code: def __init__(self):
      super(GoogLeNet, self).__init__()
      self.pre_layers = nn.Sequential(
          nn.Conv2d(3, 192, kernel_size=3, padding=1),
          nn.BatchNorm2d(192),
          nn.ReLU(True),
      )
  
      self.a3 = Inception(192, 64, 96, 128, 16, 32, 32)
      self.b3 = Inception(256, 128, 128, 192, 32, 96, 64)
  
      self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)
  
      self.a4 = Inception(480, 192, 96, 208, 16, 48, 64)
      self.b4 = Inception(512, 160, 112, 224, 24, 64, 64)
      self.c4 = Inception(512, 128, 128, 256, 24, 64, 64)
      self.d4 = Inception(512, 112, 144, 288, 32, 64, 64)
      self.e4 = Inception(528, 256, 160, 320, 32, 128, 128)
  
      self.a5 = Inception(832, 256, 160, 320, 32, 128, 128)
      self.b5 = Inception(832, 384, 192, 384, 48, 128, 128)
  
      self.avgpool = nn.AvgPool2d(8, stride=1)
      self.linear = nn.Linear(1024, 10)
  
file_path: nni/examples/trials/cifar10_pytorch/models/googlenet.py
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new GoogLeNet object from inherited base classes: nn with example: obj = GoogLeNet()