code: def __init__(
      self, in_planes, n1x1, n3x3red, n3x3, n5x5red, n5x5, pool_planes
  ):
      super(Inception, self).__init__()
      # 1x1 conv branch
      self.b1 = nn.Sequential(
          nn.Conv2d(in_planes, n1x1, kernel_size=1),
          nn.BatchNorm2d(n1x1),
          nn.ReLU(True),
      )
  
      # 1x1 conv -> 3x3 conv branch
      self.b2 = nn.Sequential(
          nn.Conv2d(in_planes, n3x3red, kernel_size=1),
          nn.BatchNorm2d(n3x3red),
          nn.ReLU(True),
          nn.Conv2d(n3x3red, n3x3, kernel_size=3, padding=1),
          nn.BatchNorm2d(n3x3),
          nn.ReLU(True),
      )
  
      # 1x1 conv -> 5x5 conv branch
      self.b3 = nn.Sequential(
          nn.Conv2d(in_planes, n5x5red, kernel_size=1),
          nn.BatchNorm2d(n5x5red),
          nn.ReLU(True),
          nn.Conv2d(n5x5red, n5x5, kernel_size=3, padding=1),
          nn.BatchNorm2d(n5x5),
          nn.ReLU(True),
          nn.Conv2d(n5x5, n5x5, kernel_size=3, padding=1),
          nn.BatchNorm2d(n5x5),
          nn.ReLU(True),
      )
  
      # 3x3 pool -> 1x1 conv branch
      self.b4 = nn.Sequential(
          nn.MaxPool2d(3, stride=1, padding=1),
          nn.Conv2d(in_planes, pool_planes, kernel_size=1),
          nn.BatchNorm2d(pool_planes),
          nn.ReLU(True),
      )
  
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 Inception object from inherited base classes: nn with example: obj = Inception(in_planes, n1x1, n3x3red, n3x3, n5x5red, n5x5, pool_planes)