code: def forward(self, x: Tensor) -> Tensor:
      if self.stride == 1:
          x1, x2 = x.chunk(2, dim=1)
          out = torch.cat((x1, self.branch2(x2)), dim=1)
      else:
          out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)
  
      out = channel_shuffle(out, 2)
  
      return out
  
file_path: deep-learning-for-image-processing/pytorch_classification/Test7_shufflenet/model.py
imports: from typing import List, Callable
  import torch
  from torch import Tensor
  import torch.nn
label_desc: Use image node_id 3 for calling the InvertedResidual obj's underlying member method code with example usage: obj.forward(x) and returns: out