code: def get_cifar100N(
      dataset_name,
      data_dir,
      split,
      rand_fraction=None,
      transform=None,
      imsize=None,
      bucket="pytorch-data",
      **kwargs,
  ):
      transform = get_transform(
          split, transform=transform, imsize=imsize, aug="small"
      )
      if split == "train":
          return CIFAR100N(
              root=data_dir,
              train=(split == "train"),
              transform=transform,
              download=True,
              rand_fraction=rand_fraction,
          )
      else:
          return datasets.CIFAR100(
              data_dir,
              train=(split == "train"),
              transform=transform,
              download=True,
              **kwargs,
          )
  
file_path: deepspeedexamples/training/data_efficiency/vit_finetuning/utils/get_data.py
imports: import torch
  import os
  import torchvision.transforms
  import torchvision.datasets
label_desc: Use image node_id 6 for calling a global function with example usage: get_cifar100N(dataset_name, data_dir, split, rand_fraction, transform, imsize, bucket) and returns: CIFAR100N, datasets