class_docstr: Bi-Directional Feature Pyramid Network
class_docstr_tok: ["Bi-Directional","Feature","Pyramid","Network"]
code: def bottom_up_pathway(self, transition_features, inputs):
      # Calculate Bottom-Up Pathway
      # p3_out = p3_td
      # p4_out = self.p4_out(
      #     w2[0, 0] * p4_x + w2[1, 0] * p4_td + w2[2, 0] * F.interpolate(p3_out, size=p4_x.size()[2:])
      # )
      # p5_out = self.p5_out(
      #     w2[0, 1] * p5_x + w2[1, 1] * p5_td + w2[2, 1] * F.interpolate(p4_out, size=p5_x.size()[2:])
      # )
      # p6_out = self.p6_out(
      #     w2[0, 2] * p6_x + w2[1, 2] * p6_td + w2[2, 2] * F.interpolate(p5_out, size=p6_x.size()[2:])
      # )
      # p7_out = self.p7_out(
      #     w2[0, 3] * p7_x + w2[1, 3] * p7_td + w2[2, 3] * F.interpolate(p6_out, size=p7_x.size()[2:])
      # )
  
      w2 = torch.nn.functional.relu(self.w2)
      w2 = w2 / (torch.sum(w2, dim=0) + self.epsilon)
  
      outputs = [transition_features[-1]]
      transition_reversed = transition_features[:-1][::-1]
      for i, block in enumerate(self.bottom_up_blocks):
          x = inputs[i + 1]
          td = transition_reversed[i]
          y = block(
              x * w2[0, i]
              + td * w2[1, i]
              + F.interpolate(outputs[-1], size=x.size()[2:]) * w2[2, i]
          )
          outputs.append(y)
  
      return outputs
  
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 3 for calling the BiFPNBlock obj's underlying member method code with example usage: obj.bottom_up_pathway(transition_features, inputs) and returns: outputs