code: def _data_transforms_cifar10(args):
      CIFAR_MEAN = [0.49139968, 0.48215827, 0.44653124]
      CIFAR_STD = [0.24703233, 0.24348505, 0.26158768]
  
      train_transform = transforms.Compose(
          [
              transforms.RandomCrop(32, padding=4),
              transforms.RandomHorizontalFlip(),
              transforms.ToTensor(),
              transforms.Normalize(CIFAR_MEAN, CIFAR_STD),
          ]
      )
      if args.cutout:
          train_transform.transforms.append(Cutout(args.cutout_length))
  
      valid_transform = transforms.Compose(
          [
              transforms.ToTensor(),
              transforms.Normalize(CIFAR_MEAN, CIFAR_STD),
          ]
      )
      return train_transform, valid_transform
  
imports: import math
  import os
  import random
  import sys
  import time
  import warnings
  import numpy
  import numpy
  import shutil
  import logging
  import torch
  import torchvision.transforms
  import torchvision.datasets
  import torch.nn
label_desc: Use image node_id 4 for calling a global function with example usage: _data_transforms_cifar10(args) and returns: train_transform,  valid_transform