code: def __init__(self, input_channels):
      super().__init__()
      self.branch1x1 = BasicConv2d(input_channels, 320, kernel_size=1)
  
      self.branch3x3_1 = BasicConv2d(input_channels, 384, kernel_size=1)
      self.branch3x3_2a = BasicConv2d(
          384, 384, kernel_size=(1, 3), padding=(0, 1)
      )
      self.branch3x3_2b = BasicConv2d(
          384, 384, kernel_size=(3, 1), padding=(1, 0)
      )
  
      self.branch3x3stack_1 = BasicConv2d(
          input_channels, 448, kernel_size=1
      )
      self.branch3x3stack_2 = BasicConv2d(
          448, 384, kernel_size=3, padding=1
      )
      self.branch3x3stack_3a = BasicConv2d(
          384, 384, kernel_size=(1, 3), padding=(0, 1)
      )
      self.branch3x3stack_3b = BasicConv2d(
          384, 384, kernel_size=(3, 1), padding=(1, 0)
      )
  
      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 InceptionE object from inherited base classes: nn with example: obj = InceptionE(input_channels)