code: def get_dataset(
      dataset_name,
      data_dir,
      split,
      rand_fraction=None,
      clean=False,
      transform=None,
      imsize=None,
      bucket="pytorch-data",
      **kwargs,
  ):
      if dataset_name in ["cifar10", "cifar100"]:
          dataset = globals()[f"get_{dataset_name}"](
              dataset_name,
              data_dir,
              split,
              imsize=imsize,
              bucket=bucket,
              **kwargs,
          )
      elif dataset_name in [
          "cifar10vit224",
          "cifar100vit224",
          "cifar10vit384",
          "cifar100vit384",
      ]:
          imsize = int(dataset_name.split("vit")[-1])
          dataset_name = dataset_name.split("vit")[0]
          # print ('here')
          dataset = globals()["get_cifar_vit"](
              dataset_name,
              data_dir,
              split,
              imsize=imsize,
              bucket=bucket,
              **kwargs,
          )
      else:
          assert "cifar" in dataset_name
      print(dataset_name)
      item = dataset.__getitem__(0)[0]
  
      dataset.nchannels = item.size(0)
      dataset.imsize = item.size(1)
      return dataset
  
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 1 for calling a global function with example usage: get_dataset(dataset_name, data_dir, split, rand_fraction, clean, transform, imsize, bucket) and returns: dataset