code: def __init__(self, in_channels, num_classes):
      super(InceptionAux, self).__init__()
      self.averagePool = nn.AvgPool2d(kernel_size=5, stride=3)
      self.conv = BasicConv2d(
          in_channels, 128, kernel_size=1
      )  # output[batch, 128, 4, 4]
  
      self.fc1 = nn.Linear(2048, 1024)
      self.fc2 = nn.Linear(1024, num_classes)
  
file_path: deep-learning-for-image-processing/pytorch_classification/Test4_googlenet/model.py
imports: import torch.nn
  import torch
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new InceptionAux object from inherited base classes: nn with example: obj = InceptionAux(in_channels, num_classes)