diff --git "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md" "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md"
index 31c03490c4743e1cc9d7aefd4f6734f5884b5bba..6c9eef7278ae5efe82e99629505135c9c61b1f00 100644
--- "a/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md"	
+++ "b/docs/zh/PyTorch API\346\224\257\346\214\201\346\270\205\345\215\225_1.5.0.md"	
@@ -788,19 +788,7 @@
 | 121  | torch.nn.BatchNorm3d                                     | 是                           |
 | 122  | torch.nn.GroupNorm                                       | 是                           |
 | 123  | torch.nn.SyncBatchNorm                                   | 是                           |
-<<<<<<< HEAD
-<<<<<<< HEAD
-<<<<<<< HEAD
 | 124  | torch.nn.SyncBatchNorm.convert_sync_batchnorm            | 是                           |
-=======
-| 124  | torch.nn.SyncBatchNorm.convert_sync_batchnorm            | 否                           |
->>>>>>> 34d323c (new api support)
-=======
-| 124  | torch.nn.SyncBatchNorm.convert_sync_batchnorm            | 是                           |
->>>>>>> 0585771 (new api support)
-=======
-| 124  | torch.nn.SyncBatchNorm.convert_sync_batchnorm            | 是                           |
->>>>>>> ba861db (new file)
 | 125  | torch.nn.InstanceNorm1d                                  | 是                           |
 | 126  | torch.nn.InstanceNorm2d                                  | 是                           |
 | 127  | torch.nn.InstanceNorm3d                                  | 是                           |
@@ -3251,7 +3239,7 @@ Rotate Bounding Box Encoding.
 
 - Examples：
 
-  ```python
+  ```
   >>> anchor_boxes = torch.tensor([[[30.69], [32.6], [45.94], [59.88], [-44.53]]], dtype=torch.float16).to("npu")
       >>> gt_bboxes = torch.tensor([[[30.44], [18.72], [33.22], [45.56], [8.5]]], dtype=torch.float16).to("npu")
       >>> weight = torch.tensor([1., 1., 1., 1., 1.], dtype=torch.float16).npu()
@@ -3264,32 +3252,33 @@ Rotate Bounding Box Encoding.
               [ 1.1328]]], device='npu:0', dtype=torch.float16)
   ```
 
->   npu_rotated_box_decode(anchor_boxes, deltas, weight) -> Tensor
+  >   npu_rotated_box_decode(anchor_boxes, deltas, weight) -> Tensor
 
-Rotate Bounding Box Encoding
+  Rotate Bounding Box Encoding
 
-- Parameters：
+  - Parameters：
 
-  - anchor_box (Tensor) -  A 3D Tensor with shape (B, 5, N). the input tensor.Anchor boxes. "B" indicates the number of batch size, "N" indicates the number of bounding boxes, and the value "5" refers to "x0", "x1", "y0", "y1" and "angle" .
-  - deltas (Tensor) - A 3D Tensor of float32 (float16) with shape (B, 5, N).   
-  - weight (Tensor) - A float list for "x0", "x1", "y0", "y1" and "angle", defaults to [1.0, 1.0, 1.0, 1.0, 1.0].
+    - anchor_box (Tensor) -  A 3D Tensor with shape (B, 5, N). the input tensor.Anchor boxes. "B" indicates the number of batch size, "N" indicates the number of bounding boxes, and the value "5" refers to "x0", "x1", "y0", "y1" and "angle" .
+    - deltas (Tensor) - A 3D Tensor of float32 (float16) with shape (B, 5, N).   
+    - weight (Tensor) - A float list for "x0", "x1", "y0", "y1" and "angle", defaults to [1.0, 1.0, 1.0, 1.0, 1.0].
 
-- constraints：
+  - constraints：
 
-  None
+    None
 
-- Examples：
+  - Examples：
 
-  ```python
-   >>> anchor_boxes = torch.tensor([[[4.137],[33.72],[29.4], [54.06], [41.28]]], dtype=torch.float16).to("npu")
-      >>> deltas = torch.tensor([[[0.0244], [-1.992], [0.2109], [0.315], [-37.25]]], dtype=torch.float16).to("npu")
-      >>> weight = torch.tensor([1., 1., 1., 1., 1.], dtype=torch.float16).npu()
-      >>> out = torch.npu_rotated_box_decode(anchor_boxes, deltas, weight)
-      >>> out
-      tensor([[[  1.7861],
-              [-10.5781],
-              [ 33.0000],
-              [ 17.2969],
-              [-88.4375]]], device='npu:0', dtype=torch.float16)
-  ```
+    ```
+     >>> anchor_boxes = torch.tensor([[[4.137],[33.72],[29.4], [54.06], [41.28]]], dtype=torch.float16).to("npu")
+        >>> deltas = torch.tensor([[[0.0244], [-1.992], [0.2109], [0.315], [-37.25]]], dtype=torch.float16).to("npu")
+        >>> weight = torch.tensor([1., 1., 1., 1., 1.], dtype=torch.float16).npu()
+        >>> out = torch.npu_rotated_box_decode(anchor_boxes, deltas, weight)
+        >>> out
+        tensor([[[  1.7861],
+                [-10.5781],
+                [ 33.0000],
+                [ 17.2969],
+                [-88.4375]]], device='npu:0', dtype=torch.float16)
+    ```
 
+