code: def _data_transforms_cifar100(args):
      CIFAR_MEAN = [
          0.5070751592371323,
          0.48654887331495095,
          0.4409178433670343,
      ]
      CIFAR_STD = [
          0.2673342858792401,
          0.2564384629170883,
          0.27615047132568404,
      ]
  
      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 8 for calling a global function with example usage: _data_transforms_cifar100(args) and returns: train_transform,  valid_transform