code: def forward(self, x):
      # N x 3 x 224 x 224
      x = self.conv1(x)
      # N x 64 x 112 x 112
      x = self.maxpool1(x)
      # N x 64 x 56 x 56
      x = self.conv2(x)
      # N x 64 x 56 x 56
      x = self.conv3(x)
      # N x 192 x 56 x 56
      x = self.maxpool2(x)
  
      # N x 192 x 28 x 28
      x = self.inception3a(x)
      # N x 256 x 28 x 28
      x = self.inception3b(x)
      # N x 480 x 28 x 28
      x = self.maxpool3(x)
      # N x 480 x 14 x 14
      x = self.inception4a(x)
      # N x 512 x 14 x 14
      if (
          self.training and self.aux_logits
      ):  # eval model lose this layer
          aux1 = self.aux1(x)
  
      x = self.inception4b(x)
      # N x 512 x 14 x 14
      x = self.inception4c(x)
      # N x 512 x 14 x 14
      x = self.inception4d(x)
      # N x 528 x 14 x 14
      if (
          self.training and self.aux_logits
      ):  # eval model lose this layer
          aux2 = self.aux2(x)
  
      x = self.inception4e(x)
      # N x 832 x 14 x 14
      x = self.maxpool4(x)
      # N x 832 x 7 x 7
      x = self.inception5a(x)
      # N x 832 x 7 x 7
      x = self.inception5b(x)
      # N x 1024 x 7 x 7
  
      x = self.avgpool(x)
      # N x 1024 x 1 x 1
      x = torch.flatten(x, 1)
      # N x 1024
      x = self.dropout(x)
      x = self.fc(x)
      # N x 1000 (num_classes)
      if (
          self.training and self.aux_logits
      ):  # eval model lose this layer
          return x, aux2, aux1
      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 GoogLeNet obj's underlying member method code with example usage: obj.forward(x) and returns: x, x,  aux2,  aux1