code: def find_base_model_checkpoint(
      model_type: str,
      model_files: Optional[Dict[str, Union[Path, List[Path]]]] = None,
  ) -> str:
      """
      Finds the model checkpoint used in the docstrings for a given model.
  
      Args:
          model_type (`str`): A valid model type (like "bert" or "gpt2")
          model_files (`Dict[str, Union[Path, List[Path]]`, *optional*):
              The files associated to `model_type`. Can be passed to speed up the function, otherwise will be computed.
  
      Returns:
          `str`: The checkpoint used.
      """
      if model_files is None:
          model_files = get_model_files(model_type)
      module_files = model_files["model_files"]
      for fname in module_files:
          if "modeling" not in str(fname):
              continue
  
          with open(fname, "r", encoding="utf-8") as f:
              content = f.read()
              if _re_checkpoint_for_doc.search(content) is not None:
                  checkpoint = _re_checkpoint_for_doc.search(
                      content
                  ).groups()[0]
                  # Remove quotes
                  checkpoint = checkpoint.replace('"', "")
                  checkpoint = checkpoint.replace("'", "")
                  return checkpoint
  
      # TODO: Find some kind of fallback if there is no _CHECKPOINT_FOR_DOC in any of the modeling file.
      return ""
  
imports: import difflib
  import json
  import os
  import re
  from argparse import ArgumentParser, Namespace
  from dataclasses import dataclass
  from datetime import date
  from itertools import chain
  from pathlib import Path
  from typing import Any, Callable, Dict, List, Optional, Pattern, Tuple, Union
  import yaml
  from ..models import auto
  from ..models.auto.configuration_auto import model_type_to_module_name
  from ..utils import is_flax_available, is_tf_available, is_torch_available, logging
  from .None import BaseTransformersCLICommand
label_desc: Use image node_id 14 for calling a global function with example usage: find_base_model_checkpoint(model_type, model_files) and returns: str, checkpoint