code: def __init__(self, num_classes=10):
      super().__init__()
      self.Conv2d_1a_3x3 = BasicConv2d(3, 32, kernel_size=3, padding=1)
      self.Conv2d_2a_3x3 = BasicConv2d(32, 32, kernel_size=3, padding=1)
      self.Conv2d_2b_3x3 = BasicConv2d(32, 64, kernel_size=3, padding=1)
      self.Conv2d_3b_1x1 = BasicConv2d(64, 80, kernel_size=1)
      self.Conv2d_4a_3x3 = BasicConv2d(80, 192, kernel_size=3)
  
      # naive inception module
      self.Mixed_5b = InceptionA(192, pool_features=32)
      self.Mixed_5c = InceptionA(256, pool_features=64)
      self.Mixed_5d = InceptionA(288, pool_features=64)
  
      # downsample
      self.Mixed_6a = InceptionB(288)
  
      self.Mixed_6b = InceptionC(768, channels_7x7=128)
      self.Mixed_6c = InceptionC(768, channels_7x7=160)
      self.Mixed_6d = InceptionC(768, channels_7x7=160)
      self.Mixed_6e = InceptionC(768, channels_7x7=192)
  
      # downsample
      self.Mixed_7a = InceptionD(768)
  
      self.Mixed_7b = InceptionE(1280)
      self.Mixed_7c = InceptionE(2048)
  
      # 6*6 feature size
      self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
      self.dropout = nn.Dropout2d()
      self.linear = nn.Linear(2048, num_classes)
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new InceptionV3 object from inherited base classes: nn with example: obj = InceptionV3(num_classes)