code: def __init__(self, input_channels, channels_7x7):
      super().__init__()
      self.branch1x1 = BasicConv2d(input_channels, 192, kernel_size=1)
  
      c7 = channels_7x7
  
      # In theory, we could go even further and argue that one can replace any n x n
      # convolution by a 1 x n convolution followed by a n x 1 convolution and the
      # computational cost saving increases dramatically as n grows (see figure 6).
      self.branch7x7 = nn.Sequential(
          BasicConv2d(input_channels, c7, kernel_size=1),
          BasicConv2d(c7, c7, kernel_size=(7, 1), padding=(3, 0)),
          BasicConv2d(c7, 192, kernel_size=(1, 7), padding=(0, 3)),
      )
  
      self.branch7x7stack = nn.Sequential(
          BasicConv2d(input_channels, c7, kernel_size=1),
          BasicConv2d(c7, c7, kernel_size=(7, 1), padding=(3, 0)),
          BasicConv2d(c7, c7, kernel_size=(1, 7), padding=(0, 3)),
          BasicConv2d(c7, c7, kernel_size=(7, 1), padding=(3, 0)),
          BasicConv2d(c7, 192, kernel_size=(1, 7), padding=(0, 3)),
      )
  
      self.branch_pool = nn.Sequential(
          nn.AvgPool2d(kernel_size=3, stride=1, padding=1),
          BasicConv2d(input_channels, 192, kernel_size=1),
      )
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new InceptionC object from inherited base classes: nn with example: obj = InceptionC(input_channels, channels_7x7)