code: def load_meta_file(
      root: str, file: Optional[str] = None
  ) -> Tuple[Dict[str, str], List[str]]:
      if file is None:
          file = META_FILE
      file = os.path.join(root, file)
  
      if check_integrity(file):
          return torch.load(file, weights_only=True)
      else:
          msg = (
              "The meta file {} is not present in the root directory or is corrupted. "
              "This file is automatically created by the ImageNet dataset."
          )
          raise RuntimeError(msg.format(file, root))
  
file_path: vision/torchvision/datasets/imagenet.py
imports: import os
  import shutil
  import tempfile
  from contextlib import contextmanager
  from typing import Any, Dict, Iterator, List, Optional, Tuple
  import torch
  from .folder import ImageFolder
  from .utils import check_integrity, extract_archive, verify_str_arg
label_desc: Use image node_id 1 for calling a global function with example usage: load_meta_file(root, file) and returns: torch