code: def get_cached_module_file(
      pretrained_model_name_or_path: Union[str, os.PathLike],
      module_file: str,
      cache_dir: Optional[Union[str, os.PathLike]] = None,
      force_download: bool = False,
      resume_download: bool = False,
      proxies: Optional[Dict[str, str]] = None,
      token: Optional[Union[bool, str]] = None,
      revision: Optional[str] = None,
      local_files_only: bool = False,
      repo_type: Optional[str] = None,
      _commit_hash: Optional[str] = None,
      **deprecated_kwargs,
  ) -> str:
      """
      Prepares Downloads a module from a local folder or a distant repo and returns its path inside the cached
      Transformers module.
  
      Args:
          pretrained_model_name_or_path (`str` or `os.PathLike`):
              This can be either:
  
              - a string, the *model id* of a pretrained model configuration hosted inside a model repo on
                huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced
                under a user or organization name, like `dbmdz/bert-base-german-cased`.
              - a path to a *directory* containing a configuration file saved using the
                [`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`.
  
          module_file (`str`):
              The name of the module file containing the class to look for.
          cache_dir (`str` or `os.PathLike`, *optional*):
              Path to a directory in which a downloaded pretrained model configuration should be cached if the standard
              cache should not be used.
          force_download (`bool`, *optional*, defaults to `False`):
              Whether or not to force to (re-)download the configuration files and override the cached versions if they
              exist.
          resume_download (`bool`, *optional*, defaults to `False`):
              Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
          proxies (`Dict[str, str]`, *optional*):
              A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
              'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
          token (`str` or *bool*, *optional*):
              The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
              when running `huggingface-cli login` (stored in `~/.huggingface`).
          revision (`str`, *optional*, defaults to `"main"`):
              The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
              git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
              identifier allowed by git.
          local_files_only (`bool`, *optional*, defaults to `False`):
              If `True`, will only try to load the tokenizer configuration from local files.
          repo_type (`str`, *optional*):
              Specify the repo type (useful when downloading from a space for instance).
  
      <Tip>
  
      Passing `token=True` is required when you want to use a private model.
  
      </Tip>
  
      Returns:
          `str`: The path to the module inside the cache.
      """
      use_auth_token = deprecated_kwargs.pop("use_auth_token", None)
      if use_auth_token is not None:
          warnings.warn(
              "The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.",
              FutureWarning,
          )
          if token is not None:
              raise ValueError(
                  "`token` and `use_auth_token` are both specified. Please set only the argument `token`."
              )
          token = use_auth_token
  
      if is_offline_mode() and not local_files_only:
          logger.info("Offline mode: forcing local_files_only=True")
          local_files_only = True
  
      # Download and cache module_file from the repo `pretrained_model_name_or_path` of grab it if it's a local file.
      pretrained_model_name_or_path = str(pretrained_model_name_or_path)
      is_local = os.path.isdir(pretrained_model_name_or_path)
      if is_local:
          submodule = os.path.basename(pretrained_model_name_or_path)
      else:
          submodule = pretrained_model_name_or_path.replace(
              "/", os.path.sep
          )
          cached_module = try_to_load_from_cache(
              pretrained_model_name_or_path,
              module_file,
              cache_dir=cache_dir,
              revision=_commit_hash,
              repo_type=repo_type,
          )
  
      new_files = []
      try:
          # Load from URL or cache if already cached
          resolved_module_file = cached_file(
              pretrained_model_name_or_path,
              module_file,
              cache_dir=cache_dir,
              force_download=force_download,
              proxies=proxies,
              resume_download=resume_download,
              local_files_only=local_files_only,
              token=token,
              revision=revision,
              repo_type=repo_type,
              _commit_hash=_commit_hash,
          )
          if not is_local and cached_module != resolved_module_file:
              new_files.append(module_file)
  
      except EnvironmentError:
          logger.error(
              f"Could not locate the {module_file} inside {pretrained_model_name_or_path}."
          )
          raise
  
      # Check we have all the requirements in our environment
      modules_needed = check_imports(resolved_module_file)
  
      # Now we move the module inside our cached dynamic modules.
      full_submodule = (
          TRANSFORMERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
      )
      create_dynamic_module(full_submodule)
      submodule_path = Path(HF_MODULES_CACHE) / full_submodule
      if submodule == os.path.basename(pretrained_model_name_or_path):
          # We copy local files to avoid putting too many folders in sys.path. This copy is done when the file is new or
          # has changed since last copy.
          if not (
              submodule_path / module_file
          ).exists() or not filecmp.cmp(
              resolved_module_file, str(submodule_path / module_file)
          ):
              shutil.copy(
                  resolved_module_file, submodule_path / module_file
              )
              importlib.invalidate_caches()
          for module_needed in modules_needed:
              module_needed = f"{module_needed}.py"
              module_needed_file = os.path.join(
                  pretrained_model_name_or_path, module_needed
              )
              if not (
                  submodule_path / module_needed
              ).exists() or not filecmp.cmp(
                  module_needed_file,
                  str(submodule_path / module_needed),
              ):
                  shutil.copy(
                      module_needed_file, submodule_path / module_needed
                  )
                  importlib.invalidate_caches()
      else:
          # Get the commit hash
          commit_hash = extract_commit_hash(
              resolved_module_file, _commit_hash
          )
  
          # The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
          # benefit of versioning.
          submodule_path = submodule_path / commit_hash
          full_submodule = full_submodule + os.path.sep + commit_hash
          create_dynamic_module(full_submodule)
  
          if not (submodule_path / module_file).exists():
              shutil.copy(
                  resolved_module_file, submodule_path / module_file
              )
              importlib.invalidate_caches()
          # Make sure we also have every file with relative
          for module_needed in modules_needed:
              if not (submodule_path / f"{module_needed}.py").exists():
                  get_cached_module_file(
                      pretrained_model_name_or_path,
                      f"{module_needed}.py",
                      cache_dir=cache_dir,
                      force_download=force_download,
                      resume_download=resume_download,
                      proxies=proxies,
                      token=token,
                      revision=revision,
                      local_files_only=local_files_only,
                      _commit_hash=commit_hash,
                  )
                  new_files.append(f"{module_needed}.py")
  
      if len(new_files) > 0 and revision is None:
          new_files = "\n".join([f"- {f}" for f in new_files])
          repo_type_str = "" if repo_type is None else f"{repo_type}s/"
          url = f"https://huggingface.co/{repo_type_str}{pretrained_model_name_or_path}"
          logger.warning(
              f"A new version of the following files was downloaded from {url}:\n{new_files}"
              "\n. Make sure to double-check they do not contain any added malicious code. To avoid downloading new "
              "versions of the code file, you can pin a revision."
          )
  
      return os.path.join(full_submodule, module_file)
  
imports: import filecmp
  import importlib
  import os
  import re
  import shutil
  import signal
  import sys
  import typing
  import warnings
  from pathlib import Path
  from typing import Any, Dict, List, Optional, Union
  from huggingface_hub import try_to_load_from_cache
  from .utils import HF_MODULES_CACHE, TRANSFORMERS_DYNAMIC_MODULE_NAME, cached_file, extract_commit_hash, is_offline_mode, logging
label_desc: Use image node_id 8 for calling a global function with example usage: get_cached_module_file(pretrained_model_name_or_path, module_file, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, repo_type, _commit_hash) and returns: os