code: def data_transforms_cifar(config, cutout=False):
      CIFAR_MEAN = [0.49139968, 0.48215827, 0.44653124]
      CIFAR_STD = [0.24703233, 0.24348505, 0.26158768]
  
      if config.use_aa:
          train_transform = transforms.Compose(
              [
                  transforms.RandomCrop(32, padding=4, fill=128),
                  transforms.RandomHorizontalFlip(),
                  CIFAR10Policy(),
                  transforms.ToTensor(),
                  transforms.Normalize(CIFAR_MEAN, CIFAR_STD),
              ]
          )
      else:
          train_transform = transforms.Compose(
              [
                  transforms.RandomCrop(32, padding=4),
                  transforms.RandomHorizontalFlip(),
                  transforms.ToTensor(),
                  transforms.Normalize(CIFAR_MEAN, CIFAR_STD),
              ]
          )
  
      if cutout:
          train_transform.transforms.append(
              Cutout(config.cutout_length)
          )
  
      valid_transform = transforms.Compose(
          [
              transforms.ToTensor(),
              transforms.Normalize(CIFAR_MEAN, CIFAR_STD),
          ]
      )
      return train_transform, valid_transform
  
file_path: nni/examples/nas/legacy/cdarts/datasets/cifar.py
imports: import numpy
  import torch
  import torchvision.datasets
  import torchvision.transforms
  from datasets.data_utils import CIFAR10Policy, Cutout
  from datasets.data_utils import SubsetDistributedSampler
label_desc: Use image node_id 1 for calling a global function with example usage: data_transforms_cifar(config, cutout) and returns: train_transform,  valid_transform