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
                  [`~GenerationConfig.save_pretrained`] method, e.g., `./my_model_directory/`.
            config_file_name (`str` or `os.PathLike`, *optional*, defaults to `"generation_config.json"`):
                Name of the generation configuration JSON file to be loaded from `pretrained_model_name`.
            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.
            use_auth_token (`str` or `bool`, *optional*):
                The token to use as HTTP bearer authorization for remote files. If `True`, or not specified, 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.

                <Tip>

                To test a pull request you made on the Hub, you can pass `revision="refs/pr/<pr_number>".

                </Tip>

            return_unused_kwargs (`bool`, *optional*, defaults to `False`):
                If `False`, then this function returns just the final configuration object.

                If `True`, then this functions returns a `Tuple(config, unused_kwargs)` where *unused_kwargs* is a
                dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e., the
                part of `kwargs` which has not been used to update `config` and is otherwise ignored.
            subfolder (`str`, *optional*, defaults to `""`):
                In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can
                specify the folder name here.
            kwargs (`Dict[str, Any]`, *optional*):
                The values in kwargs of any keys which are configuration attributes will be used to override the loaded
                values. Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled
                by the `return_unused_kwargs` keyword parameter.

        Returns:
            [`GenerationConfig`]: The configuration object instantiated from this pretrained model.

        Examples:

        ```python
        >>> from transformers import GenerationConfig

        >>> # Download configuration from huggingface.co and cache.
        >>> generation_config = GenerationConfig.from_pretrained("gpt2")

        >>> # E.g. config was saved using *save_pretrained('./test/saved_model/')*
        >>> generation_config.save_pretrained("./test/saved_model/")
        >>> generation_config = GenerationConfig.from_pretrained("./test/saved_model/")

        >>> # You can also specify configuration names to your generation configuration file
        >>> generation_config.save_pretrained("./test/saved_model/", config_file_name="my_configuration.json")
        >>> generation_config = GenerationConfig.from_pretrained("./test/saved_model/", "my_configuration.json")

        >>> # If you'd like to try a minor variation to an existing configuration, you can also pass generation
        >>> # arguments to `.from_pretrained()`. Be mindful that typos and unused arguments will be ignored
        >>> generation_config, unused_kwargs = GenerationConfig.from_pretrained(
        ...     "gpt2", top_k=1, foo=False, return_unused_kwargs=True
        ... )
        >>> generation_config.top_k
        1

        >>> unused_kwargs
        {'foo': False}
        ```NÚ	cache_dirÚforce_downloadFÚresume_downloadÚ