code: def load(
      self,
      root: Union[str, pathlib.Path],
      *,
      skip_integrity_check: bool = False,
  ) -> IterDataPipe[Tuple[str, IO]]:
      root = pathlib.Path(root)
      path = root / self.file_name
  
      # Instead of the raw file, there might also be files with fewer suffixes after decompression or directories
      # with no suffixes at all. `pathlib.Path().stem` will only give us the name with the last suffix removed, which
      # is not sufficient for files with multiple suffixes, e.g. foo.tar.gz.
      stem = path.name.replace("".join(path.suffixes), "")
  
      def find_candidates() -> Set[pathlib.Path]:
          # Although it looks like we could glob for f"{stem}*" to find the file candidates as well as the folder
          # candidate simultaneously, that would also pick up other files that share the same prefix. For example, the
          # test split of the stanford-cars dataset uses the files
          # - cars_test.tgz
          # - cars_test_annos_withlabels.mat
          # Globbing for `"cars_test*"` picks up both.
          candidates = {file for file in path.parent.glob(f"{stem}.*")}
          folder_candidate = path.parent / stem
          if folder_candidate.exists():
              candidates.add(folder_candidate)
  
          return candidates
  
      candidates = find_candidates()
  
      if not candidates:
          self.download(root, skip_integrity_check=skip_integrity_check)
          if self._preprocess is not None:
              self._preprocess(path)
          candidates = find_candidates()
  
      # We use the path with the fewest suffixes. This gives us the
      # extracted > decompressed > raw
      # priority that we want for the best I/O performance.
      return self._loader(
          min(candidates, key=lambda candidate: len(candidate.suffixes))
      )
  
imports: import abc
  import hashlib
  import itertools
  import pathlib
  from typing import Any, Callable, IO, Literal, NoReturn, Optional, Sequence, Set, Tuple, Union
  from urllib.parse import urlparse
  from torchdata.datapipes.iter import FileLister, FileOpener, IterableWrapper, IterDataPipe, RarArchiveLoader, TarArchiveLoader, ZipArchiveLoader
  from torchvision.datasets.utils import _decompress, _detect_file_type, _get_google_drive_file_id, _get_redirect_url, download_file_from_google_drive, download_url, extract_archive
label_desc: Use image node_id 6 for calling the OnlineResource obj's underlying member method code with example usage: obj.load(root) and returns: self, candidates