code: def from_pretrained(
      cls,
      pretrained_model_name_or_path: Union[str, os.PathLike],
      cache_dir: Optional[Union[str, os.PathLike]] = None,
      force_download: bool = False,
      local_files_only: bool = False,
      token: Optional[Union[str, bool]] = None,
      revision: str = "main",
      **kwargs,
  ) -> "PretrainedConfig":
      kwargs["cache_dir"] = cache_dir
      kwargs["force_download"] = force_download
      kwargs["local_files_only"] = local_files_only
      kwargs["revision"] = revision
  
      cls._set_token_in_kwargs(kwargs, token)
  
      config_dict, kwargs = cls.get_config_dict(
          pretrained_model_name_or_path, **kwargs
      )
  
      # get the config dict if we are loading from Bark
      if config_dict.get("model_type") == "bark":
          config_dict = config_dict[f"{cls.model_type}_config"]
  
      if (
          "model_type" in config_dict
          and hasattr(cls, "model_type")
          and config_dict["model_type"] != cls.model_type
      ):
          logger.warning(
              f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
              f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
          )
  
      return cls.from_dict(config_dict, **kwargs)
  
file_path: transformers/src/transformers/models/bark/configuration_bark.py
imports: import os
  from typing import Dict, Optional, Union
  from ...configuration_utils import PretrainedConfig
  from ...utils import add_start_docstrings, logging
  from ..auto import CONFIG_MAPPING
label_desc: Use image node_id 2 for calling the BarkSubModelConfig obj's underlying member method code with example usage: obj.from_pretrained(cls, pretrained_model_name_or_path, cache_dir, force_download, local_files_only, token, revision) and returns: cls