code: def get_train_eval_loaders(path, batch_size=256):
      """Setup the dataflow:
          - load CIFAR100 train and test datasets
          - setup train/test image transforms
              - horizontally flipped randomly and augmented using cutout.
              - each mini-batch contained 256 examples
          - setup train/test data loaders
  
      Returns:
          train_loader, test_loader, eval_train_loader
      """
      train_transform = Compose(
          [
              Pad(4),
              RandomCrop(32),
              RandomHorizontalFlip(),
              ToTensor(),
              Normalize(
                  mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
              ),
              RandomErasing(),
          ]
      )
  
      test_transform = Compose(
          [
              ToTensor(),
              Normalize(
                  mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
              ),
          ]
      )
  
      train_dataset = CIFAR100(
          root=path,
          train=True,
          transform=train_transform,
          download=True,
      )
      test_dataset = CIFAR100(
          root=path,
          train=False,
          transform=test_transform,
          download=False,
      )
  
      train_eval_indices = [
          random.randint(0, len(train_dataset) - 1)
          for i in range(len(test_dataset))
      ]
      train_eval_dataset = Subset(train_dataset, train_eval_indices)
  
      train_loader = DataLoader(
          train_dataset,
          batch_size=batch_size,
          num_workers=12,
          shuffle=True,
          drop_last=True,
          pin_memory=True,
      )
  
      test_loader = DataLoader(
          test_dataset,
          batch_size=batch_size,
          num_workers=12,
          shuffle=False,
          drop_last=False,
          pin_memory=True,
      )
  
      eval_train_loader = DataLoader(
          train_eval_dataset,
          batch_size=batch_size,
          num_workers=12,
          shuffle=False,
          drop_last=False,
          pin_memory=True,
      )
  
      return train_loader, test_loader, eval_train_loader
  
imports: import random
  from torch.utils.data import DataLoader, Subset
  from torchvision.datasets.cifar import CIFAR100
  from torchvision.transforms import Compose, Normalize, Pad, RandomCrop, RandomErasing, RandomHorizontalFlip, ToTensor
label_desc: Use image node_id 1 for calling a global function with example usage: get_train_eval_loaders(path, batch_size) and returns: train_loader,  test_loader,  eval_train_loader