code: def forward(self, x):
      # x - > 3x3(downsample)
      branch3x3 = self.branch3x3(x)
  
      # x -> 3x3 -> 3x3(downsample)
      branch3x3stack = self.branch3x3stack(x)
  
      # x -> avgpool(downsample)
      branchpool = self.branchpool(x)
  
      # """We can use two parallel stride 2 blocks: P and C. P is a pooling
      # layer (either average or maximum pooling) the activation, both of
      # them are stride 2 the filter banks of which are concatenated as in
      # figure 10."""
      outputs = [branch3x3, branch3x3stack, branchpool]
  
      return torch.cat(outputs, 1)
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the InceptionB obj's underlying member method code with example usage: obj.forward(x) and returns: torch