class_docstr: Bi-Directional Feature Pyramid Network
class_docstr_tok: ["Bi-Directional","Feature","Pyramid","Network"]
code: def forward(self, inputs: List[Tensor]) -> List[Tensor]:
      transition_features = self.top_down_pathway(inputs)
      return self.bottom_up_pathway(transition_features, inputs)
  
imports: from typing import List, Tuple, Union, Type, Callable
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from pytorch_toolbelt.modules.activations import ACT_RELU, instantiate_activation_block
  from pytorch_toolbelt.modules.simple import conv1x1
  from pytorch_toolbelt.modules.dsconv import DepthwiseSeparableConv2dBlock
  from pytorch_toolbelt.modules.interfaces import AbstractDecoder, FeatureMapsSpecification
  from pytorch_toolbelt.modules.normalization import NORM_BATCH, instantiate_normalization_block
label_desc: Use image node_id 2 for calling the BiFPNBlock obj's underlying member method code with example usage: obj.forward(inputs) and returns: self