code: def forward(self, x):
      # x -> 1x1 (same)
      branch1x1 = self.branch1x1(x)
  
      # x -> 1x1 -> 3x1
      # x -> 1x1 -> 1x3
      # concatenate(3x1, 1x3)
      # """7. Inception modules with expanded the filter bank outputs.
      # This architecture is used on the coarsest (8 x 8) grids to promote
      # high dimensional representations, as suggested by principle
      # 2 of Section 2."""
      branch3x3 = self.branch3x3_1(x)
      branch3x3 = [
          self.branch3x3_2a(branch3x3),
          self.branch3x3_2b(branch3x3),
      ]
      branch3x3 = torch.cat(branch3x3, 1)
  
      # x -> 1x1 -> 3x3 -> 1x3
      # x -> 1x1 -> 3x3 -> 3x1
      # concatenate(1x3, 3x1)
      branch3x3stack = self.branch3x3stack_1(x)
      branch3x3stack = self.branch3x3stack_2(branch3x3stack)
      branch3x3stack = [
          self.branch3x3stack_3a(branch3x3stack),
          self.branch3x3stack_3b(branch3x3stack),
      ]
      branch3x3stack = torch.cat(branch3x3stack, 1)
  
      branchpool = self.branch_pool(x)
  
      outputs = [branch1x1, branch3x3, branch3x3stack, branchpool]
  
      return torch.cat(outputs, 1)
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the InceptionE obj's underlying member method code with example usage: obj.forward(x) and returns: torch