code: def __init__(self, in_channels, use_avg_pool):
      super().__init__()
      self.conv = Conv2dLayer(in_channels, 320, kh=1, kw=1)
      self.tower_conv = Conv2dLayer(in_channels, 384, kh=1, kw=1)
      self.tower_mixed_conv = Conv2dLayer(
          384, 384, kh=1, kw=3, padding=[0, 1]
      )
      self.tower_mixed_conv_1 = Conv2dLayer(
          384, 384, kh=3, kw=1, padding=[1, 0]
      )
      self.tower_1_conv = Conv2dLayer(in_channels, 448, kh=1, kw=1)
      self.tower_1_conv_1 = Conv2dLayer(448, 384, kh=3, kw=3, padding=1)
      self.tower_1_mixed_conv = Conv2dLayer(
          384, 384, kh=1, kw=3, padding=[0, 1]
      )
      self.tower_1_mixed_conv_1 = Conv2dLayer(
          384, 384, kh=3, kw=1, padding=[1, 0]
      )
      if use_avg_pool:
          self.tower_2_pool = torch.nn.AvgPool2d(
              kernel_size=3,
              stride=1,
              padding=1,
              count_include_pad=False,
          )
      else:
          self.tower_2_pool = torch.nn.MaxPool2d(
              kernel_size=3, stride=1, padding=1
          )
      self.tower_2_conv = Conv2dLayer(in_channels, 192, kh=1, kw=1)
  
imports: import collections
  import torch
label_desc: Use image node_id 1 to create a new InceptionE object from inherited base classes: torch.nn with example: obj = InceptionE(in_channels, use_avg_pool)