code: def __init__(
      self,
      split: Optional[str],
      batchsize_per_replica: int,
      shuffle: bool,
      transform: Optional[Union[ClassyTransform, Callable]],
      num_samples: Optional[int],
      root: str,
      download: bool = None,
  ):
      assert self._CIFAR_TYPE in [
          "cifar10",
          "cifar100",
      ], "CIFARDataset must be subclassed and a valid _CIFAR_TYPE provided"
      if self._CIFAR_TYPE == "cifar10":
          dataset = CIFAR10(
              root=root, train=(split == "train"), download=download
          )
      if self._CIFAR_TYPE == "cifar100":
          dataset = CIFAR100(
              root=root, train=(split == "train"), download=download
          )
  
      super().__init__(
          dataset,
          batchsize_per_replica,
          shuffle,
          transform,
          num_samples,
      )
  
file_path: ClassyVision/classy_vision/dataset/classy_cifar.py
imports: from typing import Any, Callable, Dict, Optional, Union
  from classy_vision.dataset import ClassyDataset, register_dataset
  from classy_vision.dataset.transforms import build_transforms, ClassyTransform
  from torchvision.datasets.cifar import CIFAR10, CIFAR100
label_desc: Use image node_id 1 to create a new CIFARDataset object from inherited base classes: ClassyDataset with example: obj = CIFARDataset(split, batchsize_per_replica, shuffle, transform, num_samples, root, download)