We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Need to put `norm_layer` as a parameter.
https://github.com/pytorch/vision/blob/684f48db4e6f619389da3a6957b3edebf794ae79/torchvision/models/detection/backbone_utils.py#L47

It works fine with resnet50_fpn, but when I try to use another backbone, for example `resnext101_32x8d`

`norm_layer=misc_nn_ops.FrozenBatchNorm2d` may trouble with Imagenet pretrained  weights, which use BatchNorm

```
Traceback (most recent call last):
  File "tmp.py", line 4, in <module>
    m = maskrcnn_resnext101_32x8d_rpn(pretrained=True)
  File "/mnt/data/luan/maskrcnn/models.py", line 218, in maskrcnn_resnext101_32x8d_rpn                                                                                      
    "resnext101_32x8d", pretrained=pretrained)
  File "/mnt/data/luan/anaconda3/envs/mask/lib/python3.6/site-packages/torchvision/models/detection/backbone_utils.py", line 47, in resnet_fpn_backbone                     
    norm_layer=misc_nn_ops.FrozenBatchNorm2d)
  File "/mnt/data/luan/anaconda3/envs/mask/lib/python3.6/site-packages/torchvision/models/resnet.py", line 313, in resnext101_32x8d                                         
    pretrained, progress, **kwargs)
  File "/mnt/data/luan/anaconda3/envs/mask/lib/python3.6/site-packages/torchvision/models/resnet.py", line 224, in _resnet                                                  
    model.load_state_dict(state_dict)
  File "/mnt/data/luan/anaconda3/envs/mask/lib/python3.6/site-packages/torch/nn/modules/module.py", line 830, in load_state_dict                                            
    self.__class__.__name__, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for ResNet:
        Unexpected key(s) in state_dict: "bn1.num_batches_tracked", "layer1.0.bn1.num_batches_tracked", "layer1.0.bn2.num_batches_tracked", "layer1.0.bn3.num_batches_tracked", "layer1.0.downsample.1.num_batches_tracked", "layer1.1.bn1.num_batches_tracked", "layer1.1.bn2.num_batches_tracked", "layer1.1.bn3.num_batches_tracked", "layer1.2.bn1.num_batches_tracked", "layer1.2.bn2.num_batches_tracked", "layer1.2.bn3.num_batches_tracked", "layer2.0.bn1.num_batches_tracked", "layer2.0.bn2.num_batches_tracked", "layer2.0.bn3.num_batches_tracked", "layer2.0.downsample.1.num_batches_tracked", "layer2.1.bn1.num_batches_tracked", "layer2.1.bn2.num_batches_tracked", "layer2.1.bn3.num_batches_tracked", "layer2.2.bn1.num_batches_tracked", "layer2.2.bn2.num_batches_tracked", "layer2.2.bn3.num_batches_tracked", "layer2.3.bn1.num_batches_tracked", "layer2.3.bn2.num_batches_tracked", "layer2.3.bn3.num_batches_tracked", "layer3.0.bn1.num_batches_tracked", "layer3.0.bn2.num_batches_tracked", "layer3.0.bn3.num_batches_tracked", "layer3.0.downsample.1.num_batches_tracked", "layer3.1.bn1.num_batches_tracked", "layer3.1.bn2.num_batches_tracked", "layer3.1.bn3.num_batches_tracked", "layer3.2.bn1.num_batches_tracked",
"layer3.2.bn2.num_batches_tracked", "layer3.2.bn3.num_batches_tracked", "layer3.3.bn1.num_batches_tracked", "layer3.3.bn2.num_batches_tracked", "layer3.3.bn3.num_batches_tracked", "layer3.4.bn1.num_batches_tracked", "layer3.4.bn2.num_batches_tracked", "layer3.4.bn3.num_batches_tracked", "layer3.5.bn1.num_batches_tracked", "layer3.5.bn2.num_batches_tracked", "layer3.5.bn3.num_batches_tracked", "layer3.6.bn1.num_batches_tracked", "layer3.6.bn2.num_batches_tracked", "layer3.6.bn3.num_batches_tracked", "layer3.7.bn1.num_batches_tracked", "layer3.7.bn2.num_batches_tracked", "layer3.7.bn3.num_batches_tracked", "layer3.8.bn1.num_batches_tracked", "layer3.8.bn2.num_batches_tracked", "layer3.8.bn3.num_batches_tracked", "layer3.9.bn1.num_batches_tracked", "layer3.9.bn2.num_batches_tracked", "layer3.9.bn3.num_batches_tracked", "layer3.10.bn1.num_batches_tracked",
"layer3.10.bn2.num_batches_tracked", "layer3.10.bn3.num_batches_tracked", "layer3.11.bn1.num_batches_tracked", "layer3.11.bn2.num_batches_tracked", "layer3.11.bn3.num_batches_tracked", "layer3.12.bn1.num_batches_tracked", "layer3.12.bn2.num_batches_tracked", "layer3.12.bn3.num_batches_tracked", "layer3.13.bn1.num_batches_tracked", "layer3.13.bn2.num_batches_tracked", "layer3.13.bn3.num_batches_tracked", "layer3.14.bn1.num_batches_tracked", "layer3.14.bn2.num_batches_tracked", "layer3.14.bn3.num_batches_tracked", "layer3.15.bn1.num_batches_tracked", "layer3.15.bn2.num_batches_tracked", "layer3.15.bn3.num_batches_tracked", "layer3.16.bn1.num_batches_tracked", "layer3.16.bn2.num_batches_tracked", "layer3.16.bn3.num_batches_tracked", "layer3.17.bn1.num_batches_tracked", "layer3.17.bn2.num_batches_tracked", "layer3.17.bn3.num_batches_tracked", "layer3.18.bn1.num_batches_tracked", "layer3.18.bn2.num_batches_tracked", "layer3.18.bn3.num_batches_tracked", "layer3.19.bn1.num_batches_tracked", "layer3.19.bn2.num_batches_tracked", "layer3.19.bn3.num_batches_tracked", "layer3.20.bn1.num_batches_tracked", "layer3.20.bn2.num_batches_tracked", "layer3.20.bn3.num_batches_tracked", "layer3.21.bn1.num_batches_tracked", "layer3.21.bn2.num_batches_tracked", "layer3.21.bn3.num_batches_tracked", "layer3.22.bn1.num_batches_tracked", "layer3.22.bn2.num_batches_tracked", "layer3.22.bn3.num_batches_tracked", "layer4.0.bn1.num_batches_tracked", "layer4.0.bn2.num_batches_tracked", "layer4.0.bn3.num_batches_tracked", "layer4.0.downsample.1.num_batches_tracked", "layer4.1.bn1.num_batches_tracked", "layer4.1.bn2.num_batches_tracked", "layer4.1.bn3.num_batches_tracked", "layer4.2.bn1.num_batches_tracked", "layer4.2.bn2.num_batches_tracked", "layer4.2.bn3.num_batches_tracked".

```

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `torchvision/models/detection/backbone_utils.py`
Content:
```
1 from collections import OrderedDict
2 from torch import nn
3 from torchvision.ops.feature_pyramid_network import FeaturePyramidNetwork, LastLevelMaxPool
4 
5 from torchvision.ops import misc as misc_nn_ops
6 from .._utils import IntermediateLayerGetter
7 from .. import resnet
8 
9 
10 class BackboneWithFPN(nn.Module):
11     """
12     Adds a FPN on top of a model.
13     Internally, it uses torchvision.models._utils.IntermediateLayerGetter to
14     extract a submodel that returns the feature maps specified in return_layers.
15     The same limitations of IntermediatLayerGetter apply here.
16     Arguments:
17         backbone (nn.Module)
18         return_layers (Dict[name, new_name]): a dict containing the names
19             of the modules for which the activations will be returned as
20             the key of the dict, and the value of the dict is the name
21             of the returned activation (which the user can specify).
22         in_channels_list (List[int]): number of channels for each feature map
23             that is returned, in the order they are present in the OrderedDict
24         out_channels (int): number of channels in the FPN.
25     Attributes:
26         out_channels (int): the number of channels in the FPN
27     """
28     def __init__(self, backbone, return_layers, in_channels_list, out_channels):
29         super(BackboneWithFPN, self).__init__()
30         self.body = IntermediateLayerGetter(backbone, return_layers=return_layers)
31         self.fpn = FeaturePyramidNetwork(
32             in_channels_list=in_channels_list,
33             out_channels=out_channels,
34             extra_blocks=LastLevelMaxPool(),
35         )
36         self.out_channels = out_channels
37 
38     def forward(self, x):
39         x = self.body(x)
40         x = self.fpn(x)
41         return x
42 
43 
44 def resnet_fpn_backbone(backbone_name, pretrained):
45     backbone = resnet.__dict__[backbone_name](
46         pretrained=pretrained,
47         norm_layer=misc_nn_ops.FrozenBatchNorm2d)
48     # freeze layers
49     for name, parameter in backbone.named_parameters():
50         if 'layer2' not in name and 'layer3' not in name and 'layer4' not in name:
51             parameter.requires_grad_(False)
52 
53     return_layers = {'layer1': '0', 'layer2': '1', 'layer3': '2', 'layer4': '3'}
54 
55     in_channels_stage2 = backbone.inplanes // 8
56     in_channels_list = [
57         in_channels_stage2,
58         in_channels_stage2 * 2,
59         in_channels_stage2 * 4,
60         in_channels_stage2 * 8,
61     ]
62     out_channels = 256
63     return BackboneWithFPN(backbone, return_layers, in_channels_list, out_channels)
64
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/torchvision/models/detection/backbone_utils.py b/torchvision/models/detection/backbone_utils.py
--- a/torchvision/models/detection/backbone_utils.py
+++ b/torchvision/models/detection/backbone_utils.py
@@ -41,10 +41,10 @@
         return x
 
 
-def resnet_fpn_backbone(backbone_name, pretrained):
+def resnet_fpn_backbone(backbone_name, pretrained, norm_layer=misc_nn_ops.FrozenBatchNorm2d):
     backbone = resnet.__dict__[backbone_name](
         pretrained=pretrained,
-        norm_layer=misc_nn_ops.FrozenBatchNorm2d)
+        norm_layer=norm_layer)
     # freeze layers
     for name, parameter in backbone.named_parameters():
         if 'layer2' not in name and 'layer3' not in name and 'layer4' not in name:

{"golden_diff": "diff --git a/torchvision/models/detection/backbone_utils.py b/torchvision/models/detection/backbone_utils.py\n--- a/torchvision/models/detection/backbone_utils.py\n+++ b/torchvision/models/detection/backbone_utils.py\n@@ -41,10 +41,10 @@\n         return x\n \n \n-def resnet_fpn_backbone(backbone_name, pretrained):\n+def resnet_fpn_backbone(backbone_name, pretrained, norm_layer=misc_nn_ops.FrozenBatchNorm2d):\n     backbone = resnet.__dict__[backbone_name](\n         pretrained=pretrained,\n-        norm_layer=misc_nn_ops.FrozenBatchNorm2d)\n+        norm_layer=norm_layer)\n     # freeze layers\n     for name, parameter in backbone.named_parameters():\n         if 'layer2' not in name and 'layer3' not in name and 'layer4' not in name:\n", "issue": "Need to put `norm_layer` as a parameter.\nhttps://github.com/pytorch/vision/blob/684f48db4e6f619389da3a6957b3edebf794ae79/torchvision/models/detection/backbone_utils.py#L47\r\n\r\nIt works fine with resnet50_fpn, but when I try to use another backbone, for example `resnext101_32x8d`\r\n\r\n`norm_layer=misc_nn_ops.FrozenBatchNorm2d` may trouble with Imagenet pretrained  weights, which use BatchNorm\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"tmp.py\", line 4, in <module>\r\n    m = maskrcnn_resnext101_32x8d_rpn(pretrained=True)\r\n  File \"/mnt/data/luan/maskrcnn/models.py\", line 218, in maskrcnn_resnext101_32x8d_rpn                                                                                      \r\n    \"resnext101_32x8d\", pretrained=pretrained)\r\n  File \"/mnt/data/luan/anaconda3/envs/mask/lib/python3.6/site-packages/torchvision/models/detection/backbone_utils.py\", line 47, in resnet_fpn_backbone                     \r\n    norm_layer=misc_nn_ops.FrozenBatchNorm2d)\r\n  File \"/mnt/data/luan/anaconda3/envs/mask/lib/python3.6/site-packages/torchvision/models/resnet.py\", line 313, in resnext101_32x8d                                         \r\n    pretrained, progress, **kwargs)\r\n  File \"/mnt/data/luan/anaconda3/envs/mask/lib/python3.6/site-packages/torchvision/models/resnet.py\", line 224, in _resnet                                                  \r\n    model.load_state_dict(state_dict)\r\n  File \"/mnt/data/luan/anaconda3/envs/mask/lib/python3.6/site-packages/torch/nn/modules/module.py\", line 830, in load_state_dict                                            \r\n    self.__class__.__name__, \"\\n\\t\".join(error_msgs)))\r\nRuntimeError: Error(s) in loading state_dict for ResNet:\r\n        Unexpected key(s) in state_dict: \"bn1.num_batches_tracked\", \"layer1.0.bn1.num_batches_tracked\", \"layer1.0.bn2.num_batches_tracked\", \"layer1.0.bn3.num_batches_tracked\", \"layer1.0.downsample.1.num_batches_tracked\", \"layer1.1.bn1.num_batches_tracked\", \"layer1.1.bn2.num_batches_tracked\", \"layer1.1.bn3.num_batches_tracked\", \"layer1.2.bn1.num_batches_tracked\", \"layer1.2.bn2.num_batches_tracked\", \"layer1.2.bn3.num_batches_tracked\", \"layer2.0.bn1.num_batches_tracked\", \"layer2.0.bn2.num_batches_tracked\", \"layer2.0.bn3.num_batches_tracked\", \"layer2.0.downsample.1.num_batches_tracked\", \"layer2.1.bn1.num_batches_tracked\", \"layer2.1.bn2.num_batches_tracked\", \"layer2.1.bn3.num_batches_tracked\", \"layer2.2.bn1.num_batches_tracked\", \"layer2.2.bn2.num_batches_tracked\", \"layer2.2.bn3.num_batches_tracked\", \"layer2.3.bn1.num_batches_tracked\", \"layer2.3.bn2.num_batches_tracked\", \"layer2.3.bn3.num_batches_tracked\", \"layer3.0.bn1.num_batches_tracked\", \"layer3.0.bn2.num_batches_tracked\", \"layer3.0.bn3.num_batches_tracked\", \"layer3.0.downsample.1.num_batches_tracked\", \"layer3.1.bn1.num_batches_tracked\", \"layer3.1.bn2.num_batches_tracked\", \"layer3.1.bn3.num_batches_tracked\", \"layer3.2.bn1.num_batches_tracked\",\r\n\"layer3.2.bn2.num_batches_tracked\", \"layer3.2.bn3.num_batches_tracked\", \"layer3.3.bn1.num_batches_tracked\", \"layer3.3.bn2.num_batches_tracked\", \"layer3.3.bn3.num_batches_tracked\", \"layer3.4.bn1.num_batches_tracked\", \"layer3.4.bn2.num_batches_tracked\", \"layer3.4.bn3.num_batches_tracked\", \"layer3.5.bn1.num_batches_tracked\", \"layer3.5.bn2.num_batches_tracked\", \"layer3.5.bn3.num_batches_tracked\", \"layer3.6.bn1.num_batches_tracked\", \"layer3.6.bn2.num_batches_tracked\", \"layer3.6.bn3.num_batches_tracked\", \"layer3.7.bn1.num_batches_tracked\", \"layer3.7.bn2.num_batches_tracked\", \"layer3.7.bn3.num_batches_tracked\", \"layer3.8.bn1.num_batches_tracked\", \"layer3.8.bn2.num_batches_tracked\", \"layer3.8.bn3.num_batches_tracked\", \"layer3.9.bn1.num_batches_tracked\", \"layer3.9.bn2.num_batches_tracked\", \"layer3.9.bn3.num_batches_tracked\", \"layer3.10.bn1.num_batches_tracked\",\r\n\"layer3.10.bn2.num_batches_tracked\", \"layer3.10.bn3.num_batches_tracked\", \"layer3.11.bn1.num_batches_tracked\", \"layer3.11.bn2.num_batches_tracked\", \"layer3.11.bn3.num_batches_tracked\", \"layer3.12.bn1.num_batches_tracked\", \"layer3.12.bn2.num_batches_tracked\", \"layer3.12.bn3.num_batches_tracked\", \"layer3.13.bn1.num_batches_tracked\", \"layer3.13.bn2.num_batches_tracked\", \"layer3.13.bn3.num_batches_tracked\", \"layer3.14.bn1.num_batches_tracked\", \"layer3.14.bn2.num_batches_tracked\", \"layer3.14.bn3.num_batches_tracked\", \"layer3.15.bn1.num_batches_tracked\", \"layer3.15.bn2.num_batches_tracked\", \"layer3.15.bn3.num_batches_tracked\", \"layer3.16.bn1.num_batches_tracked\", \"layer3.16.bn2.num_batches_tracked\", \"layer3.16.bn3.num_batches_tracked\", \"layer3.17.bn1.num_batches_tracked\", \"layer3.17.bn2.num_batches_tracked\", \"layer3.17.bn3.num_batches_tracked\", \"layer3.18.bn1.num_batches_tracked\", \"layer3.18.bn2.num_batches_tracked\", \"layer3.18.bn3.num_batches_tracked\", \"layer3.19.bn1.num_batches_tracked\", \"layer3.19.bn2.num_batches_tracked\", \"layer3.19.bn3.num_batches_tracked\", \"layer3.20.bn1.num_batches_tracked\", \"layer3.20.bn2.num_batches_tracked\", \"layer3.20.bn3.num_batches_tracked\", \"layer3.21.bn1.num_batches_tracked\", \"layer3.21.bn2.num_batches_tracked\", \"layer3.21.bn3.num_batches_tracked\", \"layer3.22.bn1.num_batches_tracked\", \"layer3.22.bn2.num_batches_tracked\", \"layer3.22.bn3.num_batches_tracked\", \"layer4.0.bn1.num_batches_tracked\", \"layer4.0.bn2.num_batches_tracked\", \"layer4.0.bn3.num_batches_tracked\", \"layer4.0.downsample.1.num_batches_tracked\", \"layer4.1.bn1.num_batches_tracked\", \"layer4.1.bn2.num_batches_tracked\", \"layer4.1.bn3.num_batches_tracked\", \"layer4.2.bn1.num_batches_tracked\", \"layer4.2.bn2.num_batches_tracked\", \"layer4.2.bn3.num_batches_tracked\".\r\n\r\n```\n", "before_files": [{"content": "from collections import OrderedDict\nfrom torch import nn\nfrom torchvision.ops.feature_pyramid_network import FeaturePyramidNetwork, LastLevelMaxPool\n\nfrom torchvision.ops import misc as misc_nn_ops\nfrom .._utils import IntermediateLayerGetter\nfrom .. import resnet\n\n\nclass BackboneWithFPN(nn.Module):\n    \"\"\"\n    Adds a FPN on top of a model.\n    Internally, it uses torchvision.models._utils.IntermediateLayerGetter to\n    extract a submodel that returns the feature maps specified in return_layers.\n    The same limitations of IntermediatLayerGetter apply here.\n    Arguments:\n        backbone (nn.Module)\n        return_layers (Dict[name, new_name]): a dict containing the names\n            of the modules for which the activations will be returned as\n            the key of the dict, and the value of the dict is the name\n            of the returned activation (which the user can specify).\n        in_channels_list (List[int]): number of channels for each feature map\n            that is returned, in the order they are present in the OrderedDict\n        out_channels (int): number of channels in the FPN.\n    Attributes:\n        out_channels (int): the number of channels in the FPN\n    \"\"\"\n    def __init__(self, backbone, return_layers, in_channels_list, out_channels):\n        super(BackboneWithFPN, self).__init__()\n        self.body = IntermediateLayerGetter(backbone, return_layers=return_layers)\n        self.fpn = FeaturePyramidNetwork(\n            in_channels_list=in_channels_list,\n            out_channels=out_channels,\n            extra_blocks=LastLevelMaxPool(),\n        )\n        self.out_channels = out_channels\n\n    def forward(self, x):\n        x = self.body(x)\n        x = self.fpn(x)\n        return x\n\n\ndef resnet_fpn_backbone(backbone_name, pretrained):\n    backbone = resnet.__dict__[backbone_name](\n        pretrained=pretrained,\n        norm_layer=misc_nn_ops.FrozenBatchNorm2d)\n    # freeze layers\n    for name, parameter in backbone.named_parameters():\n        if 'layer2' not in name and 'layer3' not in name and 'layer4' not in name:\n            parameter.requires_grad_(False)\n\n    return_layers = {'layer1': '0', 'layer2': '1', 'layer3': '2', 'layer4': '3'}\n\n    in_channels_stage2 = backbone.inplanes // 8\n    in_channels_list = [\n        in_channels_stage2,\n        in_channels_stage2 * 2,\n        in_channels_stage2 * 4,\n        in_channels_stage2 * 8,\n    ]\n    out_channels = 256\n    return BackboneWithFPN(backbone, return_layers, in_channels_list, out_channels)\n", "path": "torchvision/models/detection/backbone_utils.py"}], "after_files": [{"content": "from collections import OrderedDict\nfrom torch import nn\nfrom torchvision.ops.feature_pyramid_network import FeaturePyramidNetwork, LastLevelMaxPool\n\nfrom torchvision.ops import misc as misc_nn_ops\nfrom .._utils import IntermediateLayerGetter\nfrom .. import resnet\n\n\nclass BackboneWithFPN(nn.Module):\n    \"\"\"\n    Adds a FPN on top of a model.\n    Internally, it uses torchvision.models._utils.IntermediateLayerGetter to\n    extract a submodel that returns the feature maps specified in return_layers.\n    The same limitations of IntermediatLayerGetter apply here.\n    Arguments:\n        backbone (nn.Module)\n        return_layers (Dict[name, new_name]): a dict containing the names\n            of the modules for which the activations will be returned as\n            the key of the dict, and the value of the dict is the name\n            of the returned activation (which the user can specify).\n        in_channels_list (List[int]): number of channels for each feature map\n            that is returned, in the order they are present in the OrderedDict\n        out_channels (int): number of channels in the FPN.\n    Attributes:\n        out_channels (int): the number of channels in the FPN\n    \"\"\"\n    def __init__(self, backbone, return_layers, in_channels_list, out_channels):\n        super(BackboneWithFPN, self).__init__()\n        self.body = IntermediateLayerGetter(backbone, return_layers=return_layers)\n        self.fpn = FeaturePyramidNetwork(\n            in_channels_list=in_channels_list,\n            out_channels=out_channels,\n            extra_blocks=LastLevelMaxPool(),\n        )\n        self.out_channels = out_channels\n\n    def forward(self, x):\n        x = self.body(x)\n        x = self.fpn(x)\n        return x\n\n\ndef resnet_fpn_backbone(backbone_name, pretrained, norm_layer=misc_nn_ops.FrozenBatchNorm2d):\n    backbone = resnet.__dict__[backbone_name](\n        pretrained=pretrained,\n        norm_layer=norm_layer)\n    # freeze layers\n    for name, parameter in backbone.named_parameters():\n        if 'layer2' not in name and 'layer3' not in name and 'layer4' not in name:\n            parameter.requires_grad_(False)\n\n    return_layers = {'layer1': '0', 'layer2': '1', 'layer3': '2', 'layer4': '3'}\n\n    in_channels_stage2 = backbone.inplanes // 8\n    in_channels_list = [\n        in_channels_stage2,\n        in_channels_stage2 * 2,\n        in_channels_stage2 * 4,\n        in_channels_stage2 * 8,\n    ]\n    out_channels = 256\n    return BackboneWithFPN(backbone, return_layers, in_channels_list, out_channels)\n", "path": "torchvision/models/detection/backbone_utils.py"}]}