class_docstr: Adds a FPN on top of a model.
  Internally, it uses torchvision.models._utils.IntermediateLayerGetter to
  extract a submodel that returns the feature maps specified in return_layers.
  The same limitations of IntermediatLayerGetter apply here.
  Arguments:
      backbone (nn.Module)
      return_layers (Dict[name, new_name]): a dict containing the names
          of the modules for which the activations will be returned as
          the key of the dict, and the value of the dict is the name
          of the returned activation (which the user can specify).
      in_channels_list (List[int]): number of channels for each feature map
          that is returned, in the order they are present in the OrderedDict
      out_channels (int): number of channels in the FPN.
      extra_blocks: ExtraFPNBlock
  Attributes:
      out_channels (int): the number of channels in the FPN
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code: def forward(self, x):
      x = self.body(x)
      x = self.fpn(x)
      return x
  
imports: from collections import OrderedDict
  import torch.nn
  import torch
  from torch import Tensor
  import torch.nn.functional
  from torch.jit.annotations import Tuple, List, Dict
label_desc: Use image node_id 2 for calling the BackboneWithFPN obj's underlying member method code with example usage: obj.forward(x) and returns: x