class_docstr: Module that adds a FPN from on top of a set of feature maps. This is based on
  `"Feature Pyramid Network for Object Detection" <https://arxiv.org/abs/1612.03144>`_.
  The feature maps are currently supposed to be in increasing depth
  order.
  The input to the model is expected to be an OrderedDict[Tensor], containing
  the feature maps on top of which the FPN will be added.
  Arguments:
      in_channels_list (list[int]): number of channels for each feature map that
          is passed to the module
      out_channels (int): number of channels of the FPN representation
      extra_blocks (ExtraFPNBlock or None): if provided, extra operations will
          be performed. It is expected to take the fpn features, the original
          features and the names of the original features as input, and returns
          a new list of feature maps and their corresponding names
docstr: This is equivalent to self.inner_blocks[idx](x),
  but torchscript doesn't support this yet
docstr_tok: ["This","is","equivalent","to","self.inner_blocks","[","idx","]","(","x",")",",","but","torchscript","does","n't","support","this","yet"]
code: def get_result_from_inner_blocks(self, x: Tensor, idx: int) -> Tensor:
      """
      This is equivalent to self.inner_blocks[idx](x),
      but torchscript doesn't support this yet
      """
      num_blocks = len(self.inner_blocks)
      if idx < 0:
          idx += num_blocks
      i = 0
      out = x
      for module in self.inner_blocks:
          if i == idx:
              out = module(x)
          i += 1
      return out
  
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 FeaturePyramidNetwork obj's underlying member method code with example usage: obj.get_result_from_inner_blocks(x, idx) and returns: out