code: def __init__(
      self,
      root,
      meta_train=False,
      meta_val=False,
      meta_test=False,
      meta_split=None,
      transform=None,
      class_augmentations=None,
      download=False,
  ):
      super(CIFAR100ClassDataset, self).__init__(
          meta_train=meta_train,
          meta_val=meta_val,
          meta_test=meta_test,
          meta_split=meta_split,
          class_augmentations=class_augmentations,
      )
  
      if self.subfolder is None:
          raise ValueError()
  
      self.root = os.path.join(os.path.expanduser(root), self.folder)
      self.transform = transform
  
      self.split_filename_labels = os.path.join(
          self.root,
          self.subfolder,
          self.filename_labels.format(self.meta_split),
      )
      self._data = None
      self._labels = None
  
      if download:
          self.download()
  
      if not self._check_integrity():
          raise RuntimeError("CIFAR100 integrity check failed")
      self._num_classes = len(self.labels)
  
imports: import numpy
  import os
  import json
  import h5py
  from PIL import Image
  from torchvision.datasets.utils import check_integrity, download_url
  from torchmeta.utils.data import Dataset, ClassDataset
label_desc: Use image node_id 1 to create a new CIFAR100ClassDataset object from inherited base classes: ClassDataset with example: obj = CIFAR100ClassDataset(root, meta_train, meta_val, meta_test, meta_split, transform, class_augmentations, download)