code: def forward(self, X):
      y = self.relu(self.in1(self.conv1(X)))
      y = self.relu(self.in2(self.conv2(y)))
      y = self.relu(self.in3(self.conv3(y)))
      y = self.res1(y)
      y = self.res2(y)
      y = self.res3(y)
      y = self.res4(y)
      y = self.res5(y)
      y = self.relu(self.in4(self.deconv1(y)))
      y = self.relu(self.in5(self.deconv2(y)))
      y = self.deconv3(y)
      return y
  
file_path: ignite/examples/fast_neural_style/transformer_net.py
imports: import torch
label_desc: Use image node_id 2 for calling the TransformerNet obj's underlying member method code with example usage: obj.forward(X) and returns: y