV66NxFJmjRX01XGaSkHyObTX1MlnI28v3TWSYZY+lzas3V+Q9Cuvc3mFRDIIBNLIOvOH96ARKZBl226pZYTIl0DkoBIukkK5LZrlijiCTCwH32NktNjpxwylt8uTZWAvXZK0nrkNr1ktUGRoKPBApZrPz6fphXt8/DDXP4a8AAAHYAmyx1OSLL6UsHrQha5pkKGyLntHCCIkkBTXAyJqWtBmN2oOwIO38GwDNFlpF0KX+PEKT+XZ/M4osa555xbzgOrVzGu6JK5LyPRYrHS8zUOR59lAA8rMebfHY88nAYBBMfdibeCijgmsK2gwMuFCykuujl6jWqpnOTLGEEM4wxaTg2meZWwhmu388n0oOnBqnhN84ACiCz2sftttpdcti1ECQEwiqKFlCoiNgSpOGCLHPQ+Xm7W07daFfYAdVzjOadhxWoqWfZcQzek0W9qAIOhfObdaPqFy4ZU71XT1PrmOCl/0hV+KWFS8kaiJI1jSujGlO+PLLV7g/X3H593/Eha/ATEhpAhdZdylJOKAkM5D2M5nhKmfS2vmxeOLTCmArX/muaq8U4vSTfUSiNFcuSHYQngjMK94/PuKxMp6djjgdEk6nDFwJ6+UK8c4fcMwHfP7mhPl6xbvfv0N9OePFsyMKrsiU1DyyM3VCeQNAzAZ5gyK3GQ1gCybDAE8a4J7e6IywkXbz2iv+rM/UNMlcriumBFRMWKq1LUxQNSxt0sKmEl0x8hhC4hSE70zGqpPCeCdgA9kRt7WhWUc5o5RF6noBWj+TcZwmvHhxAlHFslzAmbCeKz4sH3D3jPD69SusC+FyLjgeElATHh+ueLy/oJQHXJeCh8czyrLgi88Zh+mE+5sJz+8k+6SdBbPwKq8TyRzQPTkbRQePnVSweWp4jbV4+o6H3ECry5Ig35yw7EAVvg62oM765o6pHeBjIDEC59I4Mtwo73THourQCNBM7vc6QeR5PPsPtDBO06NA+L6LfjDjjl3WyzEQ0siFeCQgSHNK3bqxNxGN4ebWAzMYg/zeuZru6/m9Is4Td7RrTubtzu7YMKncNAOwKlZg3/IRnmD/n/KPO8/006Q8oB7URFbU3rCHOtJKwVquYrxdRF9MU8KUJhxPJ0wpaaJzBrhq/bwitaVzRp4EspV1hSTS0WQjwQAwwwNgTDlhPh5kTLUCteCyEO4frliWFasmk0opo2i2VsNpy1pxratjn2nSMkpZdxqVzlNqcjGnxvuiTWxOTObqDBGc5p5V1uc4lFvxi0wYtwyvMoHDsww7T982IhofeMQCRL6KI5RhSV9GPtmLLulk7gAC3FFlTnmzbkde7NoI67sTAQ3X5KSPVMGaeZqRiLzWso+xCrKARhRJZvwCi5zTtEwyGiaY09GihZLWNGfdYGgQrN/5lc2VQPc2PW6sNywKl8Uc2un0LMyBuVfD4NP1Q7i+t7G4LMtmoVB3gAiyQFICkQDjyoxFD3Fnrf/Wnm3i3uoqShPsAh3QXQrqlQ8wvrYBO3ZD0LKutsXLZIZn34I83xZgCmcSibZGYbzvqctATUpaBxGhT5EGjp53FDD15wuhllZnWCWDHX27ImiaYRda6D1oJEqplWtIKpArinqm4jib92sQqs1dtwOZEObUh9Z4wPsjbVD426C+ie4I/kYHgihQE1YsypIVWqSY5t2Mqvjuvn0bq/XKQh9tztruaQhdMSkZ2jeMkdTYfGoX2pwK0kcjkJ+uVOHc5qGYc0Bfl8K8VBIPNihjKcDDN4/48vVz/Ju/+xzvL1f88vdv8eF6xfGGUQrA1wJCC8U1ryQTgZOFolYkpk59jHRqc9jmwgCWYdesSqaCUWvBYQKoLpLhjQBk4Hdvv8Pv/vfv1LAk3MwHXMuCOjEyEy6l4IwL7n+34u9/UfHjn7zAT756jsf7DyJnbL0bjQzsVt0p8jCy9n2bQ5svCjUAg5J0EB4yxNmzFNvq15glzeBakTPA5QZIRxyPhJuJIdLxCMbZgiY0ioyQ02goKl3D7vKsWRBzUmigRlHlCcwVaZJ41AI517hWYKJJnWMLUqoomMAEHI4Zb14+wy9++gV+/NUNvv7mHr/+zT0qz/jizQ3+x3/3c6z1HpVX/PYfv8N3354BZFxLwfE04dWbVzhNCfNc8MXnN3j18kskAu7uTri9OUl6/mURcGk7MBFhRJ5S+e/u6sZh8MKTinLa8QBbq+1eO/NmoaB2HKJDcaYbLPybTX60e7frlzVMOLTSGSz2HyBhfHqgI0bWBLliPXJjb5Dd/hlrNkHlzXUddkGDfoU7BmSM8P4Z7aL8UgnWrDY1WKNegoQdhnVm+po1yRfXUH4ihQfDYMwci9fGyPPhEOJXG4OR7b9xu1bmu9uxAzQXGaltyAqEG9YHW8If9mQmnc6rpOGZlp+3HR43/UxEmKdZM0e3LKJlrSjlinffPkj7Gg2Qc3KjoJaCy/WKqjr5cDgIj0yTJMiqQ+g2ACTCtVSc30m7FiGQifDmxZ3mRFBjq1ZUlizF67ricl0kcRh6XQ9Aws1lEgAClrLECQvYK+pnOesvhk0V2RgcbtFQS0npU213mj1XnIXCmowwnd1jr2Y4tvbl8z5irfGQZcrejHXXKRTWDlQ3Btkh/UreEwI6zLhtszVnBlV8d2XGopns2XeqFwAJSxHnXkoqu5PwCXPVaJCMm+MEyoRDylWlmPcAACAASURBVIrHZb2aE5JZzzcygLJqxLj1v2776Rd1Pz2b7c4Ymua3KAlLWsm6ofORwIpP1w/i+t7GosW6j4aGXy50Fdi728GTa7cbg6CIHmEXBqnd72BvUCr7u3nyzrZAvWH9XUBFBxIc6Gn9Gz97GUBvkl24ZMYJBwDYXi50ai03zx/HRdi/Vv6Wm8pmjJFu8WInqYFCjuBo8GTZLpofI0EUBuzGNUCugCJA6o1l8ns7Ggaa+S6vC+ow9wpiKrMDksYDA3DXZwzCMFFkDblKbDcIZvUUM8PreprRGQjcfh0oHEt7tHvbbpL5nJ3e4Yrdd+AwKLTxcsBloLEzhHv6yOToZwb8hvZIicO14P7hgpoqaALyqgrlkPHi9S0eHhZ8/c1jUGCkuLyCFCjluBa4zYiN1dinE/7Kc96X3hYT0E4EruqhJln2xECGhEMlU5ZW9w/C729e3uLzz57jFz99jb//lz/G3emAy+ODJJ1weSF9YOt3UNpcK/ZBQbs8sgBNBiGMPe5U93zHjSDUzGXLxEUpoxRxpl2WFW+/e8CHt49gLlgZnrXO5KH1ypeufpAo7OBw439SXpPPCRkJFSSJkNASKwGMRQEuKd2ZGTMywBlTSrg5JPzoxR1mZqRVdkGfPTthnmc8PE6Y5yN+8fMb/PirFSBgmkRPvLi9wd1pxpQIUwbevL5DToSyFiyXM2qV9O9WD5I58H+UK8ZXzLAQxgbeBlnEzfR4om52a5At4+XOd/EvprCWTW8NTzBamCKNz0eBC8Tcye17C1/vX9/OvEUda9JUxt2MMepJxqNsisBvK3+aDWWyhJ2XIv814df6EcNb7SW2NuJOSf/C+KOfbw59iJfpikhTvzcS1SVt97QvoJH21hEbRsMGOw7qKEOCfDPdErNwdp1QI4oYyMRIkxhlc85SVkN5tzJQ1xUEK9ExIbPI4vNFzpWnrAaAGlESBaH1Gq0nRABLVMW6VpBVeVV8Y/iMiST5zZxxzOoU5Iq1qDG6qoFhuQk1aszrK7o6bdE1FCmSCMStfNa460uQCLI2FzESyCdKx7mZ1O6KsrKfs7BjzuOe97aNv+SyiCoM4wK3ZWRJ8SjoHnNum7EZWujen731Ziyuq23LEgpXcF09KaPUB83q+IPqlwypaSTlZyhNvp6TPmfn+iVyRdZNtX5z0EHcLY4Os4rqIXe2xFVt9xAaTnG++Qtp/en6613fP8HNkLkUiAzQrjHkM6c52BK6PILXs9+VUeMiFpSNMhkRfEcPmIQxUcji5iEHECUNJN81k2c43EuwWmseRz8MjSGp5VtmTGsX8EVBfbiMtB+FnylgAzitdUAXrzwkP7gP56RGPulROybidIl6yrEGjwYMELvORgeYcNB5GWj1MWFqXsC4mxPRRuwTiLbBB6qMTJGNBmN7j4mhoW9gKY3iL4qAUpSnZ2WyVsN46tBRC7c0JRsNhdiXfk9XB+JCk2CETmG+Kc5Xo4qPwwJWW3ryOCZbH/oOC6dCCOvligLClCfkacbheMWHh0dc+QHffLjg7UNFqTPW7x6RiDBPwFp0B4EENBi/2pqt0HOhAds5VtUhmIJJaQyLjmdYjQa6XniWHKVJijNq9CRKJWRkEDGuKCCe8WKaQAfC3/2Lz/Cv/8UX+OmbV0g3BwnN0npTUlB+y7vWj6ChO/BiYMPGbWuKdb0Sh0Vk86vNFduBGqKp7J1EQDV5dAVokt21tx/O+NlPXqMy4/HyG5QF+PbhfXPkBEeWgSbjHeFqq5sG5CS0ziYv9NGaVtQqcliKhifkXEBcMSfGfJhwnCc8Xh/xd3/3Y3z++hV++tVrHDIhVcacT3jzIuOz15/hMB8AuqKsC5hfIacJh+koKf8nBpcrmAkVM0ACfBMReF2wOqcQUAllWbEOnL8Ruvp5BXogDqGB1JzUNWaCzrMFGv/KMyYnzcjuJgrWdgN7AoSohYb3XYKFV0WwN8pz78MolOOo7auQ3Es+b/pp8xnamUbAzi01/vRkMNrPPwfMzNhqtxFiNt9uR02Qn3e1T3TVA8aRHPtwfuwLb40toDP+fZ091dzgCIrPdyJX5ZS9bnQgOfgfvgNU7DKU/+SPxhMU7quOGRK1YxDQTNvMoreMbrLrx1g11DAnqVk9aT3Esq6oWuIip6YrU8qWokGzKbPKnuRrAAColDbWoEZEhkjPDzlJ7dXTCay7g6VULMsKroyytJP4ktm01Xc105CoNIWgesmOhbA6zpLUGkGBnqvWNPuxLEejuxAuhlc/ydbRcFRd4Mak4bBhXjd6om9OmyL/m4Z1LDe0Drn/xuUNOR0YMax3MCa9Vf07y/vmOWsVgkn4oEgm9MsVWDR0mFmMvUtlPPKibSZ1lhfn1URWy5E9lJ+UlxIm4YfU6GIyxNd2Vfmj2dMsiz8w7qjGMHqTriYbbCF8un6o1z+hzqKEasSF9LGp3oR39ZowgLnwnQN/VbEcUvHaO43fEofmRChZiuTk5zMU0LHhraiE2BUsq0Ahrh4utxldEECtP9YZW/TDOP1eA3nx7iCc7BUbUjWB6DrGdyuCQg6KcFDTfgYzerjaoeiGXXplaOCnn2//OxgMrcP7IYoGLjaD3WUe8vZ34Jx2Iew089jvvl/ybm7gxhRybNtx3gD4jCW1L2YQCVsFRfvEIujDV+IQybOomnUSKedGC3NTtpuxhR184wZ3xOj0sIZ+EmFKEwgJL+6OeDiv+PDAWIqcM2OSXT0mlsiARBJWt2Po97vEaGCK+t9H9hhZRR3aYuxkAtKEAqkTJaABsPO5lStuTzNSyTgegDxPuLs94vZmlvNAlzOIgFOS0hh77MDqOfUvjfc1it6SGLF13ucGjccoPA8M73Go3LXfqCV7fdKPBiiXteDmdMTLF7c4HRKuzMg5oZQQjjxQ0NoUWdB257J2nQZPF0POvDy7O2KaM1ICTtOMKQO3x4TDPOF4mrCuB/yrn/8Iz+/u8NmrO3BlXB7OWK7iaJMi47KLAcguIjiA0iLmGyudC3QMacLlIs/k3O8k6tIMmLWNM6qNtpJMHqpRNBI7zEWkF3PYSXzi5ICD9TibbM6/BlpNhjYnFRrPbBrde9H4hb4xyJ2uFxtEHHbNbei1B85PiZ6/5BJWD+0xI5rfHW/Fd0bNsyuSud23s3bGNreGwCh76MkxGn26fjXlrZ8HgyH827djc/Pnzdy4i8rocw9ULeskJYiMd3rjQGSRrjG9R7JOF6C2XSka1ws3dUTBeERq7wFRP1rjD1+Hdp/I3ZRbpwh6lj4ROCdUYoBqM0yJfc2DTdJJ31p0F3lB+niZYVFNZ9R2vnDr5Ih48SPz0P3ksFa20SM08B4Q5ZGGVMfvot5/4uXGM1uZpU54/elYtMvy3vouOKfHdEzmxJGcAqlKZQIULbcEO34CPcsI1zdx/JUBLpLgLOk2Y1LHmGGRVjXI94Wbr9Qm33ZWaLv50372BOoN748R89P1176+fzZUFa+kC8aY+KlwUCvW3IR1Q1+9wogLWL0SXjKggeB+Z0w9TCOAk5hET5LAzJoEYjyTQO1d3e6CKsqwe0XYLgDXOymew6TB48T+d5eQZhizADz9NNupsV7BmUwxuhPgCXoazVuDrlaZnRZ2GoGZNSNkAz8EzfpJrNlTyQWs6AyLZ2+7VybQuisAqjbWBNoBaPs4SiZoVCpxjtvOcmxHPGhdQoz4eFezq90TzzyOXNyHWJPW9zS6wIlcnY92BhQuCzGWLlq4INC5zMfLeL//0OckKSGiTd9CbTWMCBW1ErjM+NmXb/D89oC7+TvcPxb86bsZ9/cLLqqYcpYwJ2ZLogAHSr7UgpfBeJcD2YnEYOnYM8ynGYqJCIkZ85RwuJnx8JhQlysmEOZcME8JSyUsa8WPXtxhKoxKV6SZcJhmMAPn9Yo5JUw5o+IARkKlsJOLDhfBgJIZjuwTJx23tSuyQMK3EORDNy9hwTXFZ5/tnMYoCQkZh1yxckVKGZcr43wuWFZGQcWKgmmaUaskszDg0dUtY8BggGU+lfqIur5rC3Gyna9pInz2+S3mnFDWBc9ujri7nfH65Q2miXCcGcdDxk+/+grL+YL7b+5Rq57zO8i55ceL1XqUWos5XSXlPkvCoUwApYvwkRqjpTBKqqgsroF1tQLloXyR0z0Ku8b1Dcg3/YFhbo3VyNaoATKCo+uYNyfyIpRbOi7hxgdtyp9AqYMO6q4nPo/REDZ+cyoKrmzyswZW6nYzdJ0x4MmYQAxbomQW3qaUT9cTB7L7XVc6BihrxwDac71OhOrsERdE4zrukI6vdmfASLvU3mLvpu4ZudxBGIBp7P/4rj0jousL7T29lQmsD7gz0/mN+/9cBsHPREtiEuMDoUuaMpgTlmVFKQuuy1XOpE0Tpqxn/IqcOyxFwlCnKXs2U66EtWqyHCI4j7M66pLsytvYpK5pBVCwFouzubouFyMyI08NRIq9npUHq+yQVtlzrJVQIbkQxDqJWM54W5lbd3iLOp9MTycKR4PcyOrnQK5w9jDOd5gDwDYCFA9R6nDitlWby26G0UJrBr7h5sAWCd3k2VM81uBI/52RzAxDGVEFSsPBhtumWfjHj3UASBPAJI5WOcYBMGeUIjSXQBg1Iqvs7rKewSWSMGjWGFcROTLmdjY/0MmYFq1ffSQCNTr00npDj0/XD+v63saiH/q3PwGYERavZa0hDE3vZUsIoww9ZBc1ZnMhEpgtfySJDIZFaElarNw7AZ5uiqhlZO08egEkys8qcfYKADsvb43KgEG1OB268fhH/Y5ep5xNEEalWioQhQpJhiunjXm140Q0UrRLzxSQ7rTIGRILGVKjVuliIV5mG9TK8JIN1n8rxqsezj6UK3R3EADdjnBX6gRaBy7eZ0I48JjxTbA6GJpwIIB1q0tZa7+ztNmR7ikmSro1s3+Z8C/OzE3kEZoh7AqZPITDmyVNkw50dIvj7N8Z/2ZXDMa1BhAtVDaQ0nd+LKlSImBOCTfTCY84o6BimY7gI1CnRzyuBaCEUlcspSBxVaZgTDljPs5gMNZyBVPCdVUe1WWRLPV2oEttS8bHTAGxS24ZCcZJ/AiqV9wdCbdTwpRn1LpgKQVLZXBKYALqYcJnP/oCr2+P+PmPXuPlzS2Op4x0lXDHaz2jeooeSYxlujiuSWbWzKTygZ/5ZStNwD7H6h4L39k6lHdEh4vPNwEbMUsAzSsYjMuaJWx1PWM6TaDHFVjOeHEsSHdH/PGe8O7tA5ZlVTlKUnM2GDNJwbTU0yTklEB1DUBcBlhVq68r47e/+QbECaUyXjy/w+Ew4f/+L9+AGXj9/Ba3M+Ph8QYvXh7x+esZUwIyJ8x0AINwrQsKKjKfsK6EZbVMiQcQMlLOuFwWlAqUS8XCBZyAtV5RWIDszc0ReUqoa5Hzx7AwUpNrGoPMgIVMiXMLYaH1MsLuAfWZQ40PsDNH43proWh9yxtHKG+lXnuDNzA+0noTdGXDiRwaiOFo/Q4mAJVzfR9cr7lTIZzZ5x3Z8mT/bDTRiLJ7Gkgt8fNaxZFI4lyx/viuiUeUDHTcc8CEDu19Xsve57bIbY6VHtjO72bOKzSjuZQfaQa7ge9+d587naPvG42MoOv3eEziCjQCAuxF6VM4U2gDSUmOzdze3QIArtcFa1nweD6rXCdM04ycMg43JxCAy/Uiht4qNRRzSrLemLHW4nNksqyyJsmBGIMl9zgM0HOxLE4eXq8yrjWAfuVVO8soyWoIKcvGU1VdUvXsOcPOILNvKBAHg3SKeEmPFdRtxEi/np9e03E9N9kIFC7ddzYeC2tn1kgX6vsjvqGI5RpO6Q2ivp+7yzBiCYi+iWPpjGPKAAtENDEJAlgOk3fwpawWyZElM3iGJviTMldcJZy5lAqiVQ3KVVCf8/sOvtOxtLPmTbaa7HSdqDxl+LUlBQq89SkT6g/6+v7GYrwCh28X8aAnoEZUSm3hRe8oqHnYwuexvb3Ldrc8nJPCogV1D/vWejzcHQ03jvcm8xu170KXmqdtVIC2QEzgCm0ayAzn5cSCg2eTjU25BNI2TdiE7/Z2c+MijWMevbymW01smnCy/roAMOsxCEXzXNl7xnd2fdsI734uq9V8gn1ubQHjpHcCWBtqYZf9/LXbgiJPCvyDrm9Ogu393Tio3W+djV/tGZnuMQzgUJTT2H7bYWxqbOgrevqGt4Sf2+9l3YlhUcDADKxXAnPC8lixXBkvnk2o64zr4xVMExgTztcFpbIYAImwsiQ5yHlChRSVrkMvnCt42xsjq68LcxZMBMoE5IzKCQkVoIp1vSKljNOc8flnJzx7doMvP7vBzV3Cf/t3f4PnxxPKsuLhfMXlcsJEBcirAlo7u9XTIWj1DaVkPbCDw35+4jhi4iyo7GtrrH9uh5fqBAJrSBojpRnLlXG9VjBlnG5OWNaWMc5ka78brl9ZdmgH5aakKXAFgYmxqsE4VUaiipwJz24TXr26AaUjrtcrEoC74wGn04TluuBySUgHycp4vmoWRzXYuBIu1wvyPElKfWKUuqDWBRLyCmSaAEwAMTIyrusiEUuVPQkaAygufzkArSAjOx7ao3Wc2kERDYZd+zrqi/HZ/t1diwZ2hrmOUTJRRrerN1IpLvj2UDD0VaRR638f4jh2Vefe2zR9wcMA+it20x2iOzJ7+3sAii7r9XM33naHv/l7j15PGrc+tu33NlvMW2PabtiyRwVqOK7ihrLu5rDJBTQIPEaBjMYioI7ZHcPWgJHezT7nmtUWsq6bvNSSFmtxOifKOByaI75UCVO1BCWkeRkqSx3ZEpJlmUJoRpuN2Q4EwI0Oy8VA+g2ZEVOFxtw58AN/21lLqIMz+HeMytHAoJxd9jbjfF+Pb9d3WEJqVfF4w97la3tgCp10NwTDI2M5jr0+jX1o89j4qPV7eA7odAtzyw9SNSO9YOQUou7k3wp1dnDLnO9HAVZJniTTzIJt9dWJEpBZI4nYj0xXO48YEh3WMF6G5kfgvu862m7cYAZ5gssdBwo1DPjp+uFd3z/BDaswpqYjTN5E0EyJ9BwLBFibAVUta5d4nKx+kLUtzbVdlPbdn+uXvtcfCdaXPeuCIBo1UqbCbA5T2uYddA/hjtDpSklgu2ii4pA+DkKQZJmzg1clom+JUjDk+pC2CHLGz6U/Sj9qYR5RuJkys/55uNceHRnN4DGQAxUWPk4aBF0Dg6My7abDW+i9VE1tB/p142ynvmy0Dbg3D7EBTNfRZpDGumofYa5SipZQ6RUEeYNGhwgCGtDwUVsJot13jeEy2k0/CkDxLmVPVefKO8xdBUcvhmzZ7l3J1Yr6yLg7nvCjVxPuHxnHFwU3pwl/+O07qULAwHW9eg1HKX0jwCOlDIIctLd5W5ZFFwAF/uNIjo2hmxIw5YyXL5/h5gi8fnmL63nBd2/v8XhZcF4LjscjXjw/4m++uMNpSjh/WPDyeMLL6TkmLvjT+0e8e6g43hxwc7OCdOEkauBDdsTDvKHxnQHp5hHeKmzZYTenUgsH8pntDNMBNA6AR3bHxeNeIAI00QXXR8b7+0dc14rPP3uD63LBP373LRilS3DZHDnRCaDvNcRpn5iCJ+GRRIRnt0f87U9e4faYcZgY/93f/xzPnx9RS8X9h0d8+9098pzw+tkJtRRMNaOuwJWAx/MVzMA8SUKOpRYgMfKknuqLJFLImXCYjgDEMVKZJVy4Vpwm0QtUZedbwtds/car+jqOxwSY2eKy4IMkAnjXNAjN1fAM9Y+ORvhwNdncXtn6I7T3HaSP6iibm6rjIwm7M77iJvPieVPPMkptDXXhyNopn37n4V4/7IkdHv7wM2cui+Ur0x/JdWWfh0Bo0bI6MeCJ8PqX9eCawzSOY3qKlKn2ER2mX6xf9jRhq3s6BwMiPwTiM7uRZ1zF4/MNEwO1vdeiXcwRUMN3MQRUdtAsi63p6fauym3XzRY8m5NR9VoK5xJJI7iuaxF+YUY880G6O0nsKdPEFU4SIi7lTZTqKsdiqVzjQXZ6tfJPjeQc9CvJsQexZJGK7gxaGzEERmkz+r1t3bX53X4fpgxAT2uhZZ8syoeHnhd8WPDGvExMxIAWsSRH9Bovh+WqBrX1vBn0fZbQHmvFqw60roorWPMtUK06LvLjJwBAnB2DmBpYapUst0abSuoIGJydkOzyzI6kFBMCjKzhwcIzImKq4laVGR4pZPikocI2RjMWh3m0efpkLP5gr3+SsdhAfvwCG3Dk8icKdZaC37UUpJyQ0vY8kC+1kLV0x4oBEPPbGMMiCAXu7u/B3lZgxXbG73aVrQuspvS7rnZtQBN3NGXiSh5mgwmAtC0bisboYIgGFRjfMvYQzP2i3TNWxl3HOO4AVaG2AzqZ4KRk/T/DjJmnvcPcbFCk7t1jP0yRRJDWxoaOF0UI9iE27XsngANLE5p7Yx/p0/1tY4jfhyY8PDl42oxO7q0e+IzDOQpXgI30nTEa398dxPdvKWTp00x8AGaeQAykmjAdE169OuDmFqDTFSiMcq44X1dcrgWFVwmBohnX64qynsEVSCzh5XmeAZLAqWVdfC48zJQT2HMChzUI1S0amjLPE06HjLvbCVQb4AQSKBNoIixLwXK/4O39FRMn/OpX75CmBefrIwofcDi1RAusP03pWphzdEK4kmXz2u7lZFNFX5sqM9dEvDPWoUMXXth4jvx9drbV6i0SKhV/V60FmRKOx7kzPqylqJj9uxGP6912fK3oTthpynh9d8BXn9/i2SnjkAk//eIVKAEPDxfUwxHL7QpMwKRZF1OSMVcwQFVBKkToMjDNJA6NteouNIM4I6cZqIxSi5/l5SrnoC15h9HWDr89ISna2Bw07+0K7wrnbtkHBg0zGIBZuAbc2CVXi5EGtk4N2G138cICHv82OWBgM6gq0zdRBOyq2/A7B5liO0DxVU+OdtAPHwNtvUEGn8foOAvoHbbrOAL0uBbt3f1O/ZNdCIDb1kPsF/c4hLf99VF2uquNW4rG605i0NFRhgeCBGNJfshuSwPt0cFnubal/umWb7toKOgxEO8HwFz1XCp72QwJAc8BQ0HPHDaGMnyS2IxZ0Y9EACWRES3Ch32nx8smEKOrGm99tIzDUe+DPGrAHV1W7cFoyREfyIT3uRdi9E+/226TZ+0YDdVd4M/bmqDwbMwT1xv+weEX+Mvui/wt+x1NurucVrq6dNrhFZufrcTZ4XnXZcaXmkHWZYbgZnPoJrZ0pjFRm3ZCs51ytbG2pGAmf6oZf2wlrAZdpnSS3qROXjX5Hfg1Dj4Ko44cwcn1ZzXAp+uvdf2Tw1DjDg4rh8QFXUoVwO4LmtUrozHtJPVe1pAqfAPa6zZkb9yhkFA9QnYBwS6Q+zIFWjvQPUWQgrTYN2zEQBkT0vTjN2+f1eL72CXlOoBoBfSe2TYiF/w1GD4NSej7AXJPNaBWqOrKHiDbmbz+LA87LRmsnr/msYwKvBn7UGPDBhXOTgINUJIBBXTz1dORxCtLvEP7sf+0+1kTMi000DzaxT10DdAkzerG+noH/07yIMz0PX6uoxpYTp0Can1h9AWCwz3NqoUVbI8KcRznaHxGpdg+UiUR0KDtkBPY67PJzprQqBBjujnggCPOj1ccJ8bdkUCY8OWXb/BvfvEFHh8vuH+44Ddff0ApK57dvsD9/SP+06/+iPcfFsyJcUyEw2lCoYSFgff3opCkPmJF5QxQBpKUUCA7a0cSXkmUUDlhWYHzecHxMOOXv/ka50vFWhl5znieEp6dMmaseHtfkMqEr378Ep+9fgacHsApYcpHTBUgvqKWCaAZBQJ85BwGucc48r7YDKZILXlIMwjcQ2q7GBYKmPrQHAKkTIvxp2fb1S89cQW5d16S/lSACogS1nLAdCA8e3aHUoF3775Bmgh3pztczhWVF89SW5mRZVJRwJjYsLG+uwKLmWcaSlaQcAXjJy/u8LMvXuHFSca+Xif85jff4N2HK755dwZTxctXJ9zVI+qpoqQETUeLRMDtzQERrCRiEM/gVeh5OGSnw1IvsnamhBmEtRTH2hLGacTbylVbG76r4iisl5lhkSGCzPZxBC4K+rjpEOuDturyNxr2/h1a+Ji8v/Z1BZWp2MPE2jv7PrfzOgzW8gJJ14W04c26YIpDHRN4tFvF+Okda75zjicubvd2H4Zdr5hkZ9y5caPQnDxBR404uZs1r3cb5KzpdsMTCEAdpm9ZQHPUlcqPQgORM9WNkq1e8YgBvccdAaazwiO+Zm0QhjFinZAg4m2UfRySgnzpGQBIVAETxmihSKVktNUyFpqKtIH7UsClYCkMXFckShIODkKaJtcZhRnrVdcjSM6uEZCyZjjNcgxgnuQs3FqKYP5aNbw1yDftYwVruQQhABkBOPCoHa3ROU3zbKao0z5zO9fJlWFnlYko7MoK9zBHqoY1FmqqOQYIRz0ME1o/GqmNr6rLCoA8H4PrVNbMz2w4ilAUl1p9XQ4772aRekZ+fU+xXYzQL3lj2GjwD0UGgitIiw2Rl6mRdWJnTKvRHgErKy9nJvnYaiZa26Tn9cP7queukC9W7T9r29GgM6w0Sm8Zeh9h5vI5xCIzV193FtHzpIz6dP3Vr39C6YzamIVsqxqIYEvuY5jnqd85smdViQ6JcfZ3uOCHn9mEerjXhKI26szYnYlk3dkLQh36dAxJ9DY9ic14f9/HvXNkeztlyQvYbsN47A3+12B09Mqau7e64qYt7ba03NJt/L4BDXTKtguttY+1tIIUewX6XcIALEzx2U+0jKyjgbW5PiJImmcbiLWD5L1w0GLn2KxYbQRQjjUFBWwpMvR9DOmNl4Xa2Hsj/WM7BihTnPRdvu+TeezNp8yTKQ/XVc5fBKAmVbQpS0gpMx7XFe/engEAx5yR6IqlVpyOhOMM1AfCn9494LtpwXGe8exwwiVXvDtf8YES6PIomTeJxF9BSUAECShyqH8nAwAAIABJREFUR0v4l5mQEkPQiNDheAKePzvg3Z+A2+OMkjSjWzng7jnh5jhhSoxSrnhxmvDZi4yffPkGHz6sePv+gukgtaJYjRDz7DuPlwAcRvAopuvG2TOGs7W5aeGOxnuJjX0CJFaebZk3W3tVEXqqCZQmFCbNDJpwdzrh888/x2Vdcbp5h/n+UZw8LJ5hK39jdlQlBdmWyIosyAyKjcTQSwDOZQHmhLtXdyhrweP9ikorkCtONwnpOOH5yxOOfMDxZDub7bxKKcXD2Zz3Api3s+hxvF4vtgNB+zJ+39kWEc32/h5oCLgyAGvyi0b5wgZYx9CnpisQZV14X5M17f2t31uH434Uh3SCmcHJAFkAzdywYn8Mg8PzfZft8+Ys2j7nj8Q54n4sHr5nxt9IokEWju8Y6TW+c6SBy2HuscM2MqN97om7Rs1rfUdz9tBH+urQfU+/+Fw0frVQT7vZI14I9o8a5wF37PCEd0xZu/+6yRA/PoBAW9fx7YwZOX4hx1zrqhgtEZgI0zTBdseqhqrW6xUAMM8TUs44HCZQJsmgqrX7qBakauflJHSadWtSaikDEUUw2HWfkaCWMhhg7DtfFS1/hc+NrdnAx5KAqIb1Hoj2Efgw6t8xLNjaE4oSwHYWMISYk+orACwFttXBAw1vBxKyy2TS0xmGjFnf7SGx4b024xwZwj5TrEcmZ2xtBxkacdzejv9IB2Ybb+RPnadhFZime0pWSxi1dNawFjQhVAORdv40JBDU1uPfT+G7T9cP4/rexmLHPMytvET304p9bhnNLTczLmtoU7/qjEETxqV6qQdWkGfCs1so2q8IAKOiJYJ41+QFm+cjGEgpeRbWUamOYUlPKWVy6+pp5Qn0C6aWMjw/HCQ3QFRjOesdxTQIlpaRar8vo/HaPx+aZWhNSum5eAbld1eiSi+j38Yw1to8NYB5UmO/Gdhpl1Z/buexU+opwUoOueea1eNnRt0OuLI2o3Nk2w97pu+T71oN47YxtX5ClBPBPaTmQRXHyBqeVKsEO/SMNDMD0QAPgIxJjPN1QV0WTCvhRBNe/eQNnj+/RSbC+7cP+OPjI7777gO+/c09fv3Lb8TjfDOh0gHzLeOuAut0AmrFT3/0BokYZVnw6999i0sBVs9mVaVGF5vHUnaikoUc1gTmhLICX//xAd+9/YDjfELBGbc3hOe3J7y8m8DrilMi/OLLz3B7ewAR4Ys3X+HrP/0BS7lgPt5hzrdSDxJFsr0pKMoKpkoposfUKxwBv4UTjXMfPhAlaollSeuL6dyTypGmtH1GdrhFPk80CfCkCwqfURYJ981zwTFn5MMJ9/cf8F/+8y8b6GU5W0SZYOfKiMTILVVS3SehgNRkI4D0bEoh2YX88evn+NmPPsNhmlEz43CcUOkeL149x+fpCAKQUDDNCbUskPPVgHiEyekqSSN1vFYWiSGFtRM1QwNwGW27erbTVcOOSnNCNb4f58LmLYI953ubDDeWDRjaOwpsR8ZmhhCMKxhMG85GhneklDy5RdzdievfAeWu0YvuO3s3lH5mFBhcZA9jLBt6NPkkJXPEOOp3qCKYdiM3JjAxirMYF36f87HRIKwPBjg409y4ZJNL/Xv9QdbSKzuQ0DFlDf1wmg5GXnjADCY3rGrbmW3jHXiIGeDkSbk6jBHkt73G9BcgYZryPo0ycUO38VRCA/QR9MeQQNeNnhnUaBnIY+e/LGIhSWSITFXDER0vWVb5lEApI2eoMSe0qFw1sbX1IYEwodaKh7PsOuJBOnKYJxw0rDXnjHww3hc611JQNJtpYTHkEpI7QG2d1NoixnLOKGV1nuIoT7iKARv4e5qCHk+EVBOKlv5oMjwkChwuu6cEHBW/A4Iedn0q56hpUmNRZU1CcjlcweBSkVNGouzGEhSn1Fo1AylH9pRfnLHRju84l8hlZbW4GoACUij1Fh0o7pBTJkicwlnQHq8Zo+w57e3tvcvEzl1uKKvrPcgRsrVpg9vBwcwqA2TTpJ1PVjk3brB/un5Q1z9LNlQT4NET6B4ZZmdYtytoPDKu7dgi4PGbcGs0VmDovzdIbX2agt8q2CakCK1P404haYHZePYj9it6bsYdwn5M++DBjI9OmbUvBuNlfDvagtwQOBKwN4DMG7TXTxvHfl9NOIVnmi8OQPMUpsAH7d6egK5g7SD+CKyZN7R+qk/xno3xbp22YxXcoJIYt/27nzJAmYPUDwBBnwp93n7e6L41RkewMo4l7mT48wyIdyUFxd+364WN9QECaUoNwlqAy7Xi/eMVD/dn/OGb98j5O0wp4/H+gu/OV7x7vODd4xUXABMzaE04nyVc/OGhAlxAlbFcLqhccT5fcS2seUcySMMgm81gnlHoxo94WRMxpkQ4HWfMh4zPXh6wlIx5lh22lzcHgBas64LHSnh5vMHzFwfUVHC+Es4XwvGGcbiT8xVJ5QEbqO2UY1sq9vfenG8u6nnTdg7cePK2guIehRgNHM7qrU4AkJB4AtUWQp0S4/Z2QspkG7BBXqCFUlI4O6MsFJe3iQSXcynj4fGChFWBYMWL57Mo7ypAbp7EoOiywQYBLDQ1YGpec2m/1urnN53nd+Si84Pf4x/DkcfgCLErpXAPgLYso7Gu85NMdu+DHoLUVwvwapcPjI/iuu29+DvNb9po7w3Iyjrv7x8/6/pjcigm5gpf+Q740L9mHu+0+URHx52K+L72eTMo/xwN7D7vT8PCAwH/gr5F2uvfVmuZuPFc90oAlnRkY4Q+Mefd34j6k7rP7I6q77Wkb7AzZI6fwzk7G7wB74+QwIyWrSPLbo2GgRkW3LYmgeZECWsnOmJb6QzGuhZUK7mgkQRZQ9JF7mRYFubk668Zh9GhbvIyZjy3/thz3r8n8AebvMSoO592zozrde97iwIqbM4woV0mmylzONSWIIvYk1JJ3UqZbXc0WRRqgAz+i3jYrQObPtktRlF/lBofbcSpdLAh6ifa7ddtj2GiDAG27/iovBje2f/WsCmHw6JhyeyL5k/XD+765ymdoVcEuIAss2RC0BaJMXaM96e6E6rVvuaIfrqzIPo468JWxvOdo1AAtgPbQVlbAhB73gVYx9istc0+MtYdQ8U8n08ZOiN41a404JL68xyDaSTAxj6nIIyGm7cnR1of9q6ndvGeur8po6Rhcj7NClYH7zO3PtkYYs+azjMl+uf7/Bcp+7qlYwwTeapdViMwoYXcRF6xq9oZtkHpdQah/aQGTLe7g0LLZijaoXZrB22+WYGBA4Ht+VoxEgjis2QQMqYp4cWLI1JmlHdSrPnxegEn4PlpBlHFNBd88eZLgAvev7tiOkx49eKETMDvv3mHd2/PePfuAUupuK4LFn+ZrHVJYlIRFjf0W99RPswJN6cD/vZvXuOLVzf46ovnOGSA14RSxIB5dvcMhRNyOuF4ItzeJrx9f4/7h4rKM448qdIuij6Tg7RVAY+RwwFlmJcUvO8uQ0bnkQEqM6BMpsGAVvVnMdIe/RpkrasGZlBhEE0SLUEVmQi1Avf3F1QuOM0zHpcVdSnIkJ2L1ZLIuPFYOwBo/GxQKlsfEuFf/eLH+Or1CW/ffQtKGYebA07HA4CEzKQlcghIE3JO4FpgWUlljC2ks2qxVuNn34mqVTPR6pozo9hE6rAuuwyY3NYidb+bDaQALhqYUa4HynPUOcCwXtVrTqGJQTaBGo/E380AMWdZcyT9GUCFcIsVd29qZmsshH63brQdEFZAKt3hPlyU0fi5o5F96WQAzMC0/tjuXoR9/PHfdw2ZeFnzqgNM+otzJLZVO1JubKPwQa089FGf8/Ko7Bhhv0P2o+GR6BQcHdoMAERuCEYjR9ZH3+HI6yPvgMKtgxNx15ANxd+9IRHsbRw6ZtMEKURDIRiQnT7R+6d58jGbw8pet5QCXlvkTyI1HnMGEXkSFKFBO4druMps1k5/udHc6J2SFoT30HB2TNEMddql0+jsdj090LH7hDUbMwNrrahMKLq7mUmM42nOgmdIHU8k8i+DJCKgKu6hJtcZQEaCHAvhrhvV9Eb4zJagddnqYafwZW/EKfMQwUM+ETCuGY4+XotXgP/V0Uwb7rO3buk3Pm9ysp8LQvRWemSKrfVxzjQktsP4n64f5PXPaiz2ly6IUZF3gtJYmbrH/KL2WLxMUTal0hp1QxGmSFkWNZGHSPhiMkWhfY2JcKzvTffGGO/YFwX8QCfM9gf0/+/aedtATuoKAau22AGrrM82YaCd31WkFpq5efu+1m3PJREgblYTu6HocxVlioIUoj5sbXyhCyXao6/d2vMQ6//G3ZxxII0m2qmnrgAsW3NReW0e8Pb2umx0af/131obzOxJkTqBjPBeriDKeo5kp+tQ1cyy65NSQjokTPOERFLmoNSK8+MDCAmJM45nxulSUdMBpRR8+LBinhJevzjgcMj45v0jmK64Liuknh6BLBFMBSShzVMU0TEQYNlQT8cJr16f8Pz5LQ5IoEq4LhXn6wWn0xGHSUNWa8G6XIFakDMwpwmHOWkoX0UldT90Xui29lkNrYjrOltlO82tnXH9mHzByFq2vnqlGltmDcFPio2rGtWJgKwp2de1erkWxxzU2o1gKxoeHcS1z/TVhylhnhMSVSARplmSqdTKmPIEIsZaVvBSME1iuNpug4WhZdJzv4lbw4OhZj99F7YTUwOIG+ys5mwMn+labgCHN8u5FwE9IPJPN+KSYYmLaOhI7IeFzzrP6D2tGseA/qKhuwe+tFE2vaNkaU5InebUKbpgNIZ+NGurM1A2yNjHHGg1jrlRDv2l5tH3VWcc5qKjpa3FXk67uth5n/V/6+JrOq6tg573XE88ATlaj9oOIFF8k7U/zKXLkrjmn9YnH6Pj6FxsrxoXinUwOX0jn5lDzrvIbWdU6F/VIUOwfWeP/ki5vaaQnjmUEjiFJYM9AxJ+mshbSHqGkdQQ9WMp2NL5yUs9N1xrlygK6Mu2fPza+77xRmgRliU0VcZaxRWwrBVF6zFb/diUdV1WWTapFZHQ5s0Ibw5bSzxnfGg4sVMRgSVtk4JUhoxzb+Xq2mPUjcfmrzGYGv+Gt56iVPeev+zqHUX2/qFhNhXFG0eJ+Ff1pOafK3v06fqrX98/wQ0IxNVB/yYm3BaJJ7ggFy4NHNsuSjDKXPHad61NAUtmYMAXX2zLQCiB2g5X8EITyMOOLCFEFPCxL63fH2fiFtYavDtky/gvX3zWlo93MD7bjpJ5BwNgNAEUXucjYVNuvcjeE+BEtDnD5S83Jb0RDA2Ig1o+LpERuguSWhfjix1W7iW4cUXXQEalbaHe7XOx342mfr7BydJ2kxgtXBq0pUv28JvYfuCrYCRsd6R21kY3zAjCtucRWh/b4FyXuEIxw7i3dkxJ1QqvwWSJAq4Lg+uEUq64LgsulxWECVQesFwK1nPF2/sPeLxc8e6ewc8Z7x8egA8Vf/rjA+7PC2YC5pxwSAmX5aolnCR7Zqy5Zn1k/VPClQjLUvFQL/jTn97jxbMDpvIB9+8q0pRxc5cxJ+Ddw4J5mnCcDpIF9ArcnibU1wRCxkQZgGRftVIRwnMSGqQwRulRFWw28B2NcBM/FT1Ol57XCCP7OQ18NiYZavMxMCYMRiWsRVLa5wTkQ8LL1ydULh4mZgBBktyQJ4YyEGgy0fwFXQ/teWb8x//313j35S0ygHkmULpiXVYUJlyPUg7lcj4DFZimCYCApGiQVTCmnJvMdrnQQC4b/TWENsU1S63vcS3uRS88BQpN5MV7bFe9G3pvVqGf6WZkN8TUdEE0wJs+Y0Dr4zUAvnlp6ySwwy22LmUN1CETdGek1v6z9vzYlr528N67wDV6+w6aySq5zU8hRuS46+UaduCdj/8yoLcXBsdh18wMFut6y/LY6/zqz5qDOPCO80WPLRDWuhtVHfC2HnDjz2gMBCzQRbsw0OpaONDo1sPHwivtntav/UvGr2ciG8kAjskGA11YDBcvXKT3mJFIgTC8Fqv86f2wuqyUJMMq5QSqFWVdUJlxXa8gAFOaPMFW0t1GkOj+KilVJbgg7dCPyA0FrnpaTyOqKjOIa0jo1XT3Hg0/Qrn2roFP8yTfHVTwH6YMBnA5r1hLxeN19cdTEueq4bqDJQwilf+JfM1EjCTyUIHTeAawBvmhmLFasUPpkjc0DtMintwJYGPs8INh0b65fh2Mz2Dzd+TnUVYRhsQ1Huod6BC0EkcHW2XPQdJ7sT5dP7Trn5gN1cKWWDz2AyDow2cU7Ggq/+48TETpjZ0DyA/vVVBvvGZP2Y1e04c0dJRUgJqni4SXx9C+vqH+SvTxBCs+Xm9iq/jj4nsqpDNZmunRsxjbcoK2umxi9JogbQZLfHrs117fpD0JzdgklfGF3D6rqqjNByngyRIUhPMKoQfBfm0CgrtmNxs4nTOCNbOW3UhtvMNgu3FG4N6AX8+HEfy0vvRCs6cZt9Avevp+StS1u7dLvXG2BCXTv5PgEY9EbvyZkpDwoRrq4Yb5oiLsVQ8ATSjljPPlgvO5oNSMUmeUsuJ08wp5WnA8LTikFW/PjyA8YD7cgemIh0vF81eMfD3gfL9iKRVlkVIXtdQG7kDN+UPUABXaWeJpXnF3M+NwM+P/+S/foP604HSb8eLZCccMTJXw7O6E589O+NsfvcGL2xv88QL8p//8B3zz3QXztOLZzYw5TRJGhFXXPkBVCucwpIRPVJnOf2CUGhPUNA9oGQRMC08cgR8pG5Cafj2vR2Xc2pF3lbyA+YysKfF/94fvcH9/AZgwJSBPE0CtfmXKCWstcjbU0ukbUIioI6w8RlhmuSBnxulwB2bG4+MHTNMNKE04PzyAQJgPB6RJDLyUJ0xZdw88+Z+mtk/Jz1IZryO82VLIM1es6xYH7DlN4s9uSMPn3XIP89PjDXJAJZclpyGApMhSpNTWIDNe0NC6hJAduweFUbbGHVDr3y6gNeOFhJ7muIhnOZlacpSRZiFXhnIUtSLuaPKh6bm282Ty085sx/FH+dR32+gUdfc+7bqnRrpy249pzlj9y+ZLn2khyik2oLs8to57/eaGIBgtTC++O/IsBrkvAEJ0RQ2tKmUY8AJd3D4zQyaR7swx7/KG9cHGbf2hOJnY6gMr3+P/2hG2TskrxnGG0DqrO6U5PCGhvpS8XxAclUSv11qxLlcxMEkS58yHI5gt0qBqfd3m/CEizNPsuIlBKFwkAVcyTEaOZ0aHi9HSHZ2AlDer7BGOHt6ZAMsi3BzZfdimJzWztgMoNSyWtOHpSMhZjmgwCOvCWEoRZ+pZE4iViloLzqXAEn/1Wb+rYlQ950mkB60ZY9b/rAmn/Fl3DGo/devRdJMbWbaW26ABIk3C1HaJ4yyXkMSq/QzyJNDGJWbIZC3s3q8lQEhAKkvcAR/u6TdA4ujJX2+bFONa+XT9cK7vnw3Va6Y1AOY/leFBcCNyjwX+XMjmblinMjiH201ImTEIyBqr+lPFMgBo7T+Bd2OfTOB0C8hXyZ+5WDNW7bS5e/vO51V3QRAW88Zgs/GC/Deh8Qh0wi3fd/0Nnp6xz6Zoun1IM6K47WWGTg9KAVrwe3i8Nd6Do9AdE1zuLY0Ap3tvBBDasHdx9GTHl2950X+KNEebCWp0bvaEv5OrhYGg4y/m9lAPLqMXMQpedPeMvOHKENEYNZ4RxWN/yZk3EkUKhiUKWNeKy7UAWDGj4Is3t3h2IDw7vMHLVzc4PTvh4bHgw/sT1jrht797j+/ePeKbtw8CIi4LVm1vZzV09GRmoEqo4/my4t2HK+7PBXmSGlYLAytn5McC8BW/ze/x7dszfvt+wS9/9yfcnwtOx4zjnJEPVRO8NAMqcmADQmNPnubvrtsRmO8pvB3+Hmeg+8LmiQGz7CkRcibkBCxLxVKrZBx08CTNUGjDk9oMbx2lgXFqSgccTze4OR5RygqgYk4TpumAQgW1rABWJJowH3THgMQo8FA1BWMjOGgA8Gl6upGl/9uERvF4v4t8J1snIjYG6P5lu8t7Z2McUgXnloXVMQG+YUlN/mwdmWEN02ZUu85Btjym5FRtPQrt+o+PqKInPx/kn8vLbm6GhlmlunvtxivontHA7wyvxnljX102uhePu/v3NE183rA0xz8o8L6t/5CN86n+RNzgk71d6J3joAPNUQ88dbGNu25HaPSOn250/TAGhuvdfkff2tIaheP3HPAYc1hPvFlQ5kzJicCcozR1ZyZR8vmokGyXXCuuy1XPNmZVl6avyHVVZEpSrNgRxUnRnBysNZlbLdvhHkI/NwxQMPqf3KFXGZdIajPnqlFmDMwE5DljYulrrRmlMpbrilLNaSZ4WOiTYPuPqyYY8nwJqc2zz7fyBpEaXnGxs6IbQsCnWqPSCdCvceNI2hnr006dniv3tIitqSgv/XPWuRne4buew+aMz6fRwRfzR9bQp+uvev2TzixyJ2yCIuWYEZB9B8o8SvIsHHi3crXoUbZfERQ3YWm3mwfW+gQI/oqLhYCWbAfm0UPXp/h8NCCeMhhbbLoVaQ3eJRfIlobTrAWE8XHQcW3heZ+JwdzXIIr3xBDH5tlqYsgdiu4B2q/bGBqWETdE5HM0Gl8AiXezsnisCG2nA01IpM07Im1F6CeCZtG0lnvj78n+tY8aX2gf29y2BAAcvGR2VXjSbPliAEj9LkJrH/SE4H1SFpsglZvM6z960zx7bzS2df5tF3SzhsA7czn0RXlMNvjEAElpwvEAXA9X5ALM8xFzBtalYMoZ0yRhndPpiC9/9ApEjA/vH7GuK54dX+Dl7RE/fX2Hf/zTW/zDrwj/9U8ruBKIJJGOAIQJzFr6wxKOmHEESF1UJHz15g5fvTzgcrngvZJknhJujrd4uFxxWT7g//xPvwUYeP/+/2PvXZokSZL0sE/NPCKznl3dPYPBAosFCAiFwsNy+QP4/8/kgUIR8kBwISR2FvPorldmRrib8qBvc4/snmoIpg/lM12R4eFupqamb1NT23BZL/juu2/QT3cYDaATyQHWqzNZMcDEqcoGmeE9EmR2wZCJzoy+tlSgYKZ3G+bRJSlL2t/QQAUaiBs2XtFo4O27e7x8ccIf/vwZP3x8wKdPj9hY9+4MRi7+4MW5iNDB6EuXs9MGy+HaYGybBKAaSVrtH394wrvXD/jN2+7OyR8/PuL+JePlqxPQO7axybFCHIMR53AkeZUQOxGbG7Nm/E02QDEm3PANhMbqWkJeEsHZSNzZMweyugaBskMB5QO5Z6sV8z4pd1SBQ0fQfgfC3XPZafKT/M+QAdO5ngYhkryRFc2Q275SOs+Dd3vglLq+yDpjIIEXdAkUbFXjLdrxQ+YV3bNzy+XvCMq5PM1FMPSfHHYsz6YxWLOSbu6Ih69OpseO5PNxhkiU8Q8ZT1JSWm5G10mPHK/M7PsrRnx2AvN8JL3q3GDGOcfqkz/D9lpayUny5WisYbhnRysjLPgwOEULzxAVfmZA9yo2qSRKjA6WY4V4YBub/qdHJRApn6mznIoTFQuQNCMBdia1jo/kOS8kVhzlMukHuK/zZSnYNcBcHVjC0ACZbVlqWoWZ0ZeGruge2wBaD7NOmWpDwxgsq65jyL5H6PPIcz1QlocJfhQOOX5iJTaCHWYf5EUPRsrx9B6y3Kkske1H/6egMu7HO9az9TK86rhDsZsPtn3vTqu5SSpy7+v167y+2Flkq+eeRbwdZAqlBZZKfAypkqU6qRItM9gOukXQUE7HAZISTIJXJZY6U8bsqRGKDzGYTP2JsGUKAZH3HZY0Hl9FqorYnU0Eyw0DgGi6B618nNe+9LvBODmE4kyMw9/y5crGpIKP303g6YWA4lab2DlkZvCQ7OPSVuXw9eHzGpNXe6VkANZ00cCtVXSkhBODsSh9Zng+3O7i1J4ZJZl29vsimWOfhqdV23hQ56O+xwd/12fy3Npzs9HznEGTG5lXAuyAY4vmtZZhrIa2OaYzzG1pWDrhvGjFt77gtHRcPl/R+hloDf/0+z/gw8cn/PDIuFxW/OnPP2Dwhu/ffYvfvn2Jf/d393j3zT2++/QC/8d/+gOAJrwo1TkgqzkIhidAAgQN4A1EjOXc8R/+9ju8ebHif/8//4hBwJOmLS4duFyBz49X/Pj5Aa0TBhacTwtev7rD3V0HiLUaLKFpQihz6OBsoNs5YxmXM1WEoYYwchHGhTsO1tZ+Cv23nA7OMZ0wpcusxsFgjHHFeelYaEHvT3i8DFyvQ8i2kQfAiFUyaFv6M3pr2CCR/UWq5MgB0joTHYyHT4/44c/AXSOvPvj46YIX6xXL3RucTt0rmV7XFb03LIsmmpOtJJpMgmTqub1rBkMydswA5ZQGtseUYzQMW/L3/HpmVdBXpCYDOK/XJZVTAj/WlwVjGnp51o2iYkhVWPKKqd6AHd3SqCF153KgdaUk68TlIZf3LeDlBhXvDd0MQ4Yx+xF5PLZPcJf+7n3I3Bn9A1r91vuSp+PYkSx7gtedPUwnJhmVIFXLu+qr6iiyPSntDgXC5XsMkKcqqT/3Cpukfmbxvjt2an7msN1qbNs9AVtpvNg3ScLsjJpkpjPSOJP9NOmMmL+xo9sMo/Onyy1j7DRQIGRaomVzyAiErsdsMEuRrm0MDAw0phL4CBhTpVWzZRA0Johnndvk3Oi4d7rObUxLTc00a3jTtv1IiuzECV55yNEaEpiwYmMDvUljzfZyql3Dw9J+EdkIenUtNrSa8TvZegUn+um2ow0fxoPpvWymJVy4Fe5yZDeNBYbZ0/NnGalNs8cqnLMTmkV2c3mgPEkVhFhDkUH+5Vz79fpvdf2yaqgqpK10cKV5i4ZIrnvPTg2A3qkoI+a+cxLKZQpyIU2NsmiUCYJ0SHlyiLITOseE51UvQmZAec+LVUzGttyzcQtr9r44Y1jTJhwNP4dO09F4YQpdD4nVd1xJe5QwaTJ14eQtE8oNsTqgEkOk3ZYLAAAgAElEQVTz4E1mHTkzFZ6Qspu+Topj0jltJILBcGHB+YB5Hlv9bjQUPQaOSJ2NcBw1fS9bQNFQvIcQ+nX8SG3HGEKJsDv+1hLxgWG1Gxi7QVVXfZOhw5bC0w7b2KXJTIrFsWP4LUfJpE3u1h+ScdoAkJyhta4b+trR2gJqZ4zLBR9+/IzeF3y8PoDoSZyJE8D9gvXyAa9fvsRvv//X4LHh46ePeBoP+P0fgR8+POKHD4+u0bxgBck5VKAlzRMrfq/oRFhXwuWJ0NsrfP/9Pf7h77/Bx4dHPD2uWPoilTs/fcby7oy333wPUMM3b1+h9wXXhyseHx/xx//yHp/vzjifT7i7X9As1We0Mg+ArLBh3kM6UopqMm5vXQe+fL2n/5Ayhq2UW6oOkIyHTc7pWlrDtnVcLuJIv37R8c1LYNFAGm+sq7Ayhza/pHQ6GLhuqyjuBqybHnehzw0eYGr4h7//t/jtu4a7Lu9RA07tNc73C3rv4FUOCT+fTyKjIXt0GGYBNQMBxh/MDGweckmIypJjb2T7KiDVHzz9kefVxQMZmbA+B22M6wh1OiuL6T6fxGdSfMkyYkwnUKnYfDOdSzvzlYAxJJo+Bb0G82S8q9waXLbnjTGwbddoPg0kEkeMv6eiZ+kKuSQ7ascB/L7Km2RdXQUOGCyoNsYIx1VlfTnSwvaTgTWWzKWtvXXYdj/kTIoSgDAaKVSgz+/oKpyHsE2CQC3phGz+vCAWJ8Ofy7mxZdPoPJSk58PADj0UQeic1tr83Qz8hDGhJee20HdjjHJ6yoy/3vuzzmL9hKaZ532bNtC0CkeE3jqglUHdwVOP7XTuOAHKBymtHnHGo83FGEOqkaZjOkLXabC6LTHWxI/hRIXtFe/bkRz6TIs9osxbjCUIPMbWOxpLmTS51XzP9rauCtOBjuYBAuPcG9Cb8+aJxEbcRuCQsbeHQFTPrzS7rKXFGEQQs3h0Y8BC76xyy4IB7kwjtmT5e8hUxsoHN4y3ZGfNWWDtBo3lBZvBKUPC5c54Nujy9frrXl/uLBYjVsi3Rj3sUwTUOiLPHWCs11UEe2tYetul2N26qEGLp4RQMwMjOz7yWzXwGybi74BZiFHC2Iy7Qza5gQqeYOdDojelLtFmqgYEBedKM5aMMCnspHT8NyIXG9Y3WdVQSi8mpSuG4t4gsHGUVU79Jzth1pCPpeBi71jvVZg+T25fYWp9MlTYy9dblC1Tnwswe0GPS8k6lln3hCLt3TCDYTdfOodKr7vAQsZP+e3ASskYyAZpflrnf17Vtkjv0RWBkJSG7a6JP+T9sEZ2l94BbFgvK562Jzw+XXG9briuGy6XK1aWPYtgwn0/4f7b78FDSoWPpwHegIVP2Abjj3/+jPefHvD54YJAdyhukBiobtwlbLCesbReV/zj//t7/PjhFT58/AQQcD4vuLvb8PrFCW/fvsJYGY9PsgUJ6yrO1onQ2gmNgNNyCqODm6acR+Vcd+oAyfxBKKtYMUyYUx7JtFUn0oYYjmD8Lhtxx9gglTPDeDUK3TY781JMxHWTCrCEBQ3Am9cvxLBe/pPWP2FAmvMFcq+UqT5GFMEALpsYK16ciyWIdF4aOhrGOtB7x93phLvzGW2RSqxMwDaojpGgxlUqejU5brEOGzIAsBXmGWlBj4Zu/1VLrM/StzowOLj2stIeZsALesxXBIvyvnqVJRMcYRzd4nA4LZQsggSTteErKL6apm27LKrjmHXZ0TiywX/rWdMXPMFkXWbYeXouDNJ9Fk7KL0ptpXFwNWoPxK2i4MY8ZvhhbXnDNpL4DTGWiht9TJ2DKASm7Q8zpvM8JgB8kHs9lWmMpjFkUrAAltkwaoGkPqZ2MC9UsdP1dLd+L/1zwccuY8fez/oCXLNC9BdfYdf52sbq32VIWQOZVpcgGC2xr3nwwNhk5ZFIjqjorcPOGDbH0ooiOk+ROB29dw+4mF3JLMEtC/BQog+jR5f9Cd3MCdtsMijRO5xkDuZ2FJlFRDidYi8nETCS/8sqqxOWVR4myZLZbmZl5c0DLhLctRHBdNKFFdv2YIbWRNdzsCCm90COzIEknn8+kC+sFO42i72Xssm4HY7p6/XruH7ZymIisCB2uUijRryVF+CpRLZBeQCDzBBRQlJh1pSwPYXTHBZm338XxrX2pw6BuVq8E1tBjpFOQcgpTq5sgNhMd8QziBQm6FgiZcaY2eCDG0JySE8ryjY7Eubs2f3DlUh9npTriGxfw2S4+gu3U1mPrrxKlZWI/SYjlHkbWhp7v3rQZBypopdghNRxV7TZH4pmUx6h2Awn1XgrhoPBqCk9AtPs9FI8h1COxWG/cW0TTnN8LwvA4kccOOHz32nou/6z45iNntkYZHUK7J2j6Ze2ZC+JRdjPdw13fcHLN2dRyvhGjtS4rGBs+vfAsjRsQxy+x6cLHi8r7pYzrtcrLp9WnM8L3n37Deif/zmMRGogXlSRxj47GWiDpR4RdYDPePzMGPwnfPvqJU7nBdQl7e+yXvH5I9Bbx5s3J5zODTzkbMft2kGnO1BbZOp5BdoKhhwFQejYbGNfpk42VKvhbhFux5XNUfNAiZ01OMsBrxaX5mvDBqoUUg1SDK9Md6IuexJ7AxNhXVd0Irx8seB6JlzGhsasRxoSVhtHiBZf77PClqTGAEH2UVuW2+nU8G/+9bd4c5I5Xjcplr89XQE0tN5hRqwcVr1f7Y9sB3Y6soqnLoM4DO6CdKBa55MVLnM4Oe0Jt8ED6S03Enm651927cxXdi7yZQYnmMsqhoJ7fDntzA8E78ad4P69AR/Us3vvwBib4T9aHTQZOjuVIzkeiaxCrtlYPOhB/qz/rcTHMHxVXc7TvTzOWCUMd+9o5TOPpbcWMJe25ovSfyEbrYJmnO4jumOuHFrwCIDNEUnS3rs/KnCnK6/5B7GNFGfMpdrtbrw37ubA/K13ds6gzfPY07wFTX/aVN9nChWnwRwru288qzxqZ7UCVmG5YWkdY2y4PK3eVmsNp9PiK3KbriSyZTDotqZlkbR50HC/ZqEFYwxcr6sCovimKQ23CBKrRJ/5ra6eeZSaI5sOkCI28s5wG5UvV4hdJgHabdPzjS3TrNsYyE9r3MZw+wg2V8lZz3TENxZXhjnu8/jyXKU9DLlNC2pWu6oGGHI75vSDcCiD5u8270cS0HF9a8P/1+uvfv2ylcX8Nd8vBCwbbinW1NXAaCHGE7FZ5MOMNPstG3dm5MuHKSBWY0Pv085ELIKwyLeD8exGNg8LwtQlHu1MVuPieYVv3vNQ0iHN8DG8JGN2d5E5zyzelts1qe/E4IcOZI5kWc/FqK2jqSt99u+tPiLdofhVeQ6RZaEaG3Mpds4x6wn8w7s3xnr84LM/H60tGzSHh87f9jX97VstF3wAai6HUX1rTEVZHDiS9bk8IYzWOtZ11TQ5YB0rGjWs6wajw+vTiutlYBsnXNcVHx8fsG6i7JYGnE8N2zawbas7EjHXA3KC8QHN67OnU8PLVw2v3i44nwkv7u/RO0kEmiT1kkiizWMA2wY06thWdUJpgHj1cZEelWGrbBPXJ1SF0TM86wFFhhS2q6xSrjlanyPMtnJVzciUOsZ6tn3TCnc6TU9PT3j//gFj6J5K2/MNi8z7SNV+McpJ6FbjDQBal5Lw2zrAi9hGlljRBgFoYG5pnBug+4gT2On895keK19P9vHNL0p6MRKtHHx85UBIMnbsq4vqbBDOTRwZ0OkNmt5Rg+inVvaiu+qUzIAE3nL6YYXnCPg5cHTo9N4Ym4j15HjquMpn6s1RsHPmuT7ILHtp0/P0HBzle8JJaY5Te9ozZR0h1+DjfnY4sZErzhjJ5vc2YlxzE96vC4cyK/HexOHxxAEtGCzM3v48vtwFA9WInsaejfwspnbdJljnNmaH9mgcdQhpLqYqpgT4ES5h2xn9hF0gxzKb7GxYFknzNV5bt03tQCeAoFmWntZtaGCd3X/vuhe7tyZwqINZeEj+CgQf6Sj7O57UuYCOEeWZMBBJneaQ8aw4GdtWnGdPzcxCOzNihqmAOBtqOvcGA+/nDIYDnr4j5Mrx83XlswRUqLaT5UlAFQ/vZIxS7DH3fL1+TdcXO4tR+dMclUjnyXTcFsuZUqXVsvMXjlpeOQl+GekhFKHRdGVu5O+IrjKfedqWt0HxoMFpBp43EgZfGAqzU8QuFEpag7+eBQ4Vb5P8zIhgVk9PUdVzI2O8CCVTOk2Fp0sz1XzB/7eV2WwEWd82RHkmxmNMHhVM6aBt0lRAgYNSkYfow/Y2ipKYFwSsS8O31TTwNpIxMY8yG0c0P7QD9ZYzqnsbys+Tcz3/UoSu0ZA9n9502KdUU3ciRDX5auxsHExwM1jLa6Movr2BawU3BraV8XTdsF7FYXy8XPHi/h6fPjxKizSwXgcYA9f1gnXbwOsAyQmmeHFesOCCR1xweXgEWFLOSavG+YIiqZplACwujh3pvpwW9FPDul5wd/8KHz7JPhBaOkBiiNydr7g/L+hrw9hkxWvbNpzPC4g3DNq8fV5ldUxNlkM8mIHI6ftRahaPCXcqArypbGy6vIizYAan4k/pfeNRMSC0EiMPKRZCkj79/vMF//mfP+mw1PjgSKsySQHEUfTJXwQz+VmngFT8XHrD5WHDlQi0NDRBFTotYCJo1qwGtBoGb8q7xkuIEvCUPGnW/uwh4+5sACc5VBFq5BqmA48oNuEushslAwWpB4YPEv/N/HB0+dMpuhVOgrw/hu0JzEbrcWuZt/OqFCanSc6LFabNMio7g3MgrsyFVT5MxmKB5EAO8MFv80VEWk9EebZph2lfWO7DyvozEHvBjB+nMef3KDGGcpFX+D32mYMfN1/prRkBNt2urphTALI253gmihMzdkhMPG4wTJPPzJIdo50f6lmecPYznH3vf/qNDE6F3YPU6pXsKsPvKeOZb7dhP3bOxdGzKqUBS8EYoKt6vXd72atoylmEIlO6lRllOYbDgwK5b5fVks4/j22FVEfrvUtFVQ1ojE1WNceW+cLOSdzg2xaYQW3a3zkRj6+HG91T7McTm0PbNTyp2bkNSxEV/m0gjFQYMpQE5MzRpGuKPVlUk84DoFtv9jyXswsy/RaZudORJmc46YEqm34qeOW8FsMqULvOCCG8a+fr9eu4vrwaahJElgJ35NlYIRpLP7AIftCElLwfnPbYOf1OqwMkLFEdBlPOoyhSjEhhHGq0ihJMK2WDHWbbpjPvUdmnf1JxYMr96Q4y7DA3UO+poM3jsPGxCQZKzLTDq+4RlcHCVwWSAUZmpCtSA+W3GdLA2rkYSZa4skrqYOc4mQGRnNm9U5r/tQ3kISjRQhBlm2XXDiNtBs/tZwW/N5x+jmDyyLzBVcaYCdHmMKXKUdAFTTRz9PfPTRMu77pVZEAlY6m0DUS5E8HpNobvnd3WAR7kUVAzFFsHluUEahfctxOoNTmHqjecTx3Xh0c0GlJNlQhaCgB21hS4ATQqbXMUIeDRsF6ATx82nM+M9elJaBsNrGnE21XSll+9WABibFcLLkXqkAxc9sAQSSbDmOd8li0/5URkBYuUEm0Mktuwqk/5sr2RTm6k2xkDNtufIwZKA7OdUtnwcLE0WMIgckPaPsRADePAhkgANgtuKT00AKeFcDrJftXWOgY0KLGxFk5gQA24XDk401PCDtwZs+8jF97IL1ZZl9soYgRhVBTbcHo/z4d8v23M7uT0TnakFCvHbSYUlPMkb/Hw8UU7vkfCV8joKueZuezh30X98xASTHnP3Gxg7yCdET/95Aly5JK3Pp8iJlZ91HRj7tBWUGwst3tVnDvYaSwHw85vzdkVVUjDnY+9eI15d015ZNDmomVUiXNHc940lfk8HnJa9ZzGwQXdybEuTgvtkbmDnx0nJtePeGC+Qs/ecgQqbE0L6cWxFNWJkEqhGgxLY7b+7cgfAOhafdkK8jAzti0WFAqedtFj0vNj5XvTCt8MVgeW1bGVvlqzdFKqpMt1jDzUHkn2QH4+61nAjuERvA+YfULOI2FNJJvQLNA01emUpmJPlLnIHXNOtU1yk13aYkck9cmfpRv9vUnO5Ev0+G0ZI5AYvfy8/r5ef73rFziL8hnOTVUI8gzruTKy14UHy8HzROjLUhkL0d7unKtESKHIzAgXhjRDjpoK/7S8WJqjtF5neeeJCY8M7f3YJ2VxbAf5iOpnKMPQJ5yUVhKwR32Srjx6NS+WqlZeBi3Dlh3raa9P+remeYqWGqZB7flkTMlTyZHXg4bzUZKugPWH6lzGNcA7mhGnw4zgGLsMcWdJItJKKnzRY6Wv+uDPFYrpi/W5e1cVUrI100+KMwtWZAfPvYmoZmfOKWXFneHhGI/thU3wzf2LHpWdvGYE2plYY1thizmPDxeZky6OyuW6YqyEfrpXpS2BiQECFsLrN+/weP0E+oHB/ASAPb0IJCtT5njkVbjBWp0VV5zvz/j3/+Ff4O1LwuV6h7GJy0losiJ26nE4PAinMwHoujrP8IgwNdjZLtkQAaKgUEZNK/uej5h4xmFSwmmSswG2o4nZuFE+aGq9DQw5DmQj8ADGuoEIWNeBhyeJmlsyaN5bJq2owUMA0GA5osxykPZgyBlfBLx+8xrff/cW37xroPWCdZUVTGqCh+B3MabaroANUByTYsyrVZMMHpdpmIwPSnNwyHq0Y0lOco2tP1a8IxzU+p5qCjpixtTbT0THrd3i8Cgv7+SOv299arpc2ddqvChzw0rDBnPZjqHIspQ+kNboVHiySfgMQhNs2bDOczjJ+mZzZr8Nx7u3knRUpsoj58nfSjA6/EX1mLy/MYT0eNaRZDo46xzrKTmJdaV1em7i/xDJBCY9AKzw34QzEwn2Xf+57SdStMcVh2XMOmh3Gmq3x+8a7GYjUDgowg7Gq4q/G7psD0vwQMFflgtZRlKifYozhNnnSZ/VWIQFWddVUjVbm+HaywY71mddY79j74vjbr2uUbW0d/TW0E62P3p4IEgHthuT8UCWAQXVRKARLlEeEkhooiWUGB+InUdKiDaHt3GOQsN7HXN8Ucha/0Y6L7f7srGXVNwbf/uwbkFA0zOO09SWMUv6+Hr9+q4vdhZ7i1eDdlIUUX/wBBNioEOrXcmz/n4PSbgjpGjNlaWsqkUVrNj/w6CBPS/lL2wGu/anoMwC1wqwMKvjRFRSbGJ8SQcmYbpbaQOlvRJcHVjFUZuc5G1skI3UzccHH3OMjQ3+ZKx4VBDkhvZIOT4MLfm8c75MJbvUdAhnRm6qo71ovnevO+4Gi+VBUUXRDme1xgZYlb0p5MCIYduECbXmZaAz6pwGdD7MqYh5jjkokW4uVAAXpxm5z0iv/cpGek1hm9bGoz9NBXUKV4W96Z4GQI52YWi6GlER4My5VH6MwDfQJxi8fVWMvQPUCOu4AAS8eHnCC5zk3FHmqJSChst6AtGCy7bi8fOTpn+eJD30ruPjh0/48OmKpyuBsamleVbcX9EI2JLxLDDJgcdgwtNl4OmBcWovcO5X/On9E1pb8OJO9rAsHaAhSq7xAGEFLXcYg7Guq9BcI6c9PyuvNz3IPs9JOODzxSUiu3/AZcxc6AQ273PBAfaqfNnhN2fD63x1S006S5VZnNA78PnyAb//0w9YlIYGGN1lEjA0GNYa4dQWXMYGs5lbWlVt1NCIsfSGl/dn3J3uZE/qkKgvb1c0rEpfDeJ0pgAeA5HoajQZ2wWMwk1WGue6owF4OlVZeTrALzPS8RQmSwGeUG7jVMt34kO10Izmpj3iR8aWy2UEbeSxEJFUkD2Y+/my94xnsyFuxrI8GGm7YZiOFMAQHDia9ebm3mbpVXESeNinXR+db3fj+8auFswpJpBXxsxYMB0Q1Zgn41t1ZwzCLAI1SMsoEAe/zxeXj6K2zJG2v8tqxuCyRWWew5KN5H9Q3Terc8qqXFwPJ9lq9H+0kj3rnRzoswDi4UWh38f8rsGd3i0BRGS9kIlob+tUJzo7M3u4joMqm6OJJ5z0Hmn5IE0b1XGZfts2edlldDOnscKV5bY5b1ZUpvcOVq9sjOAnUANTC31BJEcCkaTPook9wiOKMLUmvJ6dW7Ppcop2DSDF4MkC+gn2wQMDJCn/EDpsKqPswBOTHTWIOeEfiQ7M8Up4MZmTV6JFHkSb+cgek0Hziq3TdJr3GvwLWen8VjZxkvNoLWWgrfohkgH/z13N/Hr9da4vdhZbn7V+nugwCsJQUodJ9/QI4wLUmgiUsGpNRTsjFMP3hrFhSpWz0DGFOwkauAitQtReA+ArPB495iTUHS5jCFUeh8CZ8ZS4i2Io1qYpBSoOSpECqCIIjpxZ6WYYfbT+PRsREKM9PRgG09zi9D3PRZYqmJneBLjRApf70VQNFDDqYeluTB7BxDGXpJaLFxJKMontHEC9sVuP1OIKxHpUxvRzUfKE/bEADEhBp670sr8CtVX5y99w4W58wPFkPKOgjfIuBy3GsIMmzSBFKqoCodkuOaQxnnWTVS4euDstYHRc16scrdDjYOLeCD9+eMD7j4/4/LBqb9VQqdDY4IWSjR6Y9Sy6dvLjCjy4sg3wkLRJmR8Ox45tvgJPs+Puvds/E61m6GYo52dsFRdz2xT7E22ncVay1jUjnSJn8oItsDLAA+gLAY3x9HjF4A2n3sWJJ/jbVz2HjCB7P7tlUzDUKJBAhMk+ZmAhwv0imxQHJ7XNxhfGhQoTs9v4hUYtCOW8kBCxZ/tqzGbm2xmsgRcgDBMPcJVrTkzN7ZgQM6Q/b4AUeglW8U/rw/DgspYipcydVKuGWWDY92VEm+W/jCqUpYN9Q9/Vdvf6wgzcomluoOLwdgxJv+452e4Wnp9pwEBIbOPPUZ2eQ/ien77ySDJ1U4PZGZw5fIb3toNknG00tcMD17m85YxXZ8sUVhrHc+Od5f10HYi2mw0WW+YAzuPVxuOOftLGN7Slr/aP2Fih1FztmjzZ8VAyO2x1NqExTCzSivmyR96yEwYzMMhXLh0O7dhqKBhF54ESp052g457We/ZmcNVx9RvQQLBR/GZsZbuUwRdcltR+4ETIs32DjkRtq22QOTJMsVpdD6twY7ZRjwiAUoTf1uciRXyM9j86/VXvn7Z0RmUiXkmavm0/cfUZK1uWwEeA0+XFb11SUU7hVYip6/hDJyj9WRn5ZUox0RqvpIWywqy8GReWkTDjWmQmMbeMUXYJieteR/xrBkPt3V7Tp2N1KP6hjnI9lyHqylGed6ek/FHKy5ujPEB+DlAFEcEMNjL/u/2Q6TCHr7KlX62yw5+9euA42WlKp94XAUGp7m15yWyt4FbhiNWkhmRkui0lg+ChkXSEn1yMtRtbjXaJiu6rCsYIgh9DyRrBNnTmrXPEXOfcQpmLJQimyqkbZXT1VCUlfR59P7MUDZcWQDFYEr3vA2bCRXMofTrfBA0rW0AjYT9hUeHpyLyIF3hGVj6CTwGTn3B+dUJ69iwXq949fIevTP+8Z8+4E8/POLpkQFaQDwwcFXAux9g7MwmwMuZW2BQGzi1gUYrluUe339zD3CDbYNmZl2+sGIHUiY9B2ckPXMojuxe5AMXQzGlZoexn7471SQjwdAMJB5QnCc6YQ2jipFCUU6V4cTn62YNIMhe3gGSADcYgzYQM05gvHl5wvsPa/CxTvBylr082yqHWGMM0CAsDZAjATSttAmPn08L/vZ33+J/+Pe/w7g8Yd3kAA4ioBP5sTC2x3kMD5Ul/AlO7OgAd5wauZOTTQcRtVWOxD68IwNzpv9sNSYinlZxJKikxec50X1xnMzJi9WB8luee85dBS2VlXx3GKM/YzyXSC10lFnAxcG2sXGSZUmeww0oTv/p24pAVl7ajYfNkPbebhr+dQUt/W0DJApZVTAF94lzEMBhMqiHizh5MjtaJZU7UFXgO4I5O932PbUb6cEKhxdboYRGnROlb7JiQYZbrWfACUwaDDevkwq4uRqKdJ7u7DwaGLP9wnsaZE4yLK8oZrmfWnXHNTuXOvz4qM6g64zkLBZ45/tUeWcOvM+rqNZJzQKQ97vhHRPcaR84G/9oSrTJWMeP4nkkOC0TCbY3vzcMTmndQ/omEFoHqCs2VV5nfICRT86YVtlMRiVYMTtzUPg2H+dQmRRiYrYe2Z9NTGMPR0Ed6xSMsZkeUtiG0ndjND3zXMxV2eJhDqmgw1Yh572ZRzIkskckcA49Ds4wocFe4yftxy1A1Ydbkm1m63y9fp3Xl1dDXbUsPgAzDgAj+DThg9G6bjBWI5WZcVoWWGrB2OTctJlZKAkRuHNQHZycZSTRolbfz5/WjmhnTesyz4ATv2c4xOlo6X6r/O9ODR0qjWowAaJkGm4zBbMwoFWM5BGGcMGRCipLecp9wJi+RIQ2tEaaXjHk0PAJtvo9VujySqunV83H58XL0t/03cE++EJmeLGe8aaV0JDGVdIpcrNptWcvcK0bEejpHHC5y7Z/IUOWUq3UEbfVaTuwYK8EpBEvcivQlHE6zBz70MSG4jJOuWVjmhT+rWui2yNln1PJSoPKZ908tNaVl0S5ETWc704YPHC9XHC3dHz/7Ts8PnzG+88rHi+bFLliTakhBqiDxgLGFe5om05lqYDHY4A2xrhs+PzpAffnIcqdGNgErN67Vj7MTgRb3XUfRtCafSEp2iJWUKqanOSTBYkmSzU7AGZ4WpELxyV5R9Fxcgos8poN22rUm4IdWFfGeTmBaMP1smJZGr559wL3J+DjVZxeaWZFA+Pdm3vwGPg0Bq6bBODu0HDXN6xjw/W6AW3BUMfq/u6Mf/k33+Jf/e33eP9Pf4gUSZhBtSLLCl9VzIYQxzBt/Ee8Vlffk+GWns+pef4s4tmMUimcUdufDdFhFVInBpnTqqDzWFfzwzg6DPUluVLgnxy0vMq44z0fy9w+Jb0WMoWVliS7ZqDm4ebgR5KFSEU1UETKDhczCEdXXSxQbAMAACAASURBVNVAmiu+8ZLxlY0h3jR4yi3KY9HbCXezb7tztgbf/C1eJvCtAWa4bd703bzabI5iyGOoDskGfKL7nQz5+Zc7hQftOv4dJgOGw+F3uEeFp00wzbYCxMaYJ+nnDMFsgiOyKPOZ8FWyX9h+iwaoklIoTRAs34g3xuANrYuu6osEJsdgrNcrmBmtN9mCozJ/uNPdQJoGPjZW3h+gzrpNSm2fwQA2CUSr3apapOIg2ah5XJTO4L690hy0TIovrxirckmqwlqVRjtyLsm2BMPpZKZAkwJmY5MslW1gU/gbsR5R0/MgIr32AF5LteU8XtOdZg8q/PN+9xi0ycKwTe1+mHJ/Od98vf7bXF/sLCbbyQ1oWcwJY4w0KimVrGwvkRjdERWRsF9rfdfHzgECQLQUYw55BYKScL+5tyiU6MajCDSz96xpNgOPcuEBwNK0cntEmg42jyEALzeHGb3zw+7spfOLKOWPJ+Y0oVEEcerziOHNuTRcCuwh0OZUg9BJKibV0S6GsAI/7yuwPveOyx4luS0xYjtC8arz6OfhGe2kRsje1HGWvU9qVJpTPyl0NmMgGTYR/BDnfnPjZIvImT4zrFoeGGMQwJsbdC5ks/Km8HVcSM6KNuHU4xiZPhAri74GwVnBp7nReSSjE55+TwAQmUMp5xgSBlrrXqKc1xVjMHo7408/fMDTRc7naw1483JFbyd8/rThug4wXeAagoNu0AiXdcVpabh/eYe3717h29+8w/094cMPP6L1xVPTN5ZiOwD0KAtyhZJswsCbGn3Dyrqi0lYOrNy6fsrQm50+u2dOxxibfq97T4qTmQyiZVnwdHkEaw4DeODN6xf4n/7+v8MD/R4//vABnz9/xrffvkVrHZcr4+HzEwgrzgRsvOG6dNz1BQ0DS2fwAgxNt7o+rfhf/7f/iP/4//wX/C//8HdorePh4QG9NZzv7lz2GpxurByEJ8YIRzFWC48drUZ23PTeQZtxOa/OFH4pNF37cSPlwFk72mM46xQ3AA/mfKaD67ZNToEeqD3xdtMKt3NBorV4RvHH5t0n5ePVJWP1Qfi7Oo72lvTFz+JKxpSKcex+hbeXf2dwCcrmUY2bS5N2h3ZgtOS82LiAwClmnM7wER3TkN9zo0BgyooMdV7NXmhJntrzc5G3Gmjct5W/F/hYjGATg3p3ryuhh8fjuE3A5mLSvaq/qOVxA0ir2xEEOk4vPVwdu/Hc/v52+GwG5bkrB33E3Er6fWrE57oBjeUopTEGroAfen93f+e6UPhF0/YZ6A0g6pAK8oxV9dz1STOWmuj3RqTHDS2y5WqBO+EWOFw3OW6Dd96tnAUsGM3yhsIRYw4YoTakKuqxySp3b0Ijy2kBkTjD63YVp3irAUtW/WyrhwyAuozh7l7k+SCxtbcV0AXOGoSY8MzIQXDZ9znAGoRlrd4Nf19oa5LPKtc8rKWwmR4OexIuM79ev87rF6ShEiYRiypb1cglYxBIypI7QxyEOglzzMJ/MgrDIKgM6qtsxzHiQwXTKEWus7NYQKHonjPzT32r5kmvaKNwJrF7briDXVgUHUF1DLEtJRmqGfDdX/trVmTC3NAKshE7bjMSynhjrvysNbZh1vfy9B3Ph/Qn3e0Neo/2Kg15ylvGldGOg2ZjjNUT6yuf5TjLJHPuZlzZarV3uaNFU0gKJ4eirYEHMVqmmKTzgBuLk+NTFCUTZK/TTCy1RekyxdSTcy1Oo83VcPyWPmnTfkypDU0jb2htATDw4/v3+OHjD3j3puNhOeFyWXFaTujU8ATG6ryw6b450mNrRNHeEfDitOA3777Bd+9eg7YVlwsidZNIVQynFfCuhrGmoTI5OeZN9Du6S+jixIe3eOVLVgXye56Cc2Q8p4qlEohgMK8gnCEHUzdsfMX7P33Ew+URdwS8XIB+bnh9IqABnTrWtWF8hqSQEmNpG8ZYcDp1vHwJ9PsTmE94cVrw7t0L/Nt/8w7npWNdV/TexSEkwja2CBQojJ46vAc+FnSLjAhnosgWK32gMFqT+YzGW05kvlfv75+ZAwDHBm2MgbF3FmeacLjtqpEt57LiunGkum5HMLhsKjennuO+yaQw5KOfDNKtwjs7R6b8dUz9kwSXeztUznKei/yfV92sqcBaYtDdo9O3Q53Fh+BTMVSnLIrZYTQoUlMESAYJ2zpzxVjpK+mnmZ4qTJlftE/T7UZ7NkYvLLan3QoNJnpE1aHJADFeqPrtaP5pd+8wO+WGs3lz/MBxdfDds4x6jEx+JXS4KlP/pfWO1uTcXWYpYlMzrSCZMhDnKew50WsnkiOami54WIrqtq3YCBhjQWsW+CPxv1VHdcjxZZWug17rkFtyNAe2TfUA6YrniCB+65J11zSribaB1taKMwKYWBzK1tDPZxCkWNoYko0wWAr2mN1DekxIa4wdpZn9oXNpvxXJQtCzfsmpxQ/jcqXLxWFk/28qNniDXr5ev97ry51FymS0cxPgrOOyWs1TXR0ShjYZzshL+9nIdefAWp0Ob2dOQgEHSmUCK6cFmCNL9h5rQ0fCzYVXKOqywpWNg2y4Tv6cZUz5P5T3rOQeMKWPKJMSFUbzqLpeg6vhMM/M/D4AbJsp+1BKs2NdImMKZGnZcHtoH0Xkfx8pDkU2zyEYXkVRjGuKiVbjswq8SItwgyK1uZ8fjdg7oJRgCAjHkFQQeb4q+IJPGN0JXTYlAOnbHM4wR9nHbvBwPTbGAiKc6CLRsLViY6l41/taKbSZU5WeddfVQVIlMlYQSQrPtjEYQwMhm6TxjYHL9RHf/+6M0//V8P6J8XDd8LANEEv6C3UGbwMNoVDIjrVg4IIOXhn/3x/e48eHJ/zL37zBv/jNC3SNWg6NlFrhGANTdJrMpR0/UejVHZ8UMhqBMQ9A2LPJoAyaz1H/vbGakVayHtIbe2MhLllpUG5bNoyxgtodtu0JvHZso+PDp0c8PDzizx8+43oFLnzCj79/gLnQy6nh+3evAQBPTw94cXcH4oZtbOCx4X454f7+DgsIr89n/O71O7x+0fHn9x/luI5FjJdtbA65HTofmQe+y7fIcTFuRnoneD/jQBGnnzn4E3MRj01GJs0p9Pv0TkD1yPT7swaIOgNuqMN40gzI8nD8yZICL6sLpAY6HUTC5YB22yduYzEjlQFPz20TzVbhmWQpkcsVS0NzHIHLeLMcOAxWTOPK0kj+T3tH6VZaGaoOKrAkdRrP6ue8XWOSaVnn+71n5javKIZjK3Oc+Ts/O2Ha96GZzmZX3jp3iRYLLDRL3gpvm++XZ1rBG00LgwKb4pb5sB9tSN/L21VSG5OumoMpwjv98PlYkQzos221S8eegjDZdvN4swvjZPMkvcvjmNd9TjPVjv1+4rxPlJmw6dFN3Wwcs7mI0BbZu3fXLZApRw6tqwTitjGwDoJkNwnwrTU00sKMiTaGZsmBTP9UOiNAbbqOpcnK47ZtuheWpGorA5drrvEg7TSy40TE4Rt67AetOo62+Uoo9Y5GCxa1b7fNUoxZ6u/xcLw3mBGuuJ90YQN0L6JsHfHfiCTbijlSfXXsXrBmsiGRaWYKlLghfjjvX69fw/ULzlkMoftzptdXsbS8UhUss1JJv03VB2enLf8wq8kcwTTFOr9J+Tnre2d4l4dTkJKn53gSELO4yPwTvxHL/p6dwp1fdOGd+zbpSzg2DvJ4qIBT8GETyThMBShOiSr3W47K4bVHxa6HeJDqfGVFnd9gl3C1ab6Bw6kr1vMJZ6dXPozQlA4Hu9G2p/pwUFyfTdCaw2/R7kzzPp9T9Bt43khycPe2VShb3v+YplouK+ageOy2YucGAoEzXoh1D+OKz0+Mpytj3awQBunB8xyGADOgkVmjmgHgug1ctkc5XYUazqcTPj0+RAoys+x51rO0WFNLOQVVds69zacaA7cKTPj7HHTsuLP2PJJgfWTONn6Ez5tTcFKEharTXBLbcThD3T/ZAz64i3HTF7R2wvff3GEdYkD8+f1nEDWM9Yq7M+HlfQcIeHm6x3npeLo2XFfCCsb93YK7c5MiYgvh6bLi1Jo6PCkFSo0I2zJQxplTFpVgs/HLqOdX2r2EsMCzfveCB/N86G/utt3gg3ke2Nq0/ogKzsNpyXw0tVUCAEeyN/jMcEbp76OLOY07UFH7STI9QVxg8Kk40Df50VvO9NE70Vv0bc0fr4ZPQrLo4+DxMkcT/M9dc59VZ+WL0u8JtsT8roE5tzwLiRtw6DgrqmdZf3vOC6TJgSvN2d9Oh7fbirnJuv7wyZ+ExeRVpAr+DFwg6HhCSn3ugDd/9vwzYgWYZowc83wa2F5ftlb2vYl4U52jOLcVTFu9a9TQukTxzRnry4LGjLZJNsUYGzYagO5dHBiW3boLIBrJFNoDNDiAEvRuBHATmbWw9LVanyYnWY5hAlHUTNPFGsuuIV0+HLx5H72FMxvwzVkIGb6knzzgTm6XzvLFj0Qi8sSRsE9lXmfZZip1N5vHgvLr9Su6fsGexVpohlE38N/aF0R6gmGjvPdFfimEnVYIStTRjuzI1mF0qoLBjNvsgUabYQfSThAepdwMts3v5PIpBL4xP0SQZGlHdd+f+wNA2neV2rDHyF5XHGka5LC9cI4j7KKIR5fjd1ByLg6cCAaA4fnnrpw52LusxnkALASB4/AGULdlAZexRAU7+c0N3BTJZaiQjJGGMMpWlr0zC3UQskEcRqGbrGrYx34Q0TetOCluS6mDZasuuvYlBVpcAFce8XZMuxjYMCNhixv2Tgx3MiPCsAn0i/AeabIZsk+q8FqaF2bbwwZIbNGCO9DVQamy+c//9AHbQji9POO8dazbI54AbFfFXQO2oQ4yLVoJboNZCI0kSNKp4f58j/Nyxo/XFcsieBJOVqdqaEXf1nSVtzoDxsJ2fmMjBvNWjckUWc8Oy9Fqt0+ot2u4ycYfpb9tFgldHZZhK5o6z82AZIhUY8ZYN3ScgL6hEeGyXvF4XXEdDXd3L/F3vyGAGjYGfnx3ks9PD7DCS8SM+1f36B24XO5kZZGv+M3392it43S6w4uXZ3Bb8fHxitZlz3de3dm2IalWPv+Gz5RGquMYTvvq+I+qB9wIdpudYJKWZ1w7uQZH20HcHrQzmeJOFVf5y4hSzVP02nhKjJ5jwSOZLrcDCvl+rlwZ3e3fkUCTceRx2h4glQAt5THwZLgYKseStDMj1+FC+i3LAJOBB/v2E03PmSD2phmLhGT85WmbVK7dm8eZvxWbNTlvhY+YYcV8eMeTqbo1m47Uv13XJwEKq+5oPTrRaN9UCsN4qbeKkoDL6C7RWW5ufqus0HibXJ/kbd8ZTO4fXHz0RWVtemteOTTnJO7PK4ytOgjTGPJM7AI1JnM9CHQDfwTkehb2LDQCI/OdqzBP9kehrRi72UbQlXzW8xFtcaL3gcVsB+2PRywmXNch2nDV84lJnK1Fi+OcFvPOGsYABjdsQxy5bUhqaGtSKtFW6WrAP8ujvGYv+nTpclySkHsDQ1JbB4vdwCxFeNaty99a7GZsSc7Cji1krNcnp8um6O2dfDUUWkDOgobzlZ3EDP/gOMrMA0MUmX8M8qr34siqTEsOaFOnEiPOQo12NPB4g/S/Xn/968vPWUz7Tszpu+UsRiqD5n6TpQHA6Z3SEnc17FGE2NK0GmVhxlyAxhzAJPj0nj+T4ApY90LSvw9lc690aREfs3WSEOD92KHwyHeDWSFPh5O6CxP6HNq8jMEPmxVmJoaWaJ/HUi/mwEsoLz1LkFLH3mkI6jCNA2Y3aHa+V+D3VrDgljSYU2uZwiCjCb4saGpEO6yakhpq+FIFYqttZlD64bsKfdBfjNtWqaQ9n07wxt7tbi6GRCvDeQxU5X2hPH3a39kRdQM9NeLzYDhxAkxTmowZd+ZzBBEpDbrMqyhicdJGRAn1/L4PHz7i48dPePv2jFcvGHy94uOnR4wLg9DBaBi4gpnQGntUV5hoAOgaPR24rAMPDxdc1zssywkAaSRVYL0+XcDMWJYFII0QD5aS6wBY95sAhi9RtkyTQ7gzdOS/XEG5TkrMUSPyVN55rrzjPHdOO+TzNuDhKQDDz06UbYMDDQ3r+oSnpyc8Pl7RTwv+8ffvsfSOpS0gdFzXDdfPKgP7ABGjnTbc9TPevHqBMa7grYPWht5PAIC+ddzddWzjgvVxkz0unnJq+7xbGJSCsBhbkWeK4xbp1paaDZPdsBWexNPJCUkNIwJw8sam1n88q/ysMguZl/NM3JI3lh41zRNPn/HzsXy6vdpl3Ye8qcZ+ZIwcvOUyx/5OEgxHY6uyMP1u+EkzcHyiQwRgXNcioygCWLlEWAa/OpkWoOOi++YVhfm8bjfcuTQc+tHvG28eba+4PR/eHpk8iH5y0Gbkke9gz20lZ+yweN40V7OzmIdpYvaY1HZ0xnMDqb/irE82zG6116uq69E+cz9TH7d1+L5/GRdV2jAStgnfK6X0oH5O9sdzARweA61HYDOmwN7XQFbTUClr2m83fiFc9aiJbZgzdgXzwJUYvTeclwW9i1PUeheXcBOZaWmYwFycMbI0Cr6yjnBVe8XSZEWzW2XXDjQmLL2pfl7AgyQbZ92wbSseHp+wbUOK4airCiK0s3bDYaRsG2NbN+iyKKgBvVnBwIrn1qQf072253YMxqZ2hdlldnTMQh2NCEO3vBj+S0q8yRrjI44+Ys5uz/fX669/fXkaajZ9lThH+rUIGhdoXJjGzjKS37b0NsLxKNEuxrqFEK5OQupuqkLoQPA+5SyPqHzTMUkEJiu4eFa6FzzkJXvrThjrGQMDKHvU9qt9Im158K4MfYbb9tAENImrFdAqSNMo2EyUBAMOmNYFwF9+JbPn4Ld5zNFLqnUS8P4EFK52eg+j0xrhPDaGbi7SFa+c4ouJtliBsbRVfdclnv7p9FyNOCOeoz0jZkQdRXGTd1fGmOnQuDD2L0n3dOD/BLuxt8Ng2TOR+Iz13AoaYeZKhqngkJnx4v4F/tXvfgOmD3j/6QkfPgDbdcHSCaMNrAN4sSx4+ZLw/vMVnYDTSUqcL6cFy9bwzbvX+O3v3uDl/YLffNcxtktK940BtCYV3qSS+Yh5RMLPRGBsfDHhbJYJNPVVn68yqBYzsGmZYNE+eHKY4kcCSPjZYGyNsA1JRV16x/nuHndX4Nwavv3uHa5PV2zrCgaw8YqlCyytL2it49P7RzwuFzwu7yFO6IZ7PuPlqxPuxob1csX2eA/ouZo8GJuvWEs7Rh+Gz5yWmqPMPNGK4dFlekzADfmbcV/nhBMQvhc9ek7twHmVJh6zlRM7A/inbI9dahXtaQbJUYztFBNc2RlQ4z4cSF03dEdcZFgEHqg0McWpdBys74X+80BS2ts+67cjBz14IXq9qRX5oI2wQ+P7rpqtDjRjhWf8zjomzSfnm+k77d/VmY4v+t0kaumFo0iT6T8hH8roTroxNxu4n6/dvTBZSv+zQ7+/pheUGAwexx8lXW3jTb+bvVLmjmy/bvS9W4FM15iyBjK9xLhRbQ3je4IXHHIdkp7ZDZ0hq4t5XNlW3NGxzlly3IV0cgaC9afOHBNADYyRxiKFdVoT+SqZMyT73Hngab2ibSIPe2sg2rzI7HlZ/FQATmMkorKHsozD5s7HsmBlABvQBxBBEcvBA4iuMFVz6uJE9n4nDtwmR4xt69DVThJlLfmhYra4jNR2TW7Q2AWQ0ciD6+67MySjTwYojmKawm0MHzvDZIPai9kJtPdRZVb+/bkAxdfrr3t9+TmLm6UMJJs2/d700FS2ypZKWo3TmWlFoeyFlqXpeVCKIHv79At7tZh0McC0FQPS7QeF0s0Qyimc8sdIzhM1ApqeG8NhjJOlYrGZAfIKNRSBmPRFwY+BnRnDo7PJFTKsDf7p5fmiJA4k8dF+kjzycNqy0uQALr/L8Z4rVZ8nS3GcR541vtwjNw7Iz65kliIv5XWTXSw+WtlTybLqY1jbNHrGzYodmMlAANsRsOw4qwsorQi3iBYDtFVHwUbluDW8ZwNjbCUl16KhkcahjeUAgEVFZ9uDGQzZl1D2Rh4ETGpgI/SEO4dZ+VM+rU2uTecRrHxMAGOokuzgQXh4XHG5Mjoxzp1xfwd88+1LgNSfG5Co5/YEetPw+vVLvH3zEoSBbV3RNsbbty/wN9+/wd1pwTaAz49XDK9OPNLcC68OHtJ44VVRjj4nZvRNQZC9oAie2bZR6CDbNPKmTZTyZ8FvNeD2dhCDtZormfJkABQFUDZJSMQGBqPhtBDevL5HZ8Zvzyc8Pjzicr2iL2d8+kx4O4BtXdGXE86nMz59WrCy7FNcFkJfGpZTx93dHfoQB3TbAFAHkaSy53MjCeRpTIaAss0g0zSzB/ki8DFZ86RtZZ4mKeCQZ2IMC+SgtJ9QV1Z+JqxWnCpDFA5NjRM46B/kZ68B4fjOziu8NTNoKPVhbZtDmmS0yQNrV3VHnN1aqXEbm7J9LWTCOxoO3nbDFPBacztMJXxUoy07sihTtxsXG1y52QkP6rjCSWXGXyIJ5mRMwnWqjc30ctxPijTDNI+WqASfCw44zW0aotGDpCvrmG5FF9J4fV59A1kaZHk9NLrtjw9FOcvtvPocDmxIHtOxo+K42BB58AfBGAZ8n8yBUZ7pjec5zVM6LYmaU5oAUXzZ3GlDMx2V8SOJfAtk2ZwYHAGbqc9h6iBFL0xmpKSt1Gmslzv/ALHP3s5G1KMvmBWsodtJvIJ4tNx0pdZsOrYZM1lpOEnjHUO2SDRNPQ242efIV/v0nywr7ZiQroVy+Dx0zOTHjK3b5g6l8J7ynMOi51ASNLgcjrbtA+ImtNu4wRcqCHWvP4W9JjsC6vE+ho8Bo1GlTV2ZtYJSpbjf1+tXd/3CozOAzOxZhJiQFAKE19kQY9UYP68RVUEHwIUr5buuUGqvOYobL2crJL7GvrvS8k5zchLW9ovJec7d2FOzcpssoVCa7IaB/6bpWKaUSoB10l/TyPxuifjn8ZoRYsZdum/4yJ/6477TSckU1cYJ5snYOoZYjTZVPsSB1MH1backBgbCyI6p5kiBVuUyMLzaoM2i10oqw8yrromOdlqtTsLPCYCZj9+MDrjSzb4JU11Ihpf0basGUqjA9hEe2UaBa6cjU7aKi6LXy5hUaWkJ94wbcBgsaITHTxd8fnoCesfd/R1677h7oYp9dPAAPrz/hI+PAy9enfD29RlvX9+Dt4HLwwV39x13S8f2tOKyAaMTJAPVFGV2VoyvqzHnxkYycrOxPWHG6XtehbBgzBzVzCteeaWZfP9UfY6Q0jhzB8ksMk5DMbDYnVx7+9zkjMITGLg/4XxesJxOWLq0s143NGo4LQvuzoSNB+7uTliWjt4blq57QjmOBxIyznAHnFUWGa3Nad4J5sIOXNqa04/s0+VPvBWy41CiBT5uGvFJQHP61+mcKryy6sEAN2fiBDqAyVF2uktwTU4Ym2xxuZ90QOb5Hdx1bM3lM6Xf9HFzbnIjrnLyyLMMs5+CR0JfWZs7BAYms+DI3U5ABBlTmQ9rqR5rI0Bz8rMS5E4Pt1bvju5bupzRbNa/8U6W6yEHHHQ1kqXg1C0JYrCFXCr4SP1W3o9aAT5pXMece6wj3H8rq4gme3ay62gEySGd+Lq+a23qO07vFuw5Iujci8E4N5z/PHjZaLUi8hC2cN4nHgZ2pl+s1qUje2YIdILCLpX/+XYTYt9TF8Fq/ex5K8ezIzwcq1UMh43Hg3BwZ5Xs6A3d59r6kK0R3bJjBIZFg3KjKz2TrDIyNCBoTp6CqoXwQSMc1CwyM700tbvV3XOxMlqiC286bOp6os3e1nbrc7KJv16/rusX7FnUpX1Pj8yTLDnUEsGVM264CFAYT8BZLMJgewOYNB2OEYnS3k4yjGvDu8tajpUv/eQRApukTQKAIYe2VhmgpdOTVgzlnHZt+krfESxU+nczNBlpRISBrawYlqhMxt2kcq2L+evMiCX9a/rd9vHNz+c02JrCstcdvom6kEY2JEMB5x1IubIWiNzZ2rhG78tqEGzq1LFsssGUJqWYaa3pWEZaZfHqmZpyaOMdkwI9wuMs8Gr6DsozYZDBFV82GuwZC07Ir7aBHMlosY35E/Yn+vMgR1JpHj32d4a3azww612jw2VZ8Oa04G9evwJ4w+VywYcPH9D7AmoLto3xp5cN718C3737Fr0RTucucuPVCd+9ewPZnyEjv1wvWDdXQ2lu2Y0j0y85sayilmPcMTkJ79GGXNO+Eswzu79y5kHu3encZIfRtuMt5Bsf9Otyz2jT91EyTqeTrLYP4P7uLMqfpeCVHOtyCgeDpWjKyQrZQAyNoavqmKK+0vWRkRP3anVkdjxZsGKWPpXeJxz5eOvvzl9tmr9DKoyvLicKbORp+0MjT7k4g9D38DmjLPPB/o4ZRqBsJJMfSm3/Wel44lzBMKPA2suyj/1+GOWG073BNMb+Xn1370hxQnQ47VX2HAUXq9ypV8E1x95rc7YIsfdSn5rYRUNeh/DWe88Zj7t3wWVO5mctc8bk7TyWvD0gVkyCzqOfcIay3jC5tU/dRshtx6tJ4fyMagBDF4dDeMtBvoUPmwsLDNBUXyIHcLLzlceYRxwkwvVRDllQ3tDCLHM7u+twbgMvObNnDhIa7ZqT5jgDtCBhytbhBuR5tTaAQ9zGrE605E6ZUoDOvx/fk8dJJHvDu6z82VnBeVW6tS7prkN4fjh+VQ8bOlhpiOQ5JkS1VN+zKM82HTc1yTDp1HG6U3xqzYuhq9NWNG4M9qI/wa5daPwquNzAYn+Bk/2pD28ETavzeZWgNlIQ1CYyYdk9cZMTlSe+Xr+u68urobpCtZXC/CuDcmlfKDEkg9bSD00xVoPEqh2GwRvRVzOOqzIM4UlAykkPYbZ3cqh3uAbRQ9ybpifl9FKAI8caDSHMbCz66FTB8/Bzlk3JCfQUoZaLmr6HMAAAHrlJREFUaZjCmBlJYZDhBvMeXF5GQZ+bt1HGOCYhzabaLS2IixNZnSIySyfBDFV6M9QzgNVYlbQPE14MblHuP+8HqE3E6qO1ebQqkcs5yxpRw7wZ3VMtQGoItjj4msLZ+qnI7HxvXqWZnii/U1ZMarGEgRHwycMV9tlwsHvDNuSnVbJk6zitF8GfYPHxkxyGTBtw+XTF5bri8ekJnx9WnE7A6zcn3L864ZtvvsNle43vvn2Hh8+Pwu9aOQ6DsK0Djw8rxgCanVnlcOfPWWnXAxy4sFhR2Ym2c/p6nZ+2ZLqPwNQs0+zeoM3ljzjPySkxI4Aq7bkBl3EKW0mpMGtvoqS3DSQVcNzYlECdVA5dlgYaLHLJjQytpqfpR62zB/fCMdw7jQnByC7LvJIkOl8NplK467i9XSBD+7BS8mX0xdg2uVjfTNM8wZdo1BwSnZfRptU/vfKqcmSnZCMmBtecF01n5Oeze0tF17mTMl3FT3N6SS9lvBxc+6Bdrthc27kVKJw9ITFEbXZjdupYguF8/5/em2O5pT99bWg08FhOSrvZeXtu7PN4gocTnQiC5W46i9Cer9tBIo0wZ95kJ9KKiZVMBR5R4AWBkwKnj6fSR9YN8qsIluz8PrfiMgcns6ySvy110uDV7qm+X9u82V2Zf6GZSK+3AJXxUIqRHQaZy0MJR0d6stx3/TfJKjxPG/squyFr5TKZNONcbAE533BL+JSnxzRPstrNYh+3hqWfwMzYrhd11obqgKZ7IRua8sU6VmlH6wREmn0Kqrk925w2hcX02Y2BywarfOp2E2lBmkaghSb6BNZtlbMlt032QDaB0ceWUCILrQMYBKKot57XV/xcatzSA/HOzYpPX69fxUVfuuz7P/+P/72/aHFXMmudwyw6LyfdeCuRFStrXFKVMEWCMoDTvaHliuf72cgGyJ3U29dQwUE6BjN+ooy6p+UxSrXW/CnvhiM1/zYb/zU6Zc8gGevx7OCtOAUlSuv32QVPvX80ZB3TtLkYCD61dqyE/c7BTo5UHuNMRzaGo/v5s2iTjBATTgSPjPIYsU8kRJDDC9i5eiIg53mw+QmjEFqwA3pw/W2YV6WLxvX+fNn7sqp+8Ls6ehlHf+m1N3TEMrCV0CNnXp4VpSQ0mHCTYJrHNXTP8bxi2oi8Yg43gNvA5XLB0/WKcSWMQXh6YGA0bJ0xVtnIuJwI53PD+STnKt6fT2gNGLxCjuwwA81gmWiGalGDfN2itdkJ93sgPeokR51tJbteJrcMH7mPHEwRpzz2xQgdVHhk32f0eWgYWb9k7doxDxK8ENtM9qY8PT6p/Gxe6bT35jJJjFApud5pHx+04mLZ6ToqrV5Np0ozTht71P3kdRTcsD4i2r6Xqfu+VTa5TEFJKc1ODRJN743tfNmKH+tcuO9RjDWb8waqQsJhtD9uu3+W5SCG6I2HDtq96VClOTp0DgOkXQAx6/CjF2banWXN7tJAFRG5QznPG0AHhXJuwEBceEj4OY6AOJzbSXYcwnzDYJWfRabbKpEHSeOBhKLo+0iP1pUyIYqSzUOmm8IhOno/0+xRQODWylm+vAXO/YyYf9/rVtuG434KxKcxlGKDO/hGcSINNgl9zeNNKdrWNp4PKBiM87nRs+0ZWAi54/J8Z+dEaRejP3OihI7l+XW9Yht2xEWM8Xw+SdXTZrYb9F0APTLTmAe261qOjMrXYNajYQTCpgsf43qEC93PmI+mSOftAlK/j0gcyNYa+hLHtQHAtiFV46/zzYY+htghw4RcsiNM8O3NvEnOEP7v//zjwRi+Xv81L77++S9W1b9gz2Lq2BQxKJwO/XfYWWcWMzPmRzhl9kZeMZqFYXGYgJsa14zA57RtJU6Dx5h3b7hQyIBkeJPDYHtNWm+lTfdlKIwVe82FgkLjhVBc+SdF6hgKQYl8N53795zz4c6LP5IFYW22tEOSVGzOSPQbzRDq3r/czk2jA3AFsjMGqbq8BIryqAD8ePASkQ/l2nu/rSyLwaj36PgZm28/6kKFImGP60wbs0ORf9/B8YWXjTkAi0+j20zD+3ftlWrBHhk3+ou/Z+W111UDGq1hu3RcHi64XiVV++myYQxg5RXnfsbptABgrOvA3anDz5Vi4YhbtOsKiRNfaf/PSbwjXIf8AWyPBaemJRK6D7cYT8anIy/FO46djnjWOJ+f5wl9iSFFFSwyTI0lYKK/Wbvnuzt5xQtnqQGXZOYcSMgQ+b9G4jzP+wF42NPM88/v52puwyERCzLg8rmrsmW+b+3tjWbvLU+B/+bvc82CqG2Ixpp/F4M38YXkoh608Iw+msfl//z0dWSERztHWJ/4AqhnA994btcy12d2jtf8qjuG8dPMFyW9fHK0nneIpVXRUUkouF2SnNpdA9P9/R8o2nrMMFY9/ZN8bU+bM4vsnCSc5KO/dkyyx9OtVbtDZ/JgeuYfZAorYdSmTd+zyyp7SOZMx5AaPObLqm+srVQ+0fHE0xhAxn9ApXP2dwzWaZRlHAGLSUMPQexl3JQxQN7GpPN7l2PeWg0gelCJdR/5iN9Iz6YwWxGN0EjPdxw2z9F3g9TVYJbFGHunjCwJvaa/+VYKtdtkmEp/KwM00ESto5md3jTIDAk0u9zLRu4OrYxYSgrsz/IuOP9nCr2v11/l+vI0VI1okIYHmKpgMbWweV6/nuOVDKucZiopSfF98hQQpk91eLKR4O+GDN8pGnNEsgLIn3awqjVuUfn8ip0oGQ6KpDK21nftVYNe/pYVMm3QhCipA2S1MwgpIysUQTge8PbDoQ6Eya0qQC2PHKTFUfJxHDDBwUW5eou+l6i2CRU28ltOwpkei9Efr6QQfmYWAntPRyuoP0dpzp5T2uo2Kd/kZLU8B/ZOSG4PAjRKNHhbOT63Cv3c/fl3m/9Zkbsauxk8qMpZvziQon9p+j2e2zZWOtJZGITz6YRTb9g0Kr3pataJXuDUz2AG1nXFul5wf7eg9e7yoelRJ0FLDpHyYFxzSfc93WNHgm6zRKvK0zQZXzNOtDnK+E7Pgidyyo55ciTte1GbudMKq0WsG3U1uqARW8L1soG6rXgNXUWEGwPWE2NIVVoVJL03rei3G1wdI1R6T05FAdF/q3L0FgvPBsLc8N5Zqi8E76S+uMKR56UGPKb2/L0wXMJQD0fIV+0nGI7gcLnk8iMPkmYyk/ccdREw5ME1C/wIccWJCF3FCXaofsYETy55D5PXNybt+RUbHxmyDuDpM3qKf6w6rKltnaWd/OPU0PNBNk5jS06Jf8QKEZuMs7ll0YyFNjDjlw9+O5bRdpahOQ9234uL8MHKp+oad9DcCU1bORBpqhW5OjYvCzrrfMj+XU4xjEm+SZsTH7ONPGyxY3qQ1bYgZZ0LzdgpMu7Q8a8y0XgoUXaau1t0EPov3Eqkz7oIEfIFpRf7KDZKypRzOTKN31fGE6/2ZZHZUFkztAr6GAObpnjaPmcqcmlgHVJkiXS1rzU5f3GM4VVUB9WgtMkq3w/Pma1tLFrHgYZjNWSkpe3Ly7IiytAP3Xph2XHSclO7XVL5bY8TUIgL+xMLjnQ0Ke3fsla+Xn/968tXFpUxnK05CTN5ADL9lWTtjiUaGAHSJDTmdD35PAZFeJ7jS1bq6V5ZpSwUawrEOjFBn5QZJefW3pyK+9RIY4Gg/CbfLRXQjBb9Pu0BOkotMWckhCdNw56U3HzNFkIRtNHubqUkCdKbUc8J1vRFoCLatztBfdCQRr9tjg1OV8tTn3UvWFxqUIY1qHdjbP7kHLDgI3xZqwFJVYOYxlojnjOO/5KVxvz8frVIiYOCL/NzOxzz/msYmtO72WBhS3OUt1qXFd0BS6eSvRkvljMA4LoO9E5Y+oLWGxoBG0uUEtxr1P4IzhsCIEHpCudZVFrVRqq8tkPHZNSImBO5dwhJbm+yQQw6C4pkw/4m3SPm2fabMuRIEjmEWQJPm89zTrOOIJSxe3Wuov1jej4qs2Joyb/M8mmC3/8pttThs/t7+4lxfUMVDuN73GifAd+nFY5hiP14j3aTcSSvbgWjYoyTbOMMv8Gk8ke8BA+4MieEzcRRxqytpOBm1THRk4+zGP/7MfpYbqmOCefHgazKJzxNhOsZk18/o5/57yNZOXAQCMkOWQl+TfNnz2Juf5bo+Tebr3SvzHHQRA7u3ULuTef8xm+BP/YmTZcBMuEjyxnOgRC7Zy/Ejbmn52XpbdjZ5BwSCXOA51ci23y7pI/eDHpGAzPeb8P8jPyasmwqfNMcVzIoNgdRi2kg8hRjo0WGrFITyLe/SDuWwwWp2yD7Y0CkqavZqXc+i1oek8mVAMzvNrfCrQp/FiGOao5tAGOIneuFa0ByPFkj+D5gEWIHuDEETVbxhOqvjuKv+/pFaaj7dCtjlKQw2YxLWTL3ZXJmDJDuFYNW66sKi4wo5YWkhG+aMWjUCpPYKwQSpVzgj7TRqhym9ERzFNXKKCktbPv7AGbdUOx9InmWuW1hXjNaWAXKGN6qPheKKwt5yxlnwM+xzA6dPRdjk7+6Haw6C0hEiQZ7dlAI+dmQiPuRLGK/lXY5RVFLW/s5NPuEeUgwajbMjLZK6j7tBE4gQZ8vCt6ig/IqkR2CQGFUOPJCxTEzenZICVrMYO4yHxCsibKTMrVV3G2zs/ekv7/EUfQup3nJc5UV11GqWLmK80NaSXaiEd2nY8wt+2SBvPdxXYF1JayXVfc3AGDGdrdijFUjpR2EM3gQBg0VBw28cVjz0xWb+mOsBbZE4zZzh8GO+hKiIEiiFZoeSTLInFlCzTR0luKkSEeFeXYUHNrUn9kLeRVqQKLQYzCW3oQ3GkmRhd7QT4v03TrWdVXDgeRcN7iZgNm5crCyIV4KOY4jUbFj3TAyLAX7hnQ2e6EYM7l41h6uLNfyfEpzVNrw8R10LltrqcBuOCZHUFz7PavGP/O9EPLVoUEZrM/7PMakC6WVsdsTywBoCqLURmKlZG4v+tXiProqUCqi2ruzs5hs0ugq6SO9ZxVBY7Uf4eNOjsdRoPAoGHprnOXrEUvzwdqE0SOJMRvHeESIz4MxSW8cX6G3QeTbT4hSBV1Qwi/7fM4Ob+j0uJ/n3ug/6ySTawfocBwV/E1kZ/J4JvoCR9YVmfcQOI8tudkOivGbjcIwJyPEYx6v6WTf4pHoJY9Vhh/HRQn6szDJp7Em2i7qJOF7IpEjuI50B6XxhtwK2vf1awL4egWYNJgaRWhaa2AC1rFhjA00pBaN4M0K0ch2jaEZOuvYAKidp0ciLaOD2YqY6YogiVMZCoQTXJSy/0TvDbWrrLiMPKMBTZ1Hs49OmZZMN43hdQugz4kNkpTARCuO4xv66Ov1673+q+xZfP4SwrU8aRrZQFPmajSV4E6EREkAMtDbYm/pVbYgw/bvZYUYkRJ42wzWIjhHCkLEgTGNHApl47BiOKYUINWgFNYioWefiI73gh3tfSPSzJIMlUo/N9BUac175Oq1d8yinwJa6cejyy4I5J+y4OZ/pxk4HMsBbNkod8UUAt3fS0orIHEL1VOaOKcm5tUSt+eowO0fSfr53FC0Y7gYWy70khTDPKwCqylOLkbts7jhZOSlZ54TrCLzc2GmVFlUf3uORijjVBs0JTinTFnBFAAYaMBmkUlC15Z6Y4zGaEtHXxpaO+Hp6QF9ET5aLxsWLApXFN6RFMk4/HfmW8GHKsibuAgeuc0TVXUdpXZHg3U+LG4xy6mw6QzuMPKMJMPBOQCijCH6yM7YGLIvulMDs67oEmHoGWAPj49Yupy9SMuC1shxJWnPDVal2gZnsOQxuYzh+oz+soPVaHyHO5hhp3hkLnPiuPqJYmRkFtjuPTiPHok4T892IzS3kdtNq0P27DyP3p5OjtnbjVA2M2ZPX9u1gi5zDeejlbn9PTmi/KBmTvQxLPlulsb2hMDpRxLB5FH8PmcO7NrIzg7SeK1Pyp/R9pGzPzt1t1YRp1EiU98sT0VXhDHvMJCHehxWX3HPMA1py1K5BQ9BV0YfHsSAHj0AVoO56vVbzrClE8JGk3RMdlrsb3O2HAs8t1nTrzMfzGxFQfQFs7cuC14zOAqUAJMMMP4JW4u5lXvGLjf7SUKIEXw2cvDVHBGTxtqm2ZUihzIucvA3xrrjL61sbDRj7966mFEyYGLhIGSHp2aT7DmH7u+jRn6+aGuE3k/AxhhbpKg6fM3OUGwSlIBk4KybnLPY0dBaw7KcyrsbRwFItVLNAHK+70ToRKB0xInPUZERihGSrCHXZeYqqygc2xA6NX5km6k67+a0HhEDEXnGzdfr13l9sbM4Rz+NykzohVAHAD0KAmaYQoV1ep+CYE2xNzWGYjGcAC+1YuoxCfcbVtgc9a0GSjUUY4AhFKthGaP2VRUzznUg+ZnSwNRnEn2wVFxj6GiwGkkxEGv9YO+bM21VBTtDTr/b3DjEFBG8ihAugsVS1SruNEJlgsT2Z3IejzZOWTkEukAmfHcoQHh/qHvN3VipFU9BeRzZPNJx2vcyWC2UkNvXQ9JDCB/AhmrszU5fubIxkY1hyqvIx3Q8U7nZp0xAQziKBACNMLjpZvjcWDQydC5cHc8GCatrTCS0bhjQR7ZtYGySGdBax2DdW7FesZwWrNcV6/aE82mRSnDeJiDFjRnMsg8vIE/7UBJxhBw4oPcJvxGAirmYi2g43bCkxNrcVtILg2hO0Ys5Ok7Jc34w4PPrz+hFk5t5GoYw1J6PddG3967l0Zvyngo7NuNp28u5DGeiw+gjjLQjsAkhU7PjCSiJp/FmngxBXOWHG4KG5/RbzTaAj62k3RYY2YHIEtf4jbyhPKDcB5vNp+KKyzPKNtJq5jmQprORs4rLDOSjFwwP9g6ANqf/qlYg/wvlNZfEjpQYvcLZQH7S36yS/O8yCSbnKyQlvWy6nl8pnAIOpPtpj+RcZe3pyhyZjQfj10lmkWQTuTOW4Ng5BBQreZWWZ75uYOhqjqSYVLvmBux5pS5Gw7HVKzlW1g7XBqYxzkzzbPcVBp5f4F1fiWmdVrPEdT5FrjZqdsPwoEOFSzJSqo10ALvKPqppREmGy7eolFzbmaufVuI7mPsk+2Ybct/UbQw7vEp7uwZUJjOlA+5dfhFa0+CSjXRTem+mQRQnzTLdojKvNBT6y/plmB7Q9w0+TnOezKLYJqHv2txv7GfX0jQ2RgRnIzBY7TPvyHQMR/kks6W+Ooq/7uuLnUWJ/HAINSd8+W9o4YsBPbSYEvGY8iRVcxTEGe0xwA0DhGYMpVE5KQCwhRGmAk3O55NQHCGXfg/GYbAb1K52tOOSAmLCwqOG8jal9qA53GxnuO2cwSrcJXMiG7b5OTiCIoJpP6pgofjNxKbdy6Lc0w44wwmMtKqbV4t672VuZf4qhCZMbeN6Hl3PApihZxdaP2YYyUqsR56VWGZlaW0wMwZ0o3fCUx1XmgsF0sZuAs9jk0XI2+qBKDtXbBzP+qqCCkErUy0r0sk48LmLvysdxUG5gApuhu01l/OZAE2vtUbMiBY4mhskaSyM4vwNbasbPScFUO0KQ4zRu6aesG5YZ4hSUOO0Gb57c8NbOlSebpYeNbCODV1Cj3rO34oxGNerKIeVNhATzqcO1irJ6yYpXMyMFy/vECukGeaYGwJ52nc+K/AoZUs23ufS7QA3SZ8Vums6lwLfoOFHqDCS4qSURu0GSqx+E+xIrEIEHhk3qVHO8LZfdgakDcgUbkykRJDTs1qU4Pr0BAawqLNI1LCtgl87W8umrqsjmXxcEKkM0zNkPaBPViADxVH5/9s7uyW5URgKH2HPVuX93zYZo71Av+DObu1VqvZ8qdTMdNsYBAhJYNjteQ9uaWl48Qzt9aduRagbP11nKh57oB2D8OTNGYTxO2q/yNoRG1BCpzYH351KL6u2/tHakXVstQKp6lrqKYDMNeN3x6xeyga6ZhPzRN6UdY51GvlsF3nbMwGaDbhmJ8Xq3P5V3aKqGBNh0AFlcZ7dL9VA++DcAbm01Ipd9H0aiTESqh5n9nWnXqOsdblwjg+CfdytAdSYsSqfN4FaX4N2h/QYW0v7G74pRxd7XBTllzX+DwsUrvfJxtJdUECvVMIxY2/6pubV247Lxh3SaXrMpo1djFI2tPKgSVtoWaIwWsqt0UZ6//L2G/83EQLrHFJYWxshEAkNO1TwuN2mq3wesIh5ZKuvOSduCwpWlSdYO1/nGdJZJ73OPLCZF0afsfz5Rk1tRRQ8X2XmVs9WU5+5xNtnutssdTxfsSe02yxVP3l9DfFVZxL5g8qyj70+zMe8bNPDny7RR0OeabBkJYVdIDPlXNqx6GW7pf5a7Xa6XTTxAEuXuU5yo++S2OTG5SOAHeGr8RwvebzGA8V46aEp1K2twG25GTG1z+uFyJ/Af98NdW4NQ6PdxXuIvq3vBCLKH0dEGGGzWMeJVE1BTmi++6AK1WF6OYdcXxu+Ijq2vPRlHN4JJRp/p0JoM0S6DKq3BNQcjkMP+wBmGrMNXWWA62npluGyxEplW4tkXdaWfBwO157P0sXrcqC2XGN/bstMGg9AMQj0LHuL4KLXscgsBuMmsxfFvhsOaudrrc98yXHO+AFLjk95/imPrc77R0upVQsDVVbuwLo8M2LtVbo7i9UoDn/GNOPj5ZtlCY2Xb7hhEQf19XblMvPB2wY+f93czWevA49YDoWle5j9nhuY3fEbmWh8cV0DIl+49IZca8gYIrhsF7f7XjudjrEOks+ZoJWOiC//Ku1T9qcjI5eeHxvgYjiPNqvFYHKZlpfw7b0t0Qn1mdI3ZaHHL+26OtNbHYzuNLqewUmJvp89zT58MYRroEen4pmzvbsslp/5uMMokCt3m975NMSfWsHbVO3TQK2uMCDVZl5CNl4vs8n1NBRLgprR7PX7apWH87LXR/GEI3lPpqbXCqnof2r/GWVCkaP3PeQu33ZOon+eS4/Pay2FkE271p7d24Vp8U3X+ofrc0mHzK9xkYfyOd+h8893Z6J2w9QB5fl+Vh5qXaPplvgO+rEN+j19Vc32fUnjzXl8m62qz98S+kfaM1p91gR6Ql4f0ur4xNvCyvc5o9We4J2qpPfmOr89MXX2bwosWdamu4AuOe2BAKk/q8qrY/Hr1QJ/z3MPlr0tTz7UsiK9xNZmNyXufWBL4NVO2mS88x6seEvD6zIUTpQhXmuKFrz8sznt+CkLpnvgOI6S02fZ21Px/XwDeIDrC2pjrNtrYRaLi8fCvVKCBvtYpr2O1u7mtQ3neF1Mnq05Lb0+zRbP1Ep9voyvTfXWRkT+SP77zOL2d31nYdW7NCVYDSvp/QjiYaJha90tTT94VHyCQNZMh0DzkNLojKsFD7m7EySfm2BxTaODtDJFbma+l7hy1sqtUMgoy9i8028R1j3taszEbEdNV+pAPLdZzprDeQzWWl52yEikvDu9KZCQSM5serovl+ubUZh1hZn+jT87jMsX5eFKaH92laE0TVU3FijXtFmmlwGsfCelDtIukPL8okplKd+URzauaPM4B0ANIdh9CjtJRjA0I8516V2YJLIOsQ7Hszj3IjmETau7qXNdD58BBeaw5VhSzAbT6+velV7Mci5rE6LAJSsaPJ/1pMf6IJ41wA3Pr0hcC6x35O5hQ+PXDQgwvx8zLh87WF5j6aSMgfk8eQ7VFjipMqpReh+Y01EfVkbfb9nfk6gOsO0GJ1f039e4pgKqEptctRl5n+Kzuo3+5lPGsHpaUzrpaMRh992AO2d6PLdPy1B9b05kYNwDP64faxfaMu04/voC8GV3aXTgZXTM2MTIZ/8VGhZN9vV+REmrCy97WUVQ/GW0ykPRhXqt8yJLHfr34d8X4yYO9cbM3zUOSUA6kVnGlfNqsCGClBIzgVZcn1ms/djLuOtvd/hUMRR2hpp/v272731mZj1Em3ysOZVldNqeo60c/gXSsCtjqZdD4pM00ELGmsvJ6qZb+7MjXdOz+R6dZ0Hj5zn+o8vipWy7XPc2/2nZfRVBzUsEUqWX+X05uOfz/btPeTgDanvZPqWX7XU3rnO8WfefpczP2ix3Nk7EaxJvdVnTj3t+L5vdTgvfYO8Xm/kRq3g0++O63do8UBLwek+hqGquYloDHHz1UA3e90hAuaE3UAAas+j/xgmpdqnr+xykO92p72lEnw859vRLrtsfY4yc4XsmIvBscl3XXLjvdSbxrTfm/Mb8Br6fiV8/fwFDMca9xoNrADpNz39bxif0AobcKwA91/nHbZGbt4/4JG2RFnj19lDtOoWNpesy35hnH1H9yLm1UstVoyJfbSF/MvJxNooQQgghhBBCyP+WfYM2QgghhBBCCCGEziIhhBBCCCGEkBM6i4QQQgghhBBCDugsEkIIIYQQQgg5oLNICCGEEEIIIeSAziIhhBBCCCGEkAM6i4QQQgghhBBCDugsEkIIIYQQQgg5oLNICCGEEEIIIeSAziIhhBBCCCGEkAM6i4QQQgghhBBCDugsEkIIIYQQQgg5oLNICCGEEEIIIeTgb5c4pZI6VuenAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import random\n", + "import colorsys\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as patches\n", + "\n", + "\n", + "set_seed(1)\n", + "\n", + "def get_ax(rows=1, cols=1, size=16):\n", + " \"\"\"\n", + " Set axis\n", + "\n", + " Return a Matplotlib Axes array to be used in all visualizations in the notebook. Provide a central\n", + " point to control graph sizes.\n", + " Adjust the size attribute to control how big to render images.\n", + "\n", + " Args:\n", + " rows(int): row size. default: 1.\n", + " cols(int): column size. default: 1.\n", + " size(int): pixel size. default: 16.\n", + "\n", + " Returns:\n", + " Array, array of Axes\n", + " \"\"\"\n", + " _, axis = plt.subplots(rows, cols, figsize=(size*cols, size*rows))\n", + " return axis\n", + "\n", + "def mindrecord_to_rgb(img_data):\n", + " \"\"\"\n", + " Returns a RGB image from evaluated results.\n", + " Args:\n", + " rows(Array): a image.\n", + "\n", + " Returns:\n", + " Array, a RGB image.\n", + " \"\"\"\n", + " index = 0\n", + " convert_img = (-np.min(img_data[index, :, :, :])+img_data[index, :, :, :]) *\\\n", + " 255/(np.max(img_data[index, :, :, :])-np.min(img_data[index, :, :, :]))\n", + " temp_img = convert_img.astype(np.uint8)\n", + " image = np.zeros([config.img_height, config.img_width, 3])\n", + " image[:, :, 0] = temp_img[0, :, :]\n", + " image[:, :, 1] = temp_img[1, :, :]\n", + " image[:, :, 2] = temp_img[2, :, :]\n", + " return image\n", + "\n", + "def random_colors(num, bright=True):\n", + " \"\"\"\n", + " Generate random colors.\n", + "\n", + " To get visually distinct colors, generate them in HSV space then\n", + " convert to RGB.\n", + "\n", + " Args:\n", + " num(int): the color number.\n", + "\n", + " Returns:\n", + " List, a list of different colors.\n", + " \"\"\"\n", + " brightness = 1.0 if bright else 0.7\n", + " hsv = [(i / num, 1, brightness) for i in range(num)]\n", + " colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))\n", + " random.shuffle(colors)\n", + " return colors\n", + "\n", + "def infer():\n", + " \"\"\"\n", + " Return Mask RCNN evaluated results.\n", + "\n", + " Returns:\n", + " output, Mask RCNN evaluated result.\n", + " [Tensor[2,80000,5],\n", + " Tensor[2,80000,1],\n", + " Tensor[2,80000,1]\n", + " Tensor[2,80000,28,28]]\n", + " img, RGB image, (height, width, 3)\n", + " \"\"\"\n", + " # load image\n", + " device_target = config.device_target\n", + " context.set_context(mode=context.GRAPH_MODE, device_target=device_target)\n", + "\n", + " mindrecord_dir = os.path.join(config.data_root, config.mindrecord_dir)\n", + "\n", + " prefix = \"MaskRcnn_eval.mindrecord\"\n", + "\n", + " mindrecord_file = os.path.join(mindrecord_dir, prefix)\n", + "\n", + " dataset = create_coco_dataset(mindrecord_file, batch_size=config.test_batch_size, is_training=False)\n", + "\n", + " total = dataset.get_dataset_size()\n", + " image_id = np.random.choice(total, 1)\n", + "\n", + " # load model\n", + " ckpt_path = config.checkpoint_path\n", + " net = MaskRcnnResnet50(config)\n", + " param_dict = load_checkpoint(ckpt_path)\n", + " load_param_into_net(net, param_dict)\n", + " net.set_train(False)\n", + "\n", + " data = list(dataset.create_dict_iterator(output_numpy=True, num_epochs=1))[image_id[0]]\n", + " print(\"Image ID: \", image_id[0])\n", + " img_data = data['image']\n", + " img_metas = data['image_shape']\n", + " gt_bboxes = data['box']\n", + " gt_labels = data['label']\n", + " gt_num = data['valid_num']\n", + " gt_mask = data[\"mask\"]\n", + "\n", + " img = mindrecord_to_rgb(img_data)\n", + "\n", + " start = time.time()\n", + " # run net\n", + " output = net(Tensor(img_data), Tensor(img_metas), Tensor(gt_bboxes),\n", + " Tensor(gt_labels), Tensor(gt_num), Tensor(gt_mask))\n", + " end = time.time()\n", + " print(\"Cost time of detection: {:.2f}\".format(end - start))\n", + " return output, img, img_metas\n", + "\n", + "def detection(output, img, img_metas):\n", + " \"\"\"Mask RCNN Detection.\n", + " Arg:\n", + " output, evaluated results by Mask RCNN.\n", + " [Tensor[2,80000,5],\n", + " Tensor[2,80000,1],\n", + " Tensor[2,80000,1]\n", + " Tensor[2,80000,28,28]]\n", + " img, RGB image.\n", + " img_metas, image shape.\n", + " \"\"\"\n", + " # scaling ratio\n", + " ratio = img_metas[0, 2]\n", + "\n", + " # output\n", + " all_bbox = output[0][0].asnumpy()\n", + " all_label = output[1][0].asnumpy()\n", + " all_mask = output[2][0].asnumpy()\n", + "\n", + " num = 0\n", + " mask_id = -1\n", + " type_ids = []\n", + " for bool_ in all_mask:\n", + " mask_id += 1\n", + " if np.equal(bool_, True) and all_bbox[mask_id, 4] > 0.8:\n", + " type_ids.append(mask_id)\n", + " num += 1\n", + " print(\"Class Num:\", num)\n", + "\n", + " # Generate random colors\n", + " colors = random_colors(num)\n", + "\n", + " # Show area outside image boundaries.\n", + " height = config.img_height\n", + " width = config.img_width\n", + " ax = get_ax(1)\n", + " ax.set_ylim(height + 10, -10)\n", + " ax.set_xlim(-10, width + 10)\n", + " ax.axis('off')\n", + " ax.set_title(\"Precision\")\n", + "\n", + " masked_image = img.astype(np.uint32).copy()\n", + " for j in range(num):\n", + " color = colors[j]\n", + " i = type_ids[j]\n", + " # Bounding box\n", + "\n", + " x1, y1, x2, y2, _ = all_bbox[i]*ratio\n", + " score = all_bbox[i, 4]\n", + "\n", + " p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2, alpha=0.7,\n", + " linestyle=\"dashed\", edgecolor=color, facecolor='none')\n", + " ax.add_patch(p)\n", + "\n", + " # Label\n", + " class_names = config.data_classes\n", + " class_id = all_label[i, 0].astype(np.uint8)+1\n", + " score = all_bbox[i, 4]\n", + " label = class_names[class_id]\n", + "\n", + " caption = \"{} {:.3f}\".format(label, score)\n", + " ax.text(x1, y1 + 8, caption, color='w', size=11, backgroundcolor=\"none\")\n", + "\n", + " ax.imshow(masked_image.astype(np.uint8))\n", + " plt.show()\n", + "\n", + "if __name__ == '__main__':\n", + " out, img_rgb, img_shape = infer()\n", + " detection(out, img_rgb, img_shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 参考文献\n", + "\n", + "[1] He K, Gkioxari G, Dollár P, et al. Mask r-cnn[C]//Proceedings of the IEEE international conference on computer vision. 2017: 2961-2969." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "MindSpore", + "language": "python", + "name": "mindspore" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + }, + "vscode": { + "interpreter": { + "hash": "b56013b6fb53d0f81239d581af87f416e933cb7b39a0273dacec5d2d78018631" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/application_example/maskrcnn/src/maskrcnn.md b/application_example/maskrcnn/src/maskrcnn.md new file mode 100644 index 0000000000000000000000000000000000000000..a6f7d92f976d996f53064677add1543d78d8509b --- /dev/null +++ b/application_example/maskrcnn/src/maskrcnn.md @@ -0,0 +1,31 @@ +. +└─maskrcnn + ├─src + ├─dataset + ├─dataset.py + ├─model + ├─anchor_generator.py + ├─bbox_assign_sample_stage2.py + ├─bbox_assign_sample.py + ├─fpn_neck.py + ├─mask_rcnn_mobilenetv1.py + ├─mask_rcnn_r50.py + ├─mobilenetv1.py + ├─proposal_generator.py + ├─rcnn_cls.py + ├─rcnn_mask.py + ├─resnet50.py + ├─roi_align.py + ├─rpn.py + ├─utils + ├─config.py + ├─lr_schedule.py + ├─network_define.py + ├─util.py + ├─eval.py + ├─infer.py + ├─train.py + ├─README.md + ├─maskrcnn.ipynb + ├─dataset.md + └─requirements.txt diff --git a/application_example/maskrcnn/src/train.py b/application_example/maskrcnn/src/train.py index beead122f1a2b02e4014953b61ecb83b9baef9df..c215e9380c7e51fa228828b5eaf2e9c0b0df9d72 100644 --- a/application_example/maskrcnn/src/train.py +++ b/application_example/maskrcnn/src/train.py @@ -25,12 +25,12 @@ from mindspore.train.serialization import load_checkpoint, load_param_into_net from mindspore.nn import Momentum from mindspore.common import set_seed +from utils.config import config # when use maskrcnn mobilenetv1, just change the following backbone and defined network # from mask_rcnn_mobilenetv1 and network_define_maskrcnnmobilenetv1 from model.mask_rcnn_r50 import MaskRcnnResnet50 from utils.network_define import LossCallBack, WithLossCell, TrainOneStepCell, LossNet from utils.lr_schedule import dynamic_lr -from utils.config import config from dataset.dataset import create_coco_dataset, data_to_mindrecord_byte_image @@ -110,7 +110,7 @@ def train_maskrcnn(): # It will generate mindrecord file in config.mindrecord_dir, # and the file name is MaskRcnn.mindrecord0, 1, ... file_num. prefix = "MaskRcnn.mindrecord" - mindrecord_dir = config.mindrecord_dir + mindrecord_dir = os.path.join(config.data_root, config.mindrecord_dir) mindrecord_file = os.path.join(mindrecord_dir, prefix + "0") if rank == 0 and not os.path.exists(mindrecord_file): create_mindrecord_dir(prefix, mindrecord_dir) @@ -149,14 +149,14 @@ def train_maskrcnn(): if config.save_checkpoint: # set saved weights. ckpt_step = config.save_checkpoint_epochs * dataset_size - ckptconfig = CheckpointConfig(save_checkpoint_steps=ckpt_step, keep_checkpoint_max=config.keep_checkpoint_max) + ckptconfig = CheckpointConfig(save_checkpoint_steps=5000, keep_checkpoint_max=config.keep_checkpoint_max) save_checkpoint_path = os.path.join(config.save_checkpoint_path, 'ckpt_' + str(rank) + '/') # apply saved weights. ckpoint_cb = ModelCheckpoint(prefix='mask_rcnn', directory=save_checkpoint_path, config=ckptconfig) cb += [ckpoint_cb] # start training. model = Model(net) - model.train(config.epoch_size, dataset, callbacks=cb, dataset_sink_mode=dataset_sink_mode_flag) + model.train(config.epoch_size, dataset, callbacks=cb, dataset_sink_mode=False) if __name__ == '__main__': train_maskrcnn() diff --git a/application_example/maskrcnn/src/utils/__init__.py b/application_example/maskrcnn/src/utils/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/application_example/maskrcnn/src/utils/config.py b/application_example/maskrcnn/src/utils/config.py index e2daf39f5aefd94eddb93b768e11a2a29474d9d2..3cc7edd0ab66913aaa264cc90e345e954d1fea5f 100644 --- a/application_example/maskrcnn/src/utils/config.py +++ b/application_example/maskrcnn/src/utils/config.py @@ -28,7 +28,7 @@ def parse_args(): parsed parameters. """ - parser = argparse.ArgumentParser()#(description='config') + parser = argparse.ArgumentParser() # Device type parser.add_argument('--device_target', default='Ascend', choices=['CPU', 'GPU', 'Ascend'], type=str, @@ -40,13 +40,12 @@ def parse_args(): help="File path of dataset in training.") # MaskRcnn training - parser.add_argument('--only_create_dataset', default=False, type=ast.literal_eval, - help="Whether to create dataset.") + parser.add_argument('--only_create_dataset', default=False, type=ast.literal_eval, help="Whether to create dataset.") parser.add_argument('--run_distribute', default=False, type=ast.literal_eval, help="Whether to run distribute.") parser.add_argument('--do_train', default=True, type=ast.literal_eval, help="Whether to do train.") parser.add_argument('--do_eval', default=False, type=ast.literal_eval, help="Whether to do eval.") parser.add_argument('--dataset', default='coco', type=str, help="Dataset name") - parser.add_argument('--pre_trained', default='../../maskrcnnr5/checkpoint/resnet50.ckpt', + parser.add_argument('--pre_trained', default='../../maskrcnnr5/checkpoint/resnet50_ascend_v180_imagenet2012_official_cv_top1acc76.97_top5acc93.44.ckpt', type=str, help="File path of pretrained checkpoint in training.") parser.add_argument('--device_id', default=0, type=int, help="Target device id.") parser.add_argument('--device_num', default=1, type=int, help="Target device number.") @@ -55,7 +54,7 @@ def parse_args(): # MaskRcnn evaluation parser.add_argument('--ann_file', default='../../coco2017bk/annotations/instances_val2017.json', type=str, help="File path of cocodataset annotations.") - parser.add_argument('--checkpoint_path', default='./checkpoint/mask_rcnn-1_117.ckpt', + parser.add_argument('--checkpoint_path', default='../checkpoint/maskrcnn_coco2017_acc32.9.ckpt', type=str, help="File path of pretrained checkpoint in evaluation.") @@ -65,8 +64,6 @@ def parse_args(): type=str, help="File path of pretrained checkpoint to export.") parser.add_argument('--file_name', default='./checkpoint/maskrcnn_coco2017_acc32.9.ckpt', type=str, help="File path of pretrained checkpoint in evaluation.") - # "file_name": "maskrcnn", - # "file_format": "MINDIR", # MaskRcnn ResNet50 inference parser.add_argument('--img_path', default='../../coco2017bk/val2017', @@ -80,8 +77,7 @@ def parse_args(): parser.add_argument('--img_width', default=1280, type=int, help="The input image width.") parser.add_argument('--img_height', default=768, type=int, help="The input image height.") - parser.add_argument('--keep_ratio', default=True, type=ast.literal_eval, - help="Whether to keep the same image scaling ratio.") + parser.add_argument('--keep_ratio', default=True, type=ast.literal_eval, help="Whether to keep the same image scaling ratio.") parser.add_argument('--flip_ratio', default=0.5, type=float, help="The flip ratio.") parser.add_argument('--expand_ratio', default=1.0, type=float, help="The expand ratio.") @@ -130,8 +126,7 @@ def parse_args(): # proposal parser.add_argument('--activate_num_classes', default=256, type=int, help="The activate number of classes.") - parser.add_argument('--use_sigmoid_cls', default=True, type=ast.literal_eval, - help="Whether to use sigmoid for classification.") + parser.add_argument('--use_sigmoid_cls', default=True, type=ast.literal_eval, help="Whether to use sigmoid for classification.") # roi_align parser.add_argument('--roi_layer', default=ed(type='RoIAlign', out_size=7, mask_out_size=14, sample_num=2), @@ -219,7 +214,7 @@ def parse_args(): help="File path of pretrained checkpoint to save.") # cocodataset - parser.add_argument('--mindrecord_dir', default='../../coco2017bk/MindRecord_COCO/MindRecord_COCO', type=str, + parser.add_argument('--mindrecord_dir', default='./MindRecord_COCO/MindRecord_COCO', type=str, help="File path of MindRecord to save/read.") parser.add_argument('--train_data_type', default='train2017', type=str, help="The data type for training (it is not necessary for other dataset.).") @@ -245,6 +240,6 @@ def parse_args(): help="The data classes for cocodataset (it is not necessary for other dataset.).") parser.add_argument('--num_classes', default=81, type=int, help="The number of classes for cocodataset (it is not necessary for other dataset.).") - return parser.parse_args() + return parser.parse_args(args=[]) config = parse_args() diff --git a/application_example/maskrcnn/src/utils/network_define.py b/application_example/maskrcnn/src/utils/network_define.py index fc398688f647235ec760cc68d457732435c85a8e..6ef90c462a0aeb78c5bfbe8e5c43f7472f1b55b4 100644 --- a/application_example/maskrcnn/src/utils/network_define.py +++ b/application_example/maskrcnn/src/utils/network_define.py @@ -156,7 +156,8 @@ class WithLossCell(nn.Cell): self._loss_fn = loss_fn def construct(self, x, img_shape, gt_bboxe, gt_label, gt_num, gt_mask): - loss1, loss2, _, _, _, _, _ = self._backbone(x, img_shape, gt_bboxe, gt_label, gt_num, gt_mask) + loss1, loss2, _, _, _, _, _ = \ + self._backbone(x, img_shape, gt_bboxe, gt_label, gt_num, gt_mask) return self._loss_fn(loss1, loss2) @property diff --git a/application_example/maskrcnn/src/utils/util.py b/application_example/maskrcnn/src/utils/util.py index 1fa3429b1c2a0cfb1ecaaa8a96363483ec62baa3..38bebe4347df5366c9c284230fa0f99ddec91fe9 100644 --- a/application_example/maskrcnn/src/utils/util.py +++ b/application_example/maskrcnn/src/utils/util.py @@ -21,7 +21,7 @@ from pycocotools.coco import COCO from pycocotools.cocoeval import COCOeval from pycocotools import mask as maskUtils -from model.config import config +from .config import config _init_value = np.array(0.0) summary_init = { diff --git a/application_example/stylegan2/StyleGAN2.ipynb b/application_example/stylegan2/StyleGAN2.ipynb index a59543955d1cdc9a3d73ad351b641d9ca2b64bb5..2d6e42601660227221d2ee9b69cd6ce05befb505 100644 --- a/application_example/stylegan2/StyleGAN2.ipynb +++ b/application_example/stylegan2/StyleGAN2.ipynb @@ -434,8 +434,8 @@ "from mindspore import nn, ops, Parameter\n", "import mindspore.numpy as mnp\n", "\n", - "from model.block import normalize_2nd_moment, FullyConnectedLayer, SynthesisBlock, resample_filter\n", - "from utils.ops import upfirdn2d" + "from model.block import normalize_2nd_moment, FullyConnectedLayer, SynthesisBlock, resample_filter, upsample2d\n", + "from model.discriminator import DiscriminatorBlock, DiscriminatorEpilogue" ] }, { @@ -1037,7 +1037,7 @@ "\n", " # ToRGB.\n", " if img is not None:\n", - " img = upfirdn2d.upsample2d(img, resample_filter, conv_info=self.conv_info)\n", + " img = upsample2d(img, resample_filter, conv_info=self.conv_info)\n", " if self.is_last or self.architecture == 'skip':\n", " y = self.torgb(x, next(w_iter)[0], fused_modconv=fused_modconv, conv_info=self.conv_info)\n", " y = y.astype(ms.float32)\n", @@ -1677,7 +1677,7 @@ "out_dir = './output/ffhq/' # Output directory\n", "snap = 1 # Snapshot interval\n", "random_seed = 0 # Random seed\n", - "data_dir = '../dataset/ffhq.zip' # Training data path\n", + "data_dir = '../dataset/ffhq_2k.zip' # Training data path\n", "img_res = 1024 # Resolution of FFHQ training data\n", "xflips = False # Enable dataset x-flips\n", "total_kimg = 25000 # Total number of true/fake images seen by discriminator to complete training, in thousand.\n", @@ -1922,7 +1922,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Num images: 70000 \n", + "Num images: 2000 \n", "Image shape: [3, 1024, 1024] \n", "Label shape: [1]\n", "Gmain loss: 4.883739\n", @@ -2177,12 +2177,12 @@ "Dmain loss: 3.966814\n", "Progress/tick: 1\n", "Progress/kimg: 1.0\n", - "Timing/total_sec: 3031.050943374634\n", - "Timing/sec_per_tick: 2877.0\n", - "Timing/sec_per_kimg: 2877.02\n", + "Timing/total_sec: 1798.050943374634\n", + "Timing/sec_per_tick: 1635.7\n", + "Timing/sec_per_kimg: 1635.68\n", "Timing/maintenance_sec: 25.9\n", - "Timing/total_hours: 0.8419585953818427\n", - "Timing/total_days: 0.03508160814091011\n", + "Timing/total_hours: 0.4989585953818427\n", + "Timing/total_days: 0.02078664584091011\n", "Gmain loss: 0.878925\n", "Dmain loss: 1.432189\n", "Gmain loss: 3.402533\n", diff --git a/application_example/stylegan2/images/lsun_car_infer_sample.png b/application_example/stylegan2/images/lsun_car_infer_sample.png new file mode 100644 index 0000000000000000000000000000000000000000..03cbeca8e99c0f7d85e9e66a1bb2f32ef115acc4 Binary files /dev/null and b/application_example/stylegan2/images/lsun_car_infer_sample.png differ diff --git a/application_example/stylegan2/readme.md b/application_example/stylegan2/readme.md new file mode 100644 index 0000000000000000000000000000000000000000..a1f8b7de41cfc5593e7229fd53e9ff35d16fc958 --- /dev/null +++ b/application_example/stylegan2/readme.md @@ -0,0 +1,147 @@ +# StyleGAN2 -- A MindSpore Implementation + +The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. This paper exposes and analyzes several of its characteristic artifacts, and proposes changes in both model architecture and training methods to address them. In particular, this paper redesignes generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent vectors to images. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably detect if an image is generated by a particular network. This paper furthermore visualizes how well the generator utilizes its output resolution, and identifies a capacity problem, motivating us to train larger models for additional quality improvements. Overall, this improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality. + + + +For more details, please refer to the following paper: + +Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, Timo Aila. [Analyzing and Improving the Image Quality of StyleGAN](https://arxiv.org/pdf/1912.04958.pdf)\[J\]. arXiv:1912.04958, 2019. + +## Requirements + +* Only Linux is supported as MindSpore-GPU framework only supports Linux. +* 64-bit Python 3.7 and MindSpore 1.7.0. +* GPU with at least 16GB VRAM, CUDA 11.1. +* You might want to try Ascend 910 NPU on ModelArts Platform as MindSpore has been continuously optimized for Ascend NPU. Be aware that training is not working on Ascend 910 at the moment. Inference works well. + +## Training parameters description + +| Parameter | Default | Description | +|:-----|:---------|:--------| +| out_dir | ./output/ffhq/ | Output path | +| snap | 10 | Number of ticks within 1 network snapshot interval| +| device_target | GPU | Platform | +| device_id | 0 | Appoint specific device if more than 1 device exist| +| seed | 0 | Random seed | +| img_res | 1024 | Image resolution | +| data_dir | ../dataset/ffhq.zip | Training data | +| xflips | False | Images' horizontal flip | +| total_kimg | 25000 | Total number of images seen by discriminator in thousands | +| batch_size | 1 | Batch size | +| start_over | False | Start training from scratch | +| resume_train | None | Path to network snapshot ckpt | +| resume_paper | None| Path to pre-trained ckpt given by author of paper | + +## Inferring parameters description + +| Parameter | Default | Description | +|:-----|:---------|:--------| +| device_target | GPU | Platform | +| device_id | 0 | Appoint specific device if more than 1 device exist| +| ckpt | ./ckpt/G_ema.ckpt | Network checkpoint | +| seeds| 66,230,389,1518 | Seeds | +| truncation-psi | 0.5 | Truncation trick | +| num_layers | 8 | Number of mapping layers | +| img_res | 1024 | Image resolution | +| noise_mode | 1 | Noise mode | +| out_dir | ./generated_images | Output path | +| grid_size | None | Curate images in axb grid | + +## Example + +Below is the introduction of the usage of StyleGAN2. + +### Preparation + +First of all, download [FFHQ dataset folder](https://drive.google.com/drive/folders/1tZUcXDBeOibC6jcMCtgRRz67pzrAHeHL?usp=sharing) or [FFHQ dataset zip file](https://drive.google.com/file/d/1WvlAIvuochQn_L_f9p3OdFdTiSLlnnhv/view?usp=sharing) and save it in `./dataset/` directory. + +Download Lsun Car dataset in zip format [here](http://dl.yf.io/lsun/objects/car.zip). After that, extract it to lmdb format and name it as `lsun_car_lmdb` which contains `data.mdb` and `lock.mdb`. + +Next, you probably want to download the pre-trained models for inference purpose. Mindspore checkpoints can be downloaded from [here](https://download.mindspore.cn/vision/stylegan2/). Save these checkpoints in `./src/ckpt/ffhq` and `./src/ckpt/lsun_car_wide` directory. + +After you make the preparation above, make sure your path is as following: + +```text +./dataset/ + ├── ffhq.zip + └── lsun_car.zip + +./src/ + └── ckpt + | ├──ffhq + | ├── G.ckpt + | ├── G_ema.ckpt + | └── D.ckpt + | ├──lsun_car_wide + | ├── G.ckpt + | ├── G_ema.ckpt + | └── D.ckpt +``` + +### Dataset conversion + +It is not a must to convert the FFHQ dataset from folders to zip if you have downloaded the dataset in folder. + +Run `src/dataset_crop_zip.py` for the conversion from folder to zip. + +```shell +cd stylegan2/src +python dataset_crop_zip.py --source=../dataset/ffhq/ --dest=../dataset/ffhq.zip +``` + +It is a must to convert the Lsun Car dataset from lmdb format to zip format before training. + +Run `src/dataset_crop_zip.py` for the conversion from lmdb to zip. + +```shell +python dataset_crop_zip.py --source=../dataset/lsun_car_lmdb --dest=../dataset/lsun_car.zip --transform=center-crop-wide --width=512 --height=384 +``` + +### Inference + +Run `infer.py` to generate fake human's face images curated in paper. + +```shell +python infer.py --seed=66,1518,389,230 --ckpt=./ckpt/ffhq/G_ema.ckpt --img_res=1024 --truncation_psi=1 +``` + + + +Run `infer.py` to generate fake car images curated in paper. + +```shell +python infer.py --seed=6000-6005 --ckpt=./ckpt/lsun_car_wide/G_ema.ckpt --img_res=512 --truncation_psi=0.5 +``` + + + +The output images are in the `out_dir` directory. + +From the images shown above, we could tell that the performance of inferring images using MindSpore is on par with the images curated in the paper. + +### Training + +Run `train.py` to start training fake human's face model from scratch. + +```shell +python train.py --data_dir=../dataset/ffhq.zip --batch_size=2 --start_over=True --xflips=True --out_dir=./output_ffhq +``` + +Run `train.py` to start training fake car model from scratch. + +```shell +python train.py --data_dir=../dataset/lsun_car.zip --batch_size=4 --start_over=True --xflips=True --img_res=512 --out_dir=./output_lsun_car +``` + +Run `train.py` to resume ffhq training from network snapshot by which 22k images have been seen by discriminator. + +```shell +python train.py --data_dir=../dataset/ffhq.zip --batch_size=2 --resume_train=./output_ffhq/network-snapshot-000022 --xflips=True --out_dir=./output_ffhq +``` + +Run `train.py` to resume lsun_car training from network snapshot by which 35k images have been seen by discriminator. + +```shell +python train.py --data_dir=../dataset/lsun_car.zip --batch_size=4 --resume_train=./output_lsun_car/network-snapshot-000035 --xflips=True --out_dir=./output_lsun_car --img_res=512 +``` \ No newline at end of file diff --git a/application_example/stylegan2/src/infer.py b/application_example/stylegan2/src/infer.py index 176e49f2ab08a16e70caf380c264465fc6fe1cfb..a46923c2aadf93a9a21c114ca7086566195d3246 100644 --- a/application_example/stylegan2/src/infer.py +++ b/application_example/stylegan2/src/infer.py @@ -21,13 +21,14 @@ import re import argparse import mindspore as ms -from mindspore import ops, Tensor, load_checkpoint, load_param_into_net +from mindspore import ops, Tensor, load_checkpoint, load_param_into_net, context import numpy as np import PIL.Image from model.generator import Generator from train import save_image_grid +os.environ['GLOG_v'] = '3' def num_range(s): """ @@ -66,6 +67,11 @@ def generate(args_infer): >>> generate(args) """ + if args_infer.device_target == 'Ascend': + context.set_context(mode=context.PYNATIVE_MODE) + + context.set_context(device_target=args_infer.device_target) + context.set_context(device_id=args_infer.device_id) ckpt = args_infer.ckpt seeds = args_infer.seeds truncation_psi = args_infer.truncation_psi @@ -118,18 +124,20 @@ def parse_args(): Parameter configuration """ - args = argparse.ArgumentParser() - args.add_argument('--ckpt', default='./ckpt/G_ema.ckpt', help='Network checkpoint') - args.add_argument('--seeds', type=num_range, default='66,230,389,1518', - help='seeds option is required, input_format=85,265,297,849 or 601-605') - args.add_argument('--truncation_psi', type=float, help='Truncation trick', default=0.5) - args.add_argument('--num_layers', type=int, help='Number of mapping layers', default=8) - args.add_argument('--noise_mode', type=int, help='Noise mode, 0=none, 1=const, 2=random', default=1) - args.add_argument('--img_res', type=int, help='Output image resolution, ffhq=1024, lsun_wide=512', default=1024) - args.add_argument('--out_dir', type=str, help='Output path', default='./generated_images') - args.add_argument('--grid_size', type=num_range, help='two integers a,b, curate images in axb grid') - - args = args.parse_args() + parser = argparse.ArgumentParser(description='infer') + parser.add_argument('--device_target', type=str, default='GPU', help='platform') + parser.add_argument('--device_id', type=int, default=0, help='appoint device_id if more than 1 device exist') + parser.add_argument('--ckpt', default='./ckpt/ffhq/G_ema.ckpt', help='Network checkpoint') + parser.add_argument('--seeds', type=num_range, default='66,1518,389,230', + help='seeds option is required, input_format=85,265,297,849 or 601-605') + parser.add_argument('--truncation_psi', type=float, help='Truncation trick', default=0.5) + parser.add_argument('--num_layers', type=int, help='Number of mapping layers', default=8) + parser.add_argument('--noise_mode', type=int, help='Noise mode, 0=none, 1=const, 2=random', default=1) + parser.add_argument('--img_res', type=int, help='Output image resolution, ffhq=1024, lsun_wide=512', default=1024) + parser.add_argument('--out_dir', type=str, help='Output path', default='./generated_images') + parser.add_argument('--grid_size', type=num_range, help='two integers a,b, curate images in axb grid') + + args = parser.parse_args() return args diff --git a/application_example/stylegan2/src/model/block.py b/application_example/stylegan2/src/model/block.py index f15f5c04ed8566250464801725a091460ad565d8..f867d06e9e04388790d104cc660a01c4e511940f 100644 --- a/application_example/stylegan2/src/model/block.py +++ b/application_example/stylegan2/src/model/block.py @@ -19,17 +19,493 @@ Stylegan2 blocks import numpy as np import mindspore as ms import mindspore.numpy as mnp +import mindspore.common.dtype as mstype from mindspore import nn, ops, Parameter, Tensor from mindspore.nn import CellList +from mindspore.ops import operations as P +from mindspore._checkparam import Validator -from utils.ops import conv2d_resample, upfirdn2d, bias_act +from utils.ops import conv2d_gradfix, bias_act -# Resample filter's values captured from pre-trained model's checkpoint resample_filter = Tensor([[0.0156, 0.0469, 0.0469, 0.0156], [0.0469, 0.1406, 0.1406, 0.0469], [0.0469, 0.1406, 0.1406, 0.0469], [0.0156, 0.0469, 0.0469, 0.0156]], ms.float32) +class Pad(ms.nn.Cell): + """ + Pad operator, output the image after padding. + + Args: + paddings (tuple): Paddings. + mode (str): Padding mode. Options = ["CONSTANT", "REFLECT" or "SYMMETRIC"]. Default: "CONSTANT". + + Inputs: + **x** (Tensor): input tensor. + + Outputs: + Tensor, padding output. + + Supported Platforms: + ``Ascend`` ``GPU`` ``CPU`` + + Examples: + >>> pad_out = Pad(paddings=p) + """ + + def __init__(self, paddings, mode="CONSTANT"): + """Initialize Pad.""" + super(Pad, self).__init__() + self.mode = mode + self.paddings = paddings + Validator.check_string(self.mode, ["CONSTANT", "REFLECT", "SYMMETRIC"], 'mode', self.cls_name) + if not isinstance(paddings, tuple): + raise TypeError(f"For '{self.cls_name}', the type of 'paddings' must be tuple, " + f"but got {type(paddings).__name__}.") + for item in paddings: + if len(item) != 2: + raise ValueError(f"For '{self.cls_name}', the dimension of 'paddings' must be (n, 2), " + f"but got {paddings}.") + if mode == "CONSTANT": + self.pad = P.Pad(self.paddings) + else: + self.paddings = Tensor(np.array(self.paddings), mstype.int64) + self.pad = P.MirrorPad(mode=mode) + + def construct(self, x): + """Pad construct""" + if self.mode == "CONSTANT": + x = self.pad(x) + else: + x = self.pad(x, self.paddings) + return x + + +def parse_scaling(scaling): + """ + Return the padding. + + Args: + scaling (int): padding parameter. + + Returns: + int, x scaling parameter. + int, x scaling parameter. + + Examples: + >>> padx0, padx1, pady0, pady1 = parse_padding(padding) + """ + + if isinstance(scaling, int): + scaling = [scaling, scaling] + sx, sy = scaling + return sx, sy + + +def parse_padding(padding): + """ + Return the padding. + + Args: + padding (int): padding parameter. + + Returns: + int, x0 scaling parameter. + int, x1 scaling parameter. + int, y0 scaling parameter. + int, y1 scaling parameter. + + Examples: + >>> padx0, padx1, pady0, pady1 = parse_padding(padding) + """ + + if isinstance(padding, int): + padding = [padding, padding] + if len(padding) == 2: + padx, pady = padding + padding = [padx, padx, pady, pady] + return padding + + +def get_filter_size(f): + """ + Get the size of the filter. + + Args: + f (Tensor): Filter tensor. + + Returns: + int, Filter width. + int, Filter height. + + Examples: + >>> fw, fh = get_filter_size(f) + """ + + if f is None: + return 1, 1 + fw = f.shape[-1] + fh = f.shape[0] + return fw, fh + + +def ceiling(a): + if a <= 0: + return 0 + return a + + +def compare_min(a, b): + if a <= b: + return a + return b + + +def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, conv_info=None): + """ + Pad, upsample, filter, and downsample a batch of 2D images. + + Performs the following sequence of operations for each channel: + 1. Upsample the image by inserting N-1 zeros after each pixel (`up`). + 2. Pad the image with the specified number of zeros on each side (`padding`). + Negative padding corresponds to cropping the image. + 3. Convolve the image with the specified 2D FIR filter (`f`), shrinking it + so that the footprint of all output pixels lies within the input image. + 4. Downsample the image by keeping every Nth pixel (`down`). + + This sequence of operations bears close resemblance to scipy.signal.upfirdn(). + The fused op is considerably more efficient than performing the same calculation + using standard PyTorch ops. It supports gradients of arbitrary order. + + Args: + x (Tensor): Float32/float64/float16 input tensor of the shape + `[batch_size, num_channels, in_height, in_width]`. + f (Tensor): Float32 FIR filter of the shape `[filter_height, filter_width]` (non-separable), + `[filter_taps]` (separable), or `None` (identity). + up (int): Integer upsampling factor. Can be a single int or a list/tuple `[x, y]`. Default: 1. + down (int): Integer downsampling factor. Can be a single int or a list/tuple `[x, y]`. Default: 1. + padding (int): Padding with respect to the upsampled image. Can be a single number + or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`. Default: 0. + flip_filter (bool): False = convolution, True = correlation. Default: False. + gain (int): Overall scaling factor for signal magnitude. Default: 1. + conv_info: Information of convolutional operators. Default: None. + + Returns: + Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. + + Examples: + >>> x = upfirdn2d(x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain, \ + conv_info=conv_info) + """ + + # Validate arguments. + if f is None: + f = ms.ops.Ones()((1, 1), ms.float32) + batch_size, num_channels, in_height, in_width = x.shape + upx, upy = parse_scaling(up) + downx, downy = parse_scaling(down) + padx0, padx1, pady0, pady1 = parse_padding(padding) + + # Upsample by inserting zeros. + x = x.reshape([batch_size, num_channels, in_height, 1, in_width, 1]) + pad1 = Pad(paddings=((0, 0), (0, 0), (0, 0), (0, upy - 1), (0, 0), (0, upx - 1))) + x = pad1(x) + x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx]) + + # Pad or crop. + pad2 = Pad(paddings=((0, 0), (0, 0), (max(pady0, 0), max(pady1, 0)), (max(padx0, 0), max(padx1, 0)))) + x = pad2(x) + max_y0 = ceiling(-pady0) + max_y1 = ceiling(-pady1) + max_x0 = ceiling(-padx0) + max_x1 = ceiling(-padx1) + x = x[:, :, max_y0: x.shape[2] - max_y1, max_x0: x.shape[3] - max_x1] + + # Setup filter. + f = f * (gain ** (f.ndim / 2)) + f = f.astype(x.dtype) + if not flip_filter: + f_dim = f.ndim + f = mnp.flip(f, list(range(f_dim))) + + # Convolve with the filter. + ff = f.astype(mnp.float32) + ff = mnp.tile(ff[np.newaxis, np.newaxis], ([num_channels, 1] + [1] * f.ndim)) + f = ff.astype(x.dtype) + if f.ndim == 4: + x = conv2d_gradfix.conv2d(x_input=x, weight=f, conv_info=conv_info) + else: + x = conv2d_gradfix.conv2d(x_input=x, weight=f.unsqueeze(2), conv_info=conv_info) + x = conv2d_gradfix.conv2d(x_input=x, weight=f.unsqueeze(3), conv_info=conv_info) + + # Downsample by throwing away pixels. + x = x[:, :, ::downy, ::downx] + return x + + +def filter2d(x, f, padding=0, flip_filter=False, gain=1, conv_info=None): + """ + Filter a batch of 2D images using the given 2D FIR filter. + + By default, the result is padded so that its shape matches the input. + User-specified padding is applied on top of that, with negative values + indicating cropping. Pixels outside the image are assumed to be zero. + + Args: + x (Tensor): Float32/float64/float16 input tensor of the shape + `[batch_size, num_channels, in_height, in_width]`. + f (Tensor): Float32 FIR filter of the shape `[filter_height, filter_width]` (non-separable), + `[filter_taps]` (separable), or `None` (identity). + padding (int): Padding with respect to the output. Can be a single number or a + list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`. Default: 0. + flip_filter (bool): False = convolution, True = correlation. Default: False. + gain (int): Overall scaling factor for signal magnitude. Default: 1. + conv_info: Information of convolutional operators. Default: None. + + Returns: + Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. + + Examples: + >>> x = filter2d(x, f, conv_info=conv_info) + """ + + padx0, padx1, pady0, pady1 = parse_padding(padding) + fw, fh = get_filter_size(f) + p = [padx0 + fw // 2, padx1 + (fw - 1) // 2, pady0 + fh // 2, pady1 + (fh - 1) // 2] + return upfirdn2d(x, f, padding=p, flip_filter=flip_filter, gain=gain, conv_info=conv_info) + + +def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, conv_info=None): + """ + Upsample a batch of 2D images using the given 2D FIR filter. + + By default, the result is padded so that its shape is a multiple of the input. + User-specified padding is applied on top of that, with negative values + indicating cropping. Pixels outside the image are assumed to be zero. + + Args: + x (Tensor): Float32/float64/float16 input tensor of the shape + `[batch_size, num_channels, in_height, in_width]`. + f (Tensor): Float32 FIR filter of the shape `[filter_height, filter_width]` (non-separable), + `[filter_taps]` (separable), or `None` (identity). + up (int): Integer upsampling factor. Can be a single int or a list/tuple `[x, y]`. Default: 2. + padding (int): Padding with respect to the upsampled image. Can be a single number + or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`. Default: 0. + flip_filter (bool): False = convolution, True = correlation. Default: False. + gain (int): Overall scaling factor for signal magnitude. Default: 1. + conv_info: Information of convolutional operators. Default: None. + + Returns: + Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. + + Examples: + >>> x = upsample2d(x, resample_filter, conv_info=conv_info) + """ + + upx, upy = parse_scaling(up) + padx0, padx1, pady0, pady1 = parse_padding(padding) + fw, fh = get_filter_size(f) + p = [padx0 + (fw + upx - 1) // 2, padx1 + (fw - upx) // 2, pady0 + (fh + upy - 1) // 2, pady1 + (fh - upy) // 2] + return upfirdn2d(x, f, up=up, padding=p, flip_filter=flip_filter, gain=gain*upx*upy, conv_info=conv_info) + + +def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, conv_info=None): + """ + Downsample a batch of 2D images using the given 2D FIR filter. + + By default, the result is padded so that its shape is a fraction of the input. + User-specified padding is applied on top of that, with negative values + indicating cropping. Pixels outside the image are assumed to be zero. + + Args: + x (Tensor): Float32/float64/float16 input tensor of the shape + `[batch_size, num_channels, in_height, in_width]`. + f (Tensor): Float32 FIR filter of the shape `[filter_height, filter_width]` (non-separable), + `[filter_taps]` (separable), or `None` (identity). + down (int): Integer downsampling factor. Can be a single int or a list/tuple `[x, y]`. Default: 2. + padding (int): Padding with respect to the upsampled image. Can be a single number + or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`. Default: 0. + flip_filter (bool): False = convolution, True = correlation. Default: False. + gain (int): Overall scaling factor for signal magnitude. Default: 1. + conv_info: Information of convolutional operators. Default: None. + + Returns: + Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. + + Examples: + >>> x = downsample2d(x, resample_filter, conv_info=conv_info) + """ + + downx, downy = parse_scaling(down) + padx0, padx1, pady0, pady1 = parse_padding(padding) + fw, fh = get_filter_size(f) + p = [padx0 + (fw - downx + 1) // 2, padx1 + (fw - downx) // 2, + pady0 + (fh - downy + 1) // 2, pady1 + (fh - downy) // 2] + return upfirdn2d(x, f, down=down, padding=p, flip_filter=flip_filter, gain=gain, conv_info=conv_info) + + +def _get_weight_shape(w): + """ + Get the shape of the weight. + + Args: + w (Tensor): The weight. + + Returns: + List, the shape of weight. + + Examples: + >>> shape = _get_weight_shape(w) + """ + + shape = [sz for sz in w.shape] + return shape + + +def conv2d_wrapper(x, w, stride=1, padding=0, groups=1, transpose=False, flip_weight=True, conv_info=None): + """ + Wrapper for the underlying `conv2d()` and `conv_transpose2d()` implementations. + + Args: + x (Tensor): input. + w (Tensor): weight. + stride (int): stride. Default=1. + padding (int): padding. Default=0. + groups (int): groups. Default=1. + transpose (bool): need to transpose. Default=False. + flip_weight (bool): need to flip the weight. Default=True. + conv_info: Information of convolutional operators. Default: None. + + Returns: + Tensor, output of conv2d. + + Examples: + >>> x = conv2d_wrapper(x=x, w=w, groups=groups, transpose=True, flip_weight=True, conv_info=conv_info) + """ + + out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w) + + # Flip weight if requested. + if not flip_weight: + w = w.flip([2, 3]) + + # Workaround performance pitfall in cuDNN 8.0.5, triggered when using + # 1x1 kernel + memory_format=channels_last + less than 64 channels. + if kw == 1 and kh == 1 and stride == 1 and padding in [0, [0, 0], (0, 0)] and not transpose: + if x.strides[1] == 1: + if min(out_channels, in_channels_per_group) < 64: + if out_channels <= 4 and groups == 1: + in_shape = x.shape + x = w.squeeze(3).squeeze(2) @ x.reshape([in_shape[0], in_channels_per_group, -1]) + x = x.reshape([in_shape[0], out_channels, in_shape[2], in_shape[3]]) + else: + raise AssertionError('shape of x is nor correct') + # Otherwise => execute using conv2d_gradfix. + op = conv2d_gradfix.conv_transpose2d if transpose else conv2d_gradfix.conv2d + return op(x, w, conv_info=conv_info) + + +def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, + flip_weight=True, flip_filter=False, conv_info=None): + """ + 2D convolution with optional up/downsampling. + + Padding is performed only once at the beginning, not between the operations. + + Args: + x (Tensor): Input tensor of shape `[batch_size, in_channels, in_height, in_width]`. + w (Tensor): Weight tensor of shape `[out_channels, in_channels//groups, kernel_height, kernel_width]`. + f (Tensor): Low-pass filter for up/downsampling. Must be prepared beforehand by + calling upfirdn2d.setup_filter(). None = identity. Default: None. + up (int): Integer upsampling factor. Default: 1. + down (int): Integer downsampling factor. Default: 1. + padding (int): Padding with respect to the upsampled image. Can be a single number + or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`. Default: 0. + groups (int): Split input channels into N groups. Default: 1. + flip_weight (bool): False = convolution, True = correlation. Default: True. + flip_filter (bool): False = convolution, True = correlation. Default: False. + conv_info: Information of convolutional operators. Default: None. + + Returns: + Tensor, output tensor of the shape `[batch_size, num_channels, out_height, out_width]`. + + Examples: + >>> x = conv2d_resample(x=x, w=w, f=f, flip_weight=flip_weight, conv_info=conv_info) + """ + + # Validate arguments. + out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w) + fw, fh = get_filter_size(f) + px0, px1, py0, py1 = parse_padding(padding) + + # Adjust padding to account for up/downsampling + if up > 1: + px0 += (fw + up - 1) // 2 + px1 += (fw - up) // 2 + py0 += (fh + up - 1) // 2 + py1 += (fh - up) // 2 + if down > 1: + px0 += (fw - down + 1) // 2 + px1 += (fw - down) // 2 + py0 += (fh - down + 1) // 2 + py1 += (fh - down) // 2 + + if kw == 1 and kh == 1: + # Fast path: 1x1 convolution with downsampling only => downsample first, then convolve. + if down > 1 and up == 1: + x = upfirdn2d(x=x, f=f, down=down, padding=[px0, px1, py0, py1], flip_filter=flip_filter, + conv_info=conv_info) + x = conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight, conv_info=conv_info) + return x + + # Fast path: 1x1 convolution with upsampling only => convolve first, then upsample. + if up > 1 and down == 1: + x = conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight, conv_info=conv_info) + x = upfirdn2d(x=x, f=f, up=up, padding=[px0, px1, py0, py1], gain=up ** 2, + flip_filter=flip_filter, conv_info=conv_info) + return x + + # Fast path: downsampling only => use strided convolution. + if down > 1 and up == 1: + x = upfirdn2d(x=x, f=f, padding=[px0, px1, py0, py1], flip_filter=flip_filter, conv_info=conv_info) + x = conv2d_wrapper(x=x, w=w, stride=down, groups=groups, flip_weight=flip_weight, conv_info=conv_info) + return x + + if up > 1: + # Fast path: upsampling with optional downsampling => use transpose strided convolution. (if up > 1) + if groups == 1: + w = w.transpose(1, 0, 2, 3) + else: + w = w.reshape(groups, out_channels // groups, in_channels_per_group, kh, kw) + w = w.transpose(0, 2, 1, 3, 4) + w = w.reshape(groups * in_channels_per_group, out_channels // groups, kh, kw) + px0 -= kw - 1 + px1 -= kw - up + py0 -= kh - 1 + py1 -= kh - up + pxt = ceiling(compare_min(-px0, -px1)) + pyt = ceiling(compare_min(-py0, -py1)) + x = conv2d_wrapper(x=x, w=w, stride=up, padding=pxt, groups=groups, transpose=True, + flip_weight=(not flip_weight), conv_info=conv_info) + x = upfirdn2d(x=x, f=f, padding=[px0 + pxt, px1 + pxt, py0 + pyt, py1 + pyt], gain=up ** 2, + flip_filter=flip_filter, conv_info=conv_info) + if down > 1: + x = upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter, conv_info=conv_info) + return x + + # Fast path: no up/downsampling, padding supported by the underlying implementation => use plain conv2d. + if up == 1 and down == 1: + if px0 == px1 and py0 == py1 and px0 >= 0 and py0 >= 0: + return conv2d_wrapper(x=x, w=w, padding=px0, groups=groups, flip_weight=flip_weight, conv_info=conv_info) + + # Fallback: Generic reference implementation. + x = upfirdn2d(x=x, f=(f if up > 1 else None), up=up, padding=[px0, px1, py0, py1], + gain=up**2, flip_filter=flip_filter, conv_info=conv_info) + x = conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight, conv_info=conv_info) + if down > 1: + x = upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter, conv_info=conv_info) + return x + def normalize_2nd_moment(x, dim=1, eps=1e-8): """ @@ -86,8 +562,8 @@ def modulated_conv2d(x, weight, styles, noise=None, up=1, down=1, padding=0, r_f # Pre-normalize inputs to avoid FP16 overflow. if x.dtype == ms.float16 and demodulate: - weight = weight * (1 / np.sqrt(in_channels * kh * kw)) / weight.max(axis=[1, 2, 3], keepdims=True) - styles = styles / styles.max(axis=1, keepdims=True) + weight = weight * (1 / mnp.sqrt(in_channels * kh * kw)) / weight.max([1, 2, 3], True) + styles = styles / styles.max(1, True) # Calculate per-sample weights and demodulation coefficients. w = None @@ -97,15 +573,15 @@ def modulated_conv2d(x, weight, styles, noise=None, up=1, down=1, padding=0, r_f w = ops.ExpandDims()(weight, 0) w = w * reshape(styles, (batch_size, 1, -1, 1, 1)) if demodulate: - dcoefs = 1 / sqrt(square(w).sum(axis=2).sum(axis=2).sum(axis=2) + 1e-8) + dcoefs = 1 / sqrt(square(w).sum(2).sum(2).sum(2) + 1e-8) if demodulate and fused_modconv: w = w * reshape(dcoefs, (batch_size, -1, 1, 1, 1)) # Execute by scaling the activations before and after the convolution. if not fused_modconv: x = x * styles.astype(x.dtype).reshape(batch_size, -1, 1, 1) - x = conv2d_resample.conv2d_resample(x=x, w=weight.astype(x.dtype), f=r_filter, up=up, down=down, - padding=padding, flip_weight=flip_weight, conv_info=conv_info) + x = conv2d_resample(x=x, w=weight.astype(x.dtype), f=r_filter, up=up, down=down, + padding=padding, flip_weight=flip_weight, conv_info=conv_info) if demodulate and noise is not None: x = x * dcoefs.astype(x.dtype).reshape(batch_size, - 1, 1, 1) + noise.astype(x.dtype) elif demodulate: @@ -117,8 +593,8 @@ def modulated_conv2d(x, weight, styles, noise=None, up=1, down=1, padding=0, r_f # Execute as one fused op using grouped convolution. x = reshape(x, (1, -1, x.shape[2], x.shape[3])) w = reshape(w, (-1, in_channels, kh, kw)) - x = conv2d_resample.conv2d_resample(x=x, w=w.astype(x.dtype), f=r_filter, up=up, down=down, padding=padding, - groups=batch_size, flip_weight=flip_weight, conv_info=conv_info) + x = conv2d_resample(x=x, w=w.astype(x.dtype), f=r_filter, up=up, down=down, padding=padding, + groups=batch_size, flip_weight=flip_weight, conv_info=conv_info) x = reshape(x, (batch_size, -1, x.shape[2], x.shape[3])) if noise is not None: x = x + noise @@ -235,9 +711,9 @@ class Conv2dLayer(nn.Cell): w = self.weight * self.weight_gain b = self.bias.astype(x.dtype) if self.bias is not None else None flip_weight = (self.up == 1) # slightly faster - x = conv2d_resample.conv2d_resample(x=x, w=w.astype(x.dtype), f=resample_filter, - up=self.up, down=self.down, padding=self.padding, - flip_weight=flip_weight, conv_info=conv_info) + x = conv2d_resample(x=x, w=w.astype(x.dtype), f=resample_filter, + up=self.up, down=self.down, padding=self.padding, + flip_weight=flip_weight, conv_info=conv_info) act_gain = self.act_gain * gain x = bias_act.bias_act(x, b, act=self.activation, gain=act_gain) @@ -279,12 +755,9 @@ class SynthesisLayer(nn.Cell): >>> x = layer(x, w, conv_info=conv_info) """ - std_normal = ops.StandardNormal() - def __init__(self, in_channels, out_channels, w_dim, resolution, kernel_size=3, up=1, use_noise=True, activation='lrelu', conv_clamp=None): super().__init__() - std_normal = ops.StandardNormal() self.resolution = resolution self.up = up self.use_noise = use_noise @@ -296,7 +769,7 @@ class SynthesisLayer(nn.Cell): self.weight = Parameter(Tensor(np.random.randn(out_channels, in_channels, kernel_size, kernel_size), ms.float32)) if use_noise: - self.noise_const = std_normal((resolution, resolution)) + self.noise_const = Tensor(np.random.randn(resolution, resolution), ms.float32) self.noise_strength = Parameter(ops.Zeros()((), ms.float32)) self.bias = Parameter(ops.Zeros()(out_channels, ms.float32)) @@ -305,7 +778,8 @@ class SynthesisLayer(nn.Cell): styles = self.affine(w) noise = None if self.use_noise and noise_mode == 2: - noise = ops.StandardNormal()((x.shape[0], 1, self.resolution, self.resolution)) * self.noise_strength + noise = Tensor(np.random.randn(x.shape[0], 1, self.resolution, self.resolution), ms.float32)\ + * self.noise_strength if self.use_noise and noise_mode == 1: noise = self.noise_const * self.noise_strength @@ -352,7 +826,8 @@ class ToRGBLayer(nn.Cell): super().__init__() self.conv_clamp = conv_clamp self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1) - self.weight = Parameter(ops.StandardNormal()((out_channels, in_channels, kernel_size, kernel_size))) + self.weight = Parameter(Tensor(np.random.randn(out_channels, in_channels, kernel_size, kernel_size), + ms.float32)) self.bias = Parameter(ops.Zeros()(out_channels, ms.float32)) self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2)) @@ -422,7 +897,7 @@ class SynthesisBlock(nn.Cell): self.num_torgb = 0 if in_channels == 0: - self.const = Parameter(ops.StandardNormal()((out_channels, resolution, resolution))) + self.const = Parameter(Tensor(np.random.randn(out_channels, resolution, resolution), ms.float32)) if in_channels != 0: self.conv0 = SynthesisLayer(in_channels, out_channels, w_dim=w_dim, resolution=resolution, up=2, @@ -729,7 +1204,7 @@ class SynthesisBlock(nn.Cell): def construct(self, x, img, ws, force_fp32=False, fused_modconv=None, noise_mode=0): """Synthesis block construct""" - w_iter = iter(ops.Unstack(axis=1)(ws)) + w_iter = iter(ops.Unstack(1)(ws)) d_type = ms.float16 if self.use_fp16 and not force_fp32 else ms.float32 if fused_modconv is None: fused_modconv = (not self.training) and (d_type == ms.float32 or x.shape[0] == 1) @@ -754,9 +1229,10 @@ class SynthesisBlock(nn.Cell): # ToRGB. if img is not None: - img = upfirdn2d.upsample2d(img, resample_filter, conv_info=self.conv_info) + img = upsample2d(img, resample_filter, conv_info=self.conv_info) if self.is_last or self.architecture == 'skip': y = self.torgb(x, next(w_iter)[0], fused_modconv=fused_modconv, conv_info=self.conv_info) y = y.astype(ms.float32) - img = img + y if img is not None else y + img = ops.Add()(img, y) if img is not None else y return x, img + \ No newline at end of file diff --git a/application_example/stylegan2/src/model/discriminator.py b/application_example/stylegan2/src/model/discriminator.py index 247dd9cdbf635360faaed004831cac2f90482240..83cafe1f745195df52b86ac1b3bd122ef494943f 100644 --- a/application_example/stylegan2/src/model/discriminator.py +++ b/application_example/stylegan2/src/model/discriminator.py @@ -21,8 +21,7 @@ import mindspore as ms from mindspore import nn, ops, Tensor import mindspore.numpy as mnp -from utils.ops import upfirdn2d -from model.block import FullyConnectedLayer, Conv2dLayer, resample_filter +from model.block import FullyConnectedLayer, Conv2dLayer, resample_filter, downsample2d from model.generator import MappingNetwork @@ -109,7 +108,7 @@ class DiscriminatorBlock(nn.Cell): img = img.astype(d_type) y = self.fromrgb(img, conv_info=conv_info) x = x + y if x is not None else y - img = upfirdn2d.downsample2d(img, resample_filter, conv_info=conv_info) \ + img = downsample2d(img, resample_filter, conv_info=conv_info) \ if self.architecture == 'skip' else None # Main layers. diff --git a/application_example/stylegan2/src/model/generator.py b/application_example/stylegan2/src/model/generator.py index 000c9332bc066a65aac2bc009ce3b47966ba8055..329756be7671e64faac03cbede0baf6ab26aa85e 100644 --- a/application_example/stylegan2/src/model/generator.py +++ b/application_example/stylegan2/src/model/generator.py @@ -105,7 +105,7 @@ class MappingNetwork(nn.Cell): # Update moving average of W. if self.w_avg_beta is not None and self.training and not skip_w_avg_update: - self.w_avg = (x.mean(0) + (self.w_avg - x.mean(0)) * self.w_avg_beta).copy() + self.w_avg = (x.mean(axis=0) + (self.w_avg - x.mean(axis=0)) * self.w_avg_beta).copy() # Broadcast. if self.num_ws is not None: diff --git a/application_example/stylegan2/src/train.py b/application_example/stylegan2/src/train.py index 7eefdec80997cb5f3ef4222b37130f4cb8452e84..77a44305654ec3d6a7b0b8205c4250bcebbaae3a 100644 --- a/application_example/stylegan2/src/train.py +++ b/application_example/stylegan2/src/train.py @@ -24,13 +24,14 @@ import numpy as np import PIL.Image import mindspore as ms from mindspore import load_checkpoint, load_param_into_net -from mindspore import Tensor, ops, nn, set_seed +from mindspore import Tensor, ops, nn, set_seed, context from model.generator import Generator from model.discriminator import Discriminator from training_dataset.dataset import Ffhq, LsunCarWide from loss.stylegan2_loss import CustomWithLossCell, StyleGANLoss +os.environ['GLOG_v'] = '3' def setup_snapshot_image_grid(dataset, seed=0): """ @@ -136,7 +137,7 @@ def save_model(module_list, out_dir, cur_nimg): for name, module in module_list: module_copy = copy.deepcopy(module) for key, value in module_copy.parameters_dict().items(): - if 'all_conv' in key: + if 'conv_list' in key: value.requires_grad = False module_copy.requires_grad = False all_param = [] @@ -145,11 +146,12 @@ def save_model(module_list, out_dir, cur_nimg): layer['name'] = par.name layer['data'] = par all_param.append(layer) - ms.save_checkpoint(all_param, out_dir + f'network-snapshot-{cur_nimg // 1000:06d}-' + name + '.ckpt') + ms.save_checkpoint(all_param, os.path.join(out_dir, + f'network-snapshot-{cur_nimg // 1000:06d}-' + name + '.ckpt')) del module_copy -def save_image_snapshot(out_dir, cur_nimg, model, grid_z, grid_c, grid_size, concat): +def save_image_snapshot(out_dir, cur_nimg, model, length, truncation_psi, grid_size, concat): """ Save image snapshot. @@ -157,22 +159,24 @@ def save_image_snapshot(out_dir, cur_nimg, model, grid_z, grid_c, grid_size, con out_dir (str): Output directory. cur_nimg (int): Current number of images shown to discriminator. model (nn.Cell): Generator_ema inference model. - grid_z (tuple): Noise. - grid_c (tuple): Labels. + length (int): Number of images generated. + truncation_psi (float): GAN truncation trick. grid_size (tuple): Image grid size. concat (Operation): Concat operator. Examples: - >>> save_image_snapshot(out_dir, cur_nimg, model, grid_z, grid_c, grid_size, concat) + >>> save_image_snapshot(out_dir, cur_nimg, model, length, truncation_psi, grid_size, concat) """ - param_dict = load_checkpoint(out_dir + f'network-snapshot-{cur_nimg // 1000:06d}-G_ema.ckpt') + param_dict = load_checkpoint(os.path.join(out_dir, f'network-snapshot-{cur_nimg // 1000:06d}-G_ema.ckpt')) load_param_into_net(model, param_dict) images = [] - for z, c in zip(grid_z, grid_c): - ws = model.mapping.construct(z=z, c=c) - image = model.synthesis.construct(ws=ws, noise_mode=1) - images.append(image) + label = ms.numpy.zeros([1, model.c_dim]) + for seed in range(length): + z = Tensor(np.random.RandomState(seed).randn(1, model.z_dim)) + ws = model.mapping.construct(z, label, truncation_psi=truncation_psi) + img = model.synthesis.construct(ws, noise_mode=1) + images.append(img) images = concat(images).asnumpy() save_image_grid(images, os.path.join(out_dir, f'fakes{cur_nimg // 1000:06d}.png'), d_range=[-1, 1], size=grid_size) @@ -188,6 +192,8 @@ def train(args): >>> train(args) """ + context.set_context(device_target=args.device_target) + context.set_context(device_id=args.device_id) out_dir = args.out_dir snap = args.snap random_seed = args.seed @@ -208,8 +214,10 @@ def train(args): cur_tick = 0 batch_idx = 0 cur_nimg = 0 - kimg_per_tick = 4 + kimg_per_tick = 1 channel_base = 32768 if img_res >= 512 else 16384 + abort_fn = None + all_done = False if start_over: tick_start_nimg = cur_nimg @@ -221,10 +229,8 @@ def train(args): tick_start_nimg = cur_nimg if resume_paper is not None: - tick_start_nimg = 2404000 - - abort_fn = None - all_done = False + cur_nimg = 25000000 + tick_start_nimg = cur_nimg if img_res == 1024: lr = 0.002 @@ -264,30 +270,30 @@ def train(args): if not os.path.exists(out_dir): os.mkdir(out_dir) - grid_size, images, labels = setup_snapshot_image_grid(dataset=training_set) + grid_size, images, _ = setup_snapshot_image_grid(dataset=training_set) save_image_grid(images, os.path.join(out_dir, 'reals.png'), d_range=[0, 255], size=grid_size) - # load inference model to show inference images from model config-f stated in paper + # Load inference model to show inference images from model config-f stated in paper generator_ema = Generator(z_dim=512, w_dim=512, c_dim=0, - img_resolution=img_res, img_channels=3, batch_size=images.shape[0], + img_resolution=img_res, img_channels=3, mapping_kwargs=g_mapping_kwargs, synthesis_kwargs=g_synthesis_kwargs) if resume_paper is not None: param_dict = load_checkpoint(os.path.join(resume_paper, 'G_ema.ckpt')) load_param_into_net(generator_ema, param_dict) if resume_train is not None: - param_dict = load_checkpoint(os.path.join(resume_train, '-G_ema.ckpt')) + param_dict = load_checkpoint(resume_train + '-G_ema.ckpt') load_param_into_net(generator_ema, param_dict) - grid_z = split_gpu(Tensor(np.random.randn(labels.shape[0], generator_ema.z_dim), ms.float32)) - grid_c = split_gpu(Tensor(labels, ms.float32)) - + # Generate fake images. imgs = [] - for z, c in zip(grid_z, grid_c): - ws = generator_ema.mapping.construct(z=z, c=c) - img = generator_ema.synthesis.construct(ws=ws, noise_mode=1) + label = ms.numpy.zeros([1, generator_ema.c_dim]) + for seed in range(4): + z = Tensor(np.random.RandomState(seed).randn(1, generator_ema.z_dim)) + ws = generator_ema.mapping.construct(z, label, truncation_psi=0.5) + img = generator_ema.synthesis.construct(ws, noise_mode=1) imgs.append(img) - imgs = concat(imgs).asnumpy() + imgs = ops.Concat()(imgs).asnumpy() save_image_grid(imgs, os.path.join(out_dir, 'fakes_init.png'), d_range=[-1, 1], size=grid_size) print('Num images: ', len(training_set), '\nImage shape: ', training_set.image_shape, @@ -295,14 +301,10 @@ def train(args): start_time = time.time() - # load train model + # Load train model generator = Generator(z_dim=512, w_dim=512, c_dim=0, img_resolution=img_res, img_channels=3, batch_size=batch_size, train=True, mapping_kwargs=g_mapping_kwargs, synthesis_kwargs=g_synthesis_kwargs) - generator_ema = copy.deepcopy(generator) - for key, value in generator_ema.parameters_dict().items(): - if 'conv_list' in key: - value.requires_grad = False discriminator = Discriminator(c_dim=0, img_resolution=img_res, img_channels=3, block_kwargs={}, mapping_kwargs={}, epilogue_kwargs=d_epilogue_kwargs, batch_size=batch_size, channel_base=channel_base, channel_max=512, num_fp16_res=4, conv_clamp=256) @@ -311,7 +313,7 @@ def train(args): if resume_train is not None: for model_name, module in module_list: - param_dict = load_checkpoint(os.path.join(resume_paper, '-' + model_name + '.ckpt')) + param_dict = load_checkpoint(resume_train + '-' + model_name + '.ckpt') load_param_into_net(module, param_dict) if resume_paper is not None: @@ -353,16 +355,18 @@ def train(args): phase['module'].requires_grad = True # Calculate loss - for (real_img, real_c, gen_z, gen_c) in zip(whole_real_img, whole_real_c, whole_gen_z, whole_gen_c): + for idx, (real_img, real_c, gen_z, gen_c) in enumerate(zip(whole_real_img, whole_real_c, + whole_gen_z, whole_gen_c)): gain = phase['interval'] do_gmain = (phase['name'] in ['Gmain', 'Gboth']) do_dmain = (phase['name'] in ['Dmain', 'Dboth']) loss = phase['network'](do_gmain, do_dmain, real_img, real_c, gen_z, gen_c, gain) - print('%s loss: %f' % (phase['name'], loss)) + if idx % 100 == 0: + print('%s loss: %f' % (phase['name'], loss)) phase['module'].requires_grad = False # Update parameters for g_ema - ema_kimg = 5.0 + ema_kimg = 10 ema_nimg = ema_kimg * 1000 ema_beta = 0.5 ** (batch_size / max(ema_nimg, 1e-8)) for p_ema, p in zip(generator_ema.get_parameters(), generator.get_parameters()): @@ -401,7 +405,7 @@ def train(args): # Save image snapshot if (image_snapshot_ticks is not None) and (done or cur_tick % image_snapshot_ticks == 0): - save_image_snapshot(out_dir, cur_nimg, generator_ema, grid_z, grid_c, grid_size, concat) + save_image_snapshot(out_dir, cur_nimg, generator_ema, 4, 0.5, grid_size, concat) # Update state cur_tick += 1 @@ -414,31 +418,34 @@ def train(args): if all_done: break # Done - print('Training Completed!') + print('Training Completed.') def parse_args(): """ Parameter configuration """ - args = argparse.ArgumentParser(description='train') - - args.add_argument('--out_dir', help='path to save the generated images', type=str, default='./out/', metavar='DIR') - args.add_argument('--gpus', help='number of GPUs to use', type=int, default=1, metavar='INT') - args.add_argument('--snap', help='snapshot interval', type=int, default=10, metavar='INT') - args.add_argument('--seed', help='random seed', type=int, default=0, metavar='INT') - args.add_argument('--img_res', type=int, help='image resolution, ffhq=1024, lsun_wide=512', default=1024) - args.add_argument('--data_dir', help='training data', type=str, default='../dataset/ffhq.zip', metavar='PATH') - args.add_argument('--xflips', help='enable dataset x-flips', type=bool, default=False, metavar='BOOL') - args.add_argument('--total_kimg', help='total training duration', type=int, default=25000, metavar='INT') - args.add_argument('--batch_size', help='total batch size', type=int, default=2, metavar='INT') - args.add_argument('--start_over', help='start training from scratch', type=bool, default=False, metavar='BOOL') - args.add_argument('--resume_train', help='network snapshot ckpt path, ie ./out/network-snapshot-001500', - type=str, metavar='PATH') - args.add_argument('--resume_paper', help='path/directory where you save pre-trained ckpt, ie ./ckpt/ffhq/', - type=str, metavar='PATH') - - args = args.parse_args() + + parser = argparse.ArgumentParser(description='train') + parser.add_argument('--out_dir', help='path to generated images', type=str, default='./output/ffhq/', metavar='DIR') + parser.add_argument('--gpus', help='number of GPUs to use', type=int, default=1, metavar='INT') + parser.add_argument('--device_target', type=str, default='GPU', help='platform') + parser.add_argument('--device_id', type=int, default=0, help='appoint device_id if more than 1 device exist') + parser.add_argument('--snap', help='snapshot interval', type=int, default=10, metavar='INT') + parser.add_argument('--seed', help='random seed', type=int, default=0, metavar='INT') + parser.add_argument('--img_res', type=int, help='image resolution, ffhq=1024, lsun_wide=512', default=1024) + parser.add_argument('--data_dir', help='training data', type=str, default='../dataset/ffhq.zip', metavar='PATH') + parser.add_argument('--xflips', help='enable dataset x-flips', type=bool, default=False, metavar='BOOL') + parser.add_argument('--total_kimg', help='total number of images seen by discriminator in thousands', + type=int, default=25000, metavar='INT') + parser.add_argument('--batch_size', help='total batch size', type=int, default=2, metavar='INT') + parser.add_argument('--start_over', help='start training from scratch', type=bool, default=False, metavar='BOOL') + parser.add_argument('--resume_train', help='network snapshot ckpt path, ie ./output/ffhq/network-snapshot-001500', + type=str, metavar='PATH') + parser.add_argument('--resume_paper', help='path/directory where you save pre-trained ckpt, ie ./ckpt/ffhq/', + type=str, metavar='PATH') + + args = parser.parse_args() return args diff --git a/application_example/stylegan2/src/utils/ops/conv2d_resample.py b/application_example/stylegan2/src/utils/ops/conv2d_resample.py deleted file mode 100644 index c8fccdcb2bba205466cf47563dec857f2150470f..0000000000000000000000000000000000000000 --- a/application_example/stylegan2/src/utils/ops/conv2d_resample.py +++ /dev/null @@ -1,183 +0,0 @@ -# Copyright 2022 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -""" -2D convolution with optional up/downsampling. -""" - -from . import conv2d_gradfix -from . import upfirdn2d - - -def _get_weight_shape(w): - """ - Get the shape of the weight. - - Args: - w (Tensor): The weight. - - Returns: - List, the shape of weight. - - Examples: - >>> shape = _get_weight_shape(w) - """ - - shape = [sz for sz in w.shape] - return shape - - -def conv2d_wrapper(x, w, stride=1, padding=0, groups=1, transpose=False, flip_weight=True, conv_info=None): - """ - Wrapper for the underlying `conv2d()` and `conv_transpose2d()` implementations. - - Args: - x (Tensor): input. - w (Tensor): weight. - stride (int): stride. Default=1. - padding (int): padding. Default=0. - groups (int): groups. Default=1. - transpose (bool): need to transpose. Default=False. - flip_weight (bool): need to flip the weight. Default=True. - conv_info: Information of convolutional operators. Default: None. - - Returns: - Tensor, output of conv2d. - - Examples: - >>> x = conv2d_wrapper(x=x, w=w, groups=groups, transpose=True, flip_weight=True, conv_info=conv_info) - """ - - out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w) - - # Flip weight if requested. - if not flip_weight: - w = w.flip([2, 3]) - - # Workaround performance pitfall in cuDNN 8.0.5, triggered when using - # 1x1 kernel + memory_format=channels_last + less than 64 channels. - if kw == 1 and kh == 1 and stride == 1 and padding in [0, [0, 0], (0, 0)] and not transpose: - if x.strides[1] == 1: - if min(out_channels, in_channels_per_group) < 64: - if out_channels <= 4 and groups == 1: - in_shape = x.shape - x = w.squeeze(3).squeeze(2) @ x.reshape([in_shape[0], in_channels_per_group, -1]) - x = x.reshape([in_shape[0], out_channels, in_shape[2], in_shape[3]]) - else: - raise AssertionError('shape of x is nor correct') - # Otherwise => execute using conv2d_gradfix. - op = conv2d_gradfix.conv_transpose2d if transpose else conv2d_gradfix.conv2d - return op(x, w, conv_info=conv_info) - - -def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, - flip_weight=True, flip_filter=False, conv_info=None): - """ - 2D convolution with optional up/downsampling. - - Padding is performed only once at the beginning, not between the operations. - - Args: - x (Tensor): Input tensor of shape `[batch_size, in_channels, in_height, in_width]`. - w (Tensor): Weight tensor of shape `[out_channels, in_channels//groups, kernel_height, kernel_width]`. - f (Tensor): Low-pass filter for up/downsampling. Must be prepared beforehand by - calling upfirdn2d.setup_filter(). None = identity. Default: None. - up (int): Integer upsampling factor. Default: 1. - down (int): Integer downsampling factor. Default: 1. - padding (int): Padding with respect to the upsampled image. Can be a single number - or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`. Default: 0. - groups (int): Split input channels into N groups. Default: 1. - flip_weight (bool): False = convolution, True = correlation. Default: True. - flip_filter (bool): False = convolution, True = correlation. Default: False. - conv_info: Information of convolutional operators. Default: None. - - Returns: - Tensor, output tensor of the shape `[batch_size, num_channels, out_height, out_width]`. - - Examples: - >>> x = conv2d_resample(x=x, w=w, f=f, flip_weight=flip_weight, conv_info=conv_info) - """ - - # Validate arguments. - out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w) - fw, fh = upfirdn2d.get_filter_size(f) - px0, px1, py0, py1 = upfirdn2d.parse_padding(padding) - - # Adjust padding to account for up/downsampling - if up > 1: - px0 += (fw + up - 1) // 2 - px1 += (fw - up) // 2 - py0 += (fh + up - 1) // 2 - py1 += (fh - up) // 2 - if down > 1: - px0 += (fw - down + 1) // 2 - px1 += (fw - down) // 2 - py0 += (fh - down + 1) // 2 - py1 += (fh - down) // 2 - - if kw == 1 and kh == 1: - # Fast path: 1x1 convolution with downsampling only => downsample first, then convolve. - if down > 1 and up == 1: - x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, padding=[px0, px1, py0, py1], flip_filter=flip_filter, - conv_info=conv_info) - x = conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight, conv_info=conv_info) - return x - - # Fast path: 1x1 convolution with upsampling only => convolve first, then upsample. - if up > 1 and down == 1: - x = conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight, conv_info=conv_info) - x = upfirdn2d.upfirdn2d(x=x, f=f, up=up, padding=[px0, px1, py0, py1], gain=up ** 2, - flip_filter=flip_filter, conv_info=conv_info) - return x - - # Fast path: downsampling only => use strided convolution. - if down > 1 and up == 1: - x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0, px1, py0, py1], flip_filter=flip_filter, conv_info=conv_info) - x = conv2d_wrapper(x=x, w=w, stride=down, groups=groups, flip_weight=flip_weight, conv_info=conv_info) - return x - - if up > 1: - # Fast path: upsampling with optional downsampling => use transpose strided convolution. (if up > 1) - if groups == 1: - w = w.transpose(1, 0, 2, 3) - else: - w = w.reshape(groups, out_channels // groups, in_channels_per_group, kh, kw) - w = w.transpose(0, 2, 1, 3, 4) - w = w.reshape(groups * in_channels_per_group, out_channels // groups, kh, kw) - px0 -= kw - 1 - px1 -= kw - up - py0 -= kh - 1 - py1 -= kh - up - pxt = max(min(-px0, -px1), 0) - pyt = max(min(-py0, -py1), 0) - x = conv2d_wrapper(x=x, w=w, stride=up, padding=pxt, groups=groups, transpose=True, - flip_weight=(not flip_weight), conv_info=conv_info) - x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0 + pxt, px1 + pxt, py0 + pyt, py1 + pyt], gain=up ** 2, - flip_filter=flip_filter, conv_info=conv_info) - if down > 1: - x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter, conv_info=conv_info) - return x - - # Fast path: no up/downsampling, padding supported by the underlying implementation => use plain conv2d. - if up == 1 and down == 1: - if px0 == px1 and py0 == py1 and px0 >= 0 and py0 >= 0: - return conv2d_wrapper(x=x, w=w, padding=px0, groups=groups, flip_weight=flip_weight, conv_info=conv_info) - - # Fallback: Generic reference implementation. - x = upfirdn2d.upfirdn2d(x=x, f=(f if up > 1 else None), up=up, padding=[px0, px1, py0, py1], - gain=up**2, flip_filter=flip_filter, conv_info=conv_info) - x = conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight, conv_info=conv_info) - if down > 1: - x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter, conv_info=conv_info) - return x diff --git a/application_example/stylegan2/src/utils/ops/upfirdn2d.py b/application_example/stylegan2/src/utils/ops/upfirdn2d.py deleted file mode 100644 index eb1811a12873a7227a54f72f437eea1cb5d71d0b..0000000000000000000000000000000000000000 --- a/application_example/stylegan2/src/utils/ops/upfirdn2d.py +++ /dev/null @@ -1,327 +0,0 @@ -# Copyright 2022 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -""" -Custom ops for efficient resampling of 2D images. -""" - -import numpy as np -import mindspore as ms -import mindspore.numpy as mnp -import mindspore.common.dtype as mstype -from mindspore import Tensor -from mindspore.ops import operations as P -from mindspore._checkparam import Validator - -from . import conv2d_gradfix - - -class Pad(ms.nn.Cell): - """ - Pad operator, output the image after padding. - - Args: - paddings (tuple): Paddings. - mode (str): Padding mode. Options = ["CONSTANT", "REFLECT" or "SYMMETRIC"]. Default: "CONSTANT". - - Inputs: - - **x** (Tensor) - input tensor. - - Outputs: - Tensor, padding output. - - Supported Platforms: - ``Ascend`` ``GPU`` ``CPU`` - - Examples: - >>> pad_out = Pad(paddings=p) - """ - - def __init__(self, paddings, mode="CONSTANT"): - """Initialize Pad.""" - super(Pad, self).__init__() - self.mode = mode - self.paddings = paddings - Validator.check_string(self.mode, ["CONSTANT", "REFLECT", "SYMMETRIC"], 'mode', self.cls_name) - if not isinstance(paddings, tuple): - raise TypeError(f"For '{self.cls_name}', the type of 'paddings' must be tuple, " - f"but got {type(paddings).__name__}.") - for item in paddings: - if len(item) != 2: - raise ValueError(f"For '{self.cls_name}', the dimension of 'paddings' must be (n, 2), " - f"but got {paddings}.") - if mode == "CONSTANT": - self.pad = P.Pad(self.paddings) - else: - self.paddings = Tensor(np.array(self.paddings), mstype.int64) - self.pad = P.MirrorPad(mode=mode) - - def construct(self, x): - """Pad construct""" - if self.mode == "CONSTANT": - x = self.pad(x) - else: - x = self.pad(x, self.paddings) - return x - - -def parse_scaling(scaling): - """ - Return the padding. - - Args: - scaling (int): padding parameter. - - Returns: - int, x scaling parameter. - int, x scaling parameter. - - Examples: - >>> padx0, padx1, pady0, pady1 = parse_padding(padding) - """ - - if isinstance(scaling, int): - scaling = [scaling, scaling] - sx, sy = scaling - return sx, sy - - -def parse_padding(padding): - """ - Return the padding. - - Args: - padding (int): padding parameter. - - Returns: - int, x0 scaling parameter. - int, x1 scaling parameter. - int, y0 scaling parameter. - int, y1 scaling parameter. - - Examples: - >>> padx0, padx1, pady0, pady1 = parse_padding(padding) - """ - - if isinstance(padding, int): - padding = [padding, padding] - if len(padding) == 2: - padx, pady = padding - padding = [padx, padx, pady, pady] - return padding - - -def get_filter_size(f): - """ - Get the size of the filter. - - Args: - f (Tensor): Filter tensor. - - Returns: - int, Filter width. - int, Filter height. - - Examples: - >>> fw, fh = get_filter_size(f) - """ - - if f is None: - return 1, 1 - fw = f.shape[-1] - fh = f.shape[0] - return fw, fh - - -def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, conv_info=None): - """ - Pad, upsample, filter, and downsample a batch of 2D images. - - Performs the following sequence of operations for each channel: - 1. Upsample the image by inserting N-1 zeros after each pixel (`up`). - 2. Pad the image with the specified number of zeros on each side (`padding`). - Negative padding corresponds to cropping the image. - 3. Convolve the image with the specified 2D FIR filter (`f`), shrinking it - so that the footprint of all output pixels lies within the input image. - 4. Downsample the image by keeping every Nth pixel (`down`). - - This sequence of operations bears close resemblance to scipy.signal.upfirdn(). - The fused op is considerably more efficient than performing the same calculation - using standard PyTorch ops. It supports gradients of arbitrary order. - - Args: - x (Tensor): Float32/float64/float16 input tensor of the shape - `[batch_size, num_channels, in_height, in_width]`. - f (Tensor): Float32 FIR filter of the shape `[filter_height, filter_width]` (non-separable), - `[filter_taps]` (separable), or `None` (identity). - up (int): Integer upsampling factor. Can be a single int or a list/tuple `[x, y]`. Default: 1. - down (int): Integer downsampling factor. Can be a single int or a list/tuple `[x, y]`. Default: 1. - padding (int): Padding with respect to the upsampled image. Can be a single number - or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`. Default: 0. - flip_filter (bool): False = convolution, True = correlation. Default: False. - gain (int): Overall scaling factor for signal magnitude. Default: 1. - conv_info: Information of convolutional operators. Default: None. - - Returns: - Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. - - Examples: - >>> x = upfirdn2d(x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain, \ - conv_info=conv_info) - """ - - # Validate arguments. - if f is None: - f = ms.ops.Ones()((1, 1), ms.float32) - batch_size, num_channels, in_height, in_width = x.shape - upx, upy = parse_scaling(up) - downx, downy = parse_scaling(down) - padx0, padx1, pady0, pady1 = parse_padding(padding) - - # Upsample by inserting zeros. - x = x.reshape([batch_size, num_channels, in_height, 1, in_width, 1]) - pad1 = Pad(paddings=((0, 0), (0, 0), (0, 0), (0, upy - 1), (0, 0), (0, upx - 1))) - x = pad1(x) - x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx]) - - # Pad or crop. - pad2 = Pad(paddings=((0, 0), (0, 0), (max(pady0, 0), max(pady1, 0)), (max(padx0, 0), max(padx1, 0)))) - x = pad2(x) - max_y0 = max(-pady0, 0) - max_y1 = max(-pady1, 0) - max_x0 = max(-padx0, 0) - max_x1 = max(-padx1, 0) - x = x[:, :, max_y0: x.shape[2] - max_y1, max_x0: x.shape[3] - max_x1] - - # Setup filter. - f = f * (gain ** (f.ndim / 2)) - f = f.astype(x.dtype) - if not flip_filter: - f_dim = f.ndim - f = mnp.flip(f, list(range(f_dim))) - - # Convolve with the filter. - ff = f.astype(mnp.float32) - ff = mnp.tile(ff[np.newaxis, np.newaxis], ([num_channels, 1] + [1] * f.ndim)) - f = ff.astype(x.dtype) - if f.ndim == 4: - x = conv2d_gradfix.conv2d(x_input=x, weight=f, conv_info=conv_info) - else: - x = conv2d_gradfix.conv2d(x_input=x, weight=f.unsqueeze(2), conv_info=conv_info) - x = conv2d_gradfix.conv2d(x_input=x, weight=f.unsqueeze(3), conv_info=conv_info) - - # Downsample by throwing away pixels. - x = x[:, :, ::downy, ::downx] - return x - - -def filter2d(x, f, padding=0, flip_filter=False, gain=1, conv_info=None): - """ - Filter a batch of 2D images using the given 2D FIR filter. - - By default, the result is padded so that its shape matches the input. - User-specified padding is applied on top of that, with negative values - indicating cropping. Pixels outside the image are assumed to be zero. - - Args: - x (Tensor): Float32/float64/float16 input tensor of the shape - `[batch_size, num_channels, in_height, in_width]`. - f (Tensor): Float32 FIR filter of the shape `[filter_height, filter_width]` (non-separable), - `[filter_taps]` (separable), or `None` (identity). - padding (int): Padding with respect to the output. Can be a single number or a - list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`. Default: 0. - flip_filter (bool): False = convolution, True = correlation. Default: False. - gain (int): Overall scaling factor for signal magnitude. Default: 1. - conv_info: Information of convolutional operators. Default: None. - - Returns: - Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. - - Examples: - >>> x = filter2d(x, f, conv_info=conv_info) - """ - - padx0, padx1, pady0, pady1 = parse_padding(padding) - fw, fh = get_filter_size(f) - p = [padx0 + fw // 2, padx1 + (fw - 1) // 2, pady0 + fh // 2, pady1 + (fh - 1) // 2] - return upfirdn2d(x, f, padding=p, flip_filter=flip_filter, gain=gain, conv_info=conv_info) - - -def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, conv_info=None): - """ - Upsample a batch of 2D images using the given 2D FIR filter. - - By default, the result is padded so that its shape is a multiple of the input. - User-specified padding is applied on top of that, with negative values - indicating cropping. Pixels outside the image are assumed to be zero. - - Args: - x (Tensor): Float32/float64/float16 input tensor of the shape - `[batch_size, num_channels, in_height, in_width]`. - f (Tensor): Float32 FIR filter of the shape `[filter_height, filter_width]` (non-separable), - `[filter_taps]` (separable), or `None` (identity). - up (int): Integer upsampling factor. Can be a single int or a list/tuple `[x, y]`. Default: 2. - padding (int): Padding with respect to the upsampled image. Can be a single number - or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`. Default: 0. - flip_filter (bool): False = convolution, True = correlation. Default: False. - gain (int): Overall scaling factor for signal magnitude. Default: 1. - conv_info: Information of convolutional operators. Default: None. - - Returns: - Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. - - Examples: - >>> x = upsample2d(x, resample_filter, conv_info=conv_info) - """ - - upx, upy = parse_scaling(up) - padx0, padx1, pady0, pady1 = parse_padding(padding) - fw, fh = get_filter_size(f) - p = [padx0 + (fw + upx - 1) // 2, padx1 + (fw - upx) // 2, pady0 + (fh + upy - 1) // 2, pady1 + (fh - upy) // 2] - return upfirdn2d(x, f, up=up, padding=p, flip_filter=flip_filter, gain=gain*upx*upy, conv_info=conv_info) - - -def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, conv_info=None): - """ - Downsample a batch of 2D images using the given 2D FIR filter. - - By default, the result is padded so that its shape is a fraction of the input. - User-specified padding is applied on top of that, with negative values - indicating cropping. Pixels outside the image are assumed to be zero. - - Args: - x (Tensor): Float32/float64/float16 input tensor of the shape - `[batch_size, num_channels, in_height, in_width]`. - f (Tensor): Float32 FIR filter of the shape `[filter_height, filter_width]` (non-separable), - `[filter_taps]` (separable), or `None` (identity). - down (int): Integer downsampling factor. Can be a single int or a list/tuple `[x, y]`. Default: 2. - padding (int): Padding with respect to the upsampled image. Can be a single number - or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`. Default: 0. - flip_filter (bool): False = convolution, True = correlation. Default: False. - gain (int): Overall scaling factor for signal magnitude. Default: 1. - conv_info: Information of convolutional operators. Default: None. - - Returns: - Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. - - Examples: - >>> x = downsample2d(x, resample_filter, conv_info=conv_info) - """ - - downx, downy = parse_scaling(down) - padx0, padx1, pady0, pady1 = parse_padding(padding) - fw, fh = get_filter_size(f) - p = [padx0 + (fw - downx + 1) // 2, padx1 + (fw - downx) // 2, - pady0 + (fh - downy + 1) // 2, pady1 + (fh - downy) // 2] - return upfirdn2d(x, f, down=down, padding=p, flip_filter=flip_filter, gain=gain, conv_info=conv_info)