class_docstr: Base class for configuration classes that need to respect the same API than PretrainedConfig but with a different
  configuration file name.
class_docstr_tok: ["Base","class","for","configuration","classes","that","need","to","respect","the","same","API","than","PretrainedConfig","but","with","a","different","configuration","file","name","."]
docstr: From a `pretrained_model_name_or_path`, resolve to a dictionary of parameters, to be used for instantiating a
  [`PretrainedConfig`] using `from_dict`.
  
  Parameters:
      pretrained_model_name_or_path (`str` or `os.PathLike`):
          The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.
  
  Returns:
      `Tuple[Dict, Dict]`: The dictionary(ies) that will be used to instantiate the configuration object.
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code: def get_config_dict(
      cls,
      pretrained_model_name_or_path: Union[str, os.PathLike],
      **kwargs,
  ) -> Tuple[Dict[str, Any], Dict[str, Any]]:
      """
      From a `pretrained_model_name_or_path`, resolve to a dictionary of parameters, to be used for instantiating a
      [`PretrainedConfig`] using `from_dict`.
  
      Parameters:
          pretrained_model_name_or_path (`str` or `os.PathLike`):
              The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.
  
      Returns:
          `Tuple[Dict, Dict]`: The dictionary(ies) that will be used to instantiate the configuration object.
  
      """
      original_kwargs = copy.deepcopy(kwargs)
      # Get config dict associated with the base config file
      config_dict, kwargs = cls._get_config_dict(
          pretrained_model_name_or_path, **kwargs
      )
      if "_commit_hash" in config_dict:
          original_kwargs["_commit_hash"] = config_dict["_commit_hash"]
  
      # That config file may point us toward another config file to use.
      if "configuration_files" in config_dict:
          configuration_file = cls.get_configuration_file(
              config_dict["configuration_files"]
          )
          config_dict, kwargs = cls._get_config_dict(
              pretrained_model_name_or_path,
              _configuration_file=configuration_file,
              **original_kwargs,
          )
  
      return config_dict, kwargs
  
file_path: optimum/optimum/configuration_utils.py
imports: import copy
  import json
  import os
  import re
  from typing import Any, Dict, List, Tuple, Union
  from huggingface_hub import HfFolder
  from packaging import version
  from transformers import PretrainedConfig
  from transformers import __version__
  from .utils import logging
  from .version import __version__
label_desc: Use image node_id 4 for calling the BaseConfig obj's underlying member method code with example usage: obj.get_config_dict(cls, pretrained_model_name_or_path) and returns: config_dict,  kwargs