code: def forward(self, x):
      # aux1: N x 512 x 14 x 14, aux2: N x 528 x 14 x 14
      x = self.averagePool(x)
      # aux1: N x 512 x 4 x 4, aux2: N x 528 x 4 x 4
      x = self.conv(x)
      # N x 128 x 4 x 4
      x = torch.flatten(x, 1)
      x = F.dropout(x, 0.5, training=self.training)
      # N x 2048
      x = F.relu(self.fc1(x), inplace=True)
      x = F.dropout(x, 0.5, training=self.training)
      # N x 1024
      x = self.fc2(x)
      # N x num_classes
      return x
  
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 2 for calling the InceptionAux obj's underlying member method code with example usage: obj.forward(x) and returns: x