code: def get_augment_datasets(config):
      dataset = config.dataset.lower()
      if dataset == "cifar10":
          dset_cls = dset.CIFAR10
      elif dataset == "cifar100":
          dset_cls = dset.CIFAR100
      else:
          raise Exception("Not support dataset!")
  
      train_transform, valid_transform = data_transforms_cifar(
          config, cutout=True
      )
      train_data = dset_cls(
          root=config.data_dir,
          train=True,
          download=True,
          transform=train_transform,
      )
      test_data = dset_cls(
          root=config.data_dir,
          train=False,
          download=True,
          transform=valid_transform,
      )
  
      if config.distributed:
          train_sampler = (
              torch.utils.data.distributed.DistributedSampler(
                  train_data
              )
          )
          test_sampler = (
              torch.utils.data.distributed.DistributedSampler(test_data)
          )
      else:
          train_sampler = None
          test_sampler = None
  
      train_loader = torch.utils.data.DataLoader(
          train_data,
          batch_size=config.batch_size,
          sampler=train_sampler,
          pin_memory=True,
          num_workers=config.workers,
      )
  
      test_loader = torch.utils.data.DataLoader(
          test_data,
          batch_size=config.eval_batch_size,
          sampler=test_sampler,
          pin_memory=True,
          num_workers=config.workers,
      )
  
      return [train_loader, test_loader], [train_sampler, test_sampler]
  
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 3 for calling a global function with example usage: get_augment_datasets(config) and returns: list,  list