text: import os
  import os.path
  import numpy as np
  from typing import Any, Callable, Optional, Tuple
  
  from torchvision.datasets.vision import VisionDataset
  from torchvision.datasets.utils import check_integrity, download_and_extract_archive
  
  import hdvw.ops.datasets as datasets
  
  
  class CIFAR10C(VisionDataset):
      """
      `CIFAR10C <https://github.com/hendrycks/robustness>`_ Dataset.
  
      Args:
          root (string): Root directory of dataset.
          ctype (string): Corruption type.
          intensity (int): Corruption intensity from 1 to 5
          transform (callable, optional): A function/transform that takes in an PIL image
              and returns a transformed version. E.g, ``transforms.RandomCrop``
          target_transform (callable, optional): A function/transform that takes in the
              target and transforms it.
          download (bool, optional): If true, downloads the dataset from the internet and
              puts it in root directory. If dataset is already downloaded, it is not
              downloaded again.
      """
      base_folder = "CIFAR-10-C"
      url = "https://zenodo.org/record/2535967/files/CIFAR-10-C.tar"
      root = "./data"
      filename = "cifar-10-c-python.tar"
      tgz_md5 = "56bf5dcef84df0e2308c6dcbcbbd8499"
  
      def __init__(
              self,
              root: str,
              ctype: str,
              intensity: int,
              transform: Optional[Callable] = None,
              target_transform: Optional[Callable] = None,
              download: bool = False,
      ) -> None:
  
          super(CIFAR10C, self).__init__(root,
                                         transform=transform,
                                         target_transform=target_transform)
  
          if download:
              self.download()
  
          if not self._check_integrity():
              raise RuntimeError("Dataset not found or corrupted." +
                                 " You can use download=True to download it")
  
          if ctype not in datasets.get_corruptions(extra=True):
              raise ValueError("Corruption type %s is not provided. " % ctype +
                               "You must choose one of the following types: " +
                               ", ".join(datasets.get_corruptions(extra=True)))
  
          self.data: Any = []
          self.targets = []
  
          # now load data and target
          fpath = os.path.join(self.root, self.base_folder, "%s.npy" % ctype)
          self.data = np.load(fpath)
          fpath = os.path.join(self.root, self.base_folder, "labels.npy")
          self.targets = np.load(fpath)
  
          self.data = self.data[10000*(intensity-1):10000*intensity]
          self.targets = self.targets[10000*(intensity-1):10000*intensity]
          self.targets = self.targets.astype(np.long)
  
      def __getitem__(self, index: int) -> Tuple[Any, Any]:
          """
          Args:
              index (int): Index
          Returns:
              tuple: (image, target) where target is index of the target class.
          """
          img, target = self.data[index], self.targets[index]
  
          if self.transform is not None:
              img = self.transform(img)
  
          if self.target_transform is not None:
              target = self.target_transform(target)
  
          return img, target
  
      def __len__(self) -> int:
          return len(self.data)
  
      def _check_integrity(self) -> bool:
          for cname in datasets.get_corruptions(extra=True):
              fpath = os.path.join(self.root, self.base_folder, "%s.npy" % cname)
              if not check_integrity(fpath):
                  return False
          return True
  
      def download(self) -> None:
          if self._check_integrity():
              print('Files already downloaded and verified')
              return
          download_and_extract_archive(self.url, self.root, filename=self.filename, md5=self.tgz_md5)
  
  
  class CIFAR100C(CIFAR10C):
      """
      `CIFAR100C <https://github.com/hendrycks/robustness>`_ Dataset.
      This is a subclass of the `CIFAR10C` Dataset.
      """
      base_folder = "CIFAR-100-C"
      url = "https://zenodo.org/record/3555552/files/CIFAR-100-C.tar"
      filename = "cifar-100-c-python.tar"
      tgz_md5 = "11f0ed0f1191edbf9fa23466ae6021d3"
  