code: def cached_path(
      url_or_filename: Union[str, PathLike],
      cache_dir: Union[str, Path] = None,
      extract_archive: bool = False,
      force_extract: bool = False,
  ) -> str:
      """
      Given something that might be a URL or local path, determine which.
      If it's a remote resource, download the file and cache it, and
      then return the path to the cached file. If it's already a local path,
      make sure the file exists and return the path.
  
      For URLs, "http://", "https://", "s3://", "gs://", and "hf://" are all supported.
      The latter corresponds to the HuggingFace Hub.
  
      For example, to download the PyTorch weights for the model `epwalsh/bert-xsmall-dummy`
      on HuggingFace, you could do:
  
      ```python
      cached_path("hf://epwalsh/bert-xsmall-dummy/pytorch_model.bin")
      ```
  
      For paths or URLs that point to a tarfile or zipfile, you can also add a path
      to a specific file to the `url_or_filename` preceeded by a "!", and the archive will
      be automatically extracted (provided you set `extract_archive` to `True`),
      returning the local path to the specific file. For example:
  
      ```python
      cached_path("model.tar.gz!weights.th", extract_archive=True)
      ```
  
      # Parameters
  
      url_or_filename : `Union[str, Path]`
          A URL or path to parse and possibly download.
  
      cache_dir : `Union[str, Path]`, optional (default = `None`)
          The directory to cache downloads.
  
      extract_archive : `bool`, optional (default = `False`)
          If `True`, then zip or tar.gz archives will be automatically extracted.
          In which case the directory is returned.
  
      force_extract : `bool`, optional (default = `False`)
          If `True` and the file is an archive file, it will be extracted regardless
          of whether or not the extracted directory already exists.
  
          !!! Warning
              Use this flag with caution! This can lead to race conditions if used
              from multiple processes on the same file.
      """
      return str(
          _cached_path.cached_path(
              url_or_filename,
              cache_dir=cache_dir or CACHE_DIRECTORY,
              extract_archive=extract_archive,
              force_extract=force_extract,
          )
      )
  
imports: import bz2
  import gzip
  import lzma
  import weakref
  from contextlib import contextmanager
  import glob
  import io
  import os
  import logging
  import json
  from abc import ABC
  from collections import defaultdict
  from datetime import timedelta
  from fnmatch import fnmatch
  from os import PathLike
  from pathlib import Path
  from typing import Optional, Tuple, Union, Callable, Set, List, Iterator, Iterable, Dict, NamedTuple, MutableMapping
  from weakref import WeakValueDictionary
  import shutil
  import pickle
  import time
  import warnings
  import cached_path
  from cached_path import resource_to_filename, check_tarfile, is_url_or_existing_file, find_latest_cached
  from cached_path.cache_file import CacheFile
  from cached_path.common import PathOrStr
  from cached_path.file_lock import FileLock
  from cached_path.meta import Meta
  import torch
  import numpy
  import lmdb
  from torch import Tensor
label_desc: Use image node_id 2 for calling a global function with example usage: cached_path(url_or_filename, cache_dir, extract_archive, force_extract) and returns: str