code: def __init__(self):
      super(TransformerNet, self).__init__()
      # Initial convolution layers
      self.conv1 = ConvLayer(3, 32, kernel_size=9, stride=1)
      self.in1 = torch.nn.InstanceNorm2d(32, affine=True)
      self.conv2 = ConvLayer(32, 64, kernel_size=3, stride=2)
      self.in2 = torch.nn.InstanceNorm2d(64, affine=True)
      self.conv3 = ConvLayer(64, 128, kernel_size=3, stride=2)
      self.in3 = torch.nn.InstanceNorm2d(128, affine=True)
      # Residual layers
      self.res1 = ResidualBlock(128)
      self.res2 = ResidualBlock(128)
      self.res3 = ResidualBlock(128)
      self.res4 = ResidualBlock(128)
      self.res5 = ResidualBlock(128)
      # Upsampling Layers
      self.deconv1 = UpsampleConvLayer(
          128, 64, kernel_size=3, stride=1, upsample=2
      )
      self.in4 = torch.nn.InstanceNorm2d(64, affine=True)
      self.deconv2 = UpsampleConvLayer(
          64, 32, kernel_size=3, stride=1, upsample=2
      )
      self.in5 = torch.nn.InstanceNorm2d(32, affine=True)
      self.deconv3 = ConvLayer(32, 3, kernel_size=9, stride=1)
      # Non-linearities
      self.relu = torch.nn.ReLU()
  
imports: import argparse
  import os
  import sys
  import re
  import json
  import traceback
  from PIL import Image
  import torch
  from torchvision import transforms
  from azureml.core.model import Model
label_desc: Use image node_id 1 to create a new TransformerNet object from inherited base classes: torch.nn with example: obj = TransformerNet()