class_docstr: Bi-Directional Feature Pyramid Network
class_docstr_tok: ["Bi-Directional","Feature","Pyramid","Network"]
code: def top_down_pathway(self, inputs: List[Tensor]) -> List[Tensor]:
      # p6_td = self.p6_td(w1[0, 0] * p6_x + w1[1, 0] * F.interpolate(p7_td, size=p6_x.size()[2:]))
      # p5_td = self.p5_td(w1[0, 1] * p5_x + w1[1, 1] * F.interpolate(p6_td, size=p5_x.size()[2:]))
      # p4_td = self.p4_td(w1[0, 2] * p4_x + w1[1, 2] * F.interpolate(p5_td, size=p4_x.size()[2:]))
      # p3_td = self.p3_td(w1[0, 3] * p3_x + w1[1, 3] * F.interpolate(p4_td, size=p3_x.size()[2:]))
  
      w1 = torch.nn.functional.relu(self.w1)
      w1 = w1 / (torch.sum(w1, dim=0) + self.epsilon)
  
      features = [inputs[-1]]
      inputs_reversed = inputs[:-1][::-1]
  
      for i, block in enumerate(self.top_down_blocks):
          x = inputs_reversed[i]
          y = block(
              w1[0, i] * x
              + w1[1, i]
              * F.interpolate(features[-1], size=x.size()[2:])
          )
          features.append(y)
  
      return features
  
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 4 for calling the BiFPNBlock obj's underlying member method code with example usage: obj.top_down_pathway(inputs) and returns: features