code: def normalize_for_resnet(train_x, test_x):
      mean = [0.4914, 0.4822, 0.4465]
      std = [0.2023, 0.1994, 0.2010]
      train_x /= 255
      test_x /= 255
      for ch in range(0, 2):
          train_x[:, ch, :, :] -= mean[ch]
          train_x[:, ch, :, :] /= std[ch]
          test_x[:, ch, :, :] -= mean[ch]
          test_x[:, ch, :, :] /= std[ch]
      return train_x, test_x
  
imports: from singa import singa_wrap
  from singa import autograd
  from singa import tensor
  from singa import device
  from singa import opt
  from PIL import Image
  import numpy
  import os
  import sys
  import time
label_desc: Use image node_id 5 for calling a global function with example usage: normalize_for_resnet(train_x, test_x) and returns: train_x,  test_x