code: def __init__(
      self,
      in_channels,
      ch1x1,
      ch3x3red,
      ch3x3,
      ch5x5red,
      ch5x5,
      pool_proj,
  ):
      super(Inception, self).__init__()
  
      self.branch1 = BasicConv2d(in_channels, ch1x1, kernel_size=1)
  
      self.branch2 = nn.Sequential(
          BasicConv2d(in_channels, ch3x3red, kernel_size=1),
          BasicConv2d(
              ch3x3red, ch3x3, kernel_size=3, padding=1
          ),  # 保证输出大小等于输入大小
      )
  
      self.branch3 = nn.Sequential(
          BasicConv2d(in_channels, ch5x5red, kernel_size=1),
          # 在官方的实现中，其实是3x3的kernel并不是5x5，这里我也懒得改了，具体可以参考下面的issue
          # Please see https://github.com/pytorch/vision/issues/906 for details.
          BasicConv2d(
              ch5x5red, ch5x5, kernel_size=5, padding=2
          ),  # 保证输出大小等于输入大小
      )
  
      self.branch4 = nn.Sequential(
          nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
          BasicConv2d(in_channels, pool_proj, kernel_size=1),
      )
  
file_path: deep-learning-for-image-processing/pytorch_classification/Test4_googlenet/model.py
imports: import torch.nn
  import torch
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new Inception object from inherited base classes: nn with example: obj = Inception(in_channels, ch1x1, ch3x3red, ch3x3, ch5x5red, ch5x5, pool_proj)