def hyperparameter_search(
        self,
        hp_space: Optional[Callable[["optuna.Trial"], Dict[str, float]]] = None,
        compute_objective: Optional[Callable[[Dict[str, float]], float]] = None,
        n_trials: int = 20,
        direction: Union[str, List[str]] = "minimize",
        backend: Optional[Union["str", HPSearchBackend]] = None,
        hp_name: Optional[Callable[["optuna.Trial"], str]] = None,
        **kwargs,
    ) -> Union[BestRun, List[BestRun]]:
        """
        Launch an hyperparameter search using `optuna` or `Ray Tune` or `SigOpt`. The optimized quantity is determined
        by `compute_objective`, which defaults to a function returning the evaluation loss when no metric is provided,
        the sum of all metrics otherwise.

        <Tip warning={true}>

        To use this method, you need to have provided a `model_init` when initializing your [`Trainer`]: we need to
        reinitialize the model at each new run. This is incompatible with the `optimizers` argument, so you need to
        subclass [`Trainer`] and override the method [`~Trainer.create_optimizer_and_scheduler`] for custom
        optimizer/scheduler.

        </Tip>

        Args:
            hp_space (`Callable[["optuna.Trial"], Dict[str, float]]`, *optional*):
                A function that defines the hyperparameter search space. Will default to
                [`~trainer_utils.default_hp_space_optuna`] or [`~trainer_utils.default_hp_space_ray`] or
                [`~trainer_utils.default_hp_space_sigopt`] depending on your backend.
            compute_objective (`Callable[[Dict[str, float]], float]`, *optional*):
                A function computing the objective to minimize or maximize from the metrics returned by the `evaluate`
                method. Will default to [`~trainer_utils.default_compute_objective`].
            n_trials (`int`, *optional*, defaults to 100):
                The number of trial runs to test.
            direction (`str` or `List[str]`, *optional*, defaults to `"minimize"`):
                If it's single objective optimization, direction is `str`, can be `"minimize"` or `"maximize"`, you
                should pick `"minimize"` when optimizing the validation loss, `"maximize"` when optimizing one or
                several metrics. If it's multi objectives optimization, direction is `List[str]`, can be List of
                `"minimize"` and `"maximize"`, you should pick `"minimize"` when optimizing the validation loss,
                `"maximize"` when optimizing one or several metrics.
            backend (`str` or [`~training_utils.HPSearchBackend`], *optional*):
                The backend to use for hyperparameter search. Will default to optuna or Ray Tune or SigOpt, depending
                on which one is installed. If all are installed, will default to optuna.
            hp_name (`Callable[["optuna.Trial"], str]]`, *optional*):
                A function that defines the trial/run name. Will default to None.
            kwargs (`Dict[str, Any]`, *optional*):
                Additional keyword arguments passed along to `optuna.create_study` or `ray.tune.run`. For more
                information see:

                - the documentation of
                  [optuna.create_study](https://optuna.readthedocs.io/en/stable/reference/generated/optuna.study.create_study.html)
                - the documentation of [tune.run](https://docs.ray.io/en/latest/tune/api_docs/execution.html#tune-run)
                - the documentation of [sigopt](https://app.sigopt.com/docs/endpoints/experiments/create)

        Returns:
            [`trainer_utils.BestRun` or `List[trainer_utils.BestRun]`]: All the information about the best run or best
            runs for multi-objective optimization. Experiment summary can be found in `run_summary` attribute for Ray
            backend.
        """
        if backend is None:
            backend = default_hp_search_backend()
        backend = HPSearchBackend(backend)
        backend_obj = ALL_HYPERPARAMETER_SEARCH_BACKENDS[backend]()
        backend_obj.ensure_available()
        self.hp_search_backend = backend
        if self.model_init is None:
            raise RuntimeError(
                "To use hyperparameter search, you need to pass your model through a model_init function."
            )

        self.hp_space = backend_obj.default_hp_space if hp_space is None else hp_space
        self.hp_name = hp_name
        self.compute_objective = default_compute_objective if compute_objective is None else compute_objective

        best_run = backend_obj.run(self, n_trials, direction, **kwargs)

        self.hp_search_backend = None
        return best_run

        Launch an hyperparameter search using `optuna` or `Ray Tune` or `SigOpt`. The optimized quantity is determined
        by `compute_objective`, which defaults to a function returning the evaluation loss when no metric is provided,
        the sum of all metrics otherwise.

        <Tip warning={true}>

        To use this method, you need to have provided a `model_init` when initializing your [`Trainer`]: we need to
        reinitialize the model at each new run. This is incompatible with the `optimizers` argument, so you need to
        subclass [`Trainer`] and override the method [`~Trainer.create_optimizer_and_scheduler`] for custom
        optimizer/scheduler.

        </Tip>

        Args:
            hp_space (`Callable[["optuna.Trial"], Dict[str, float]]`, *optional*):
                A function that defines the hyperparameter search space. Will default to
                [`~trainer_utils.default_hp_space_optuna`] or [`~trainer_utils.default_hp_space_ray`] or
                [`~trainer_utils.default_hp_space_sigopt`] depending on your backend.
            compute_objective (`Callable[[Dict[str, float]], float]`, *optional*):
                A function computing the objective to minimize or maximize from the metrics returned by the `evaluate`
                method. Will default to [`~trainer_utils.default_compute_objective`].
            n_trials (`int`, *optional*, defaults to 100):
                The number of trial runs to test.
            direction (`str` or `List[str]`, *optional*, defaults to `"minimize"`):
                If it's single objective optimization, direction is `str`, can be `"minimize"` or `"maximize"`, you
                should pick `"minimize"` when optimizing the validation loss, `"maximize"` when optimizing one or
                several metrics. If it's multi objectives optimization, direction is `List[str]`, can be List of
                `"minimize"` and `"maximize"`, you should pick `"minimize"` when optimizing the validation loss,
                `"maximize"` when optimizing one or several metrics.
            backend (`str` or [`~training_utils.HPSearchBackend`], *optional*):
                The backend to use for hyperparameter search. Will default to optuna or Ray Tune or SigOpt, depending
                on which one is installed. If all are installed, will default to optuna.
            hp_name (`Callable[["optuna.Trial"], str]]`, *optional*):
                A function that defines the trial/run name. Will default to None.
            kwargs (`Dict[str, Any]`, *optional*):
                Additional keyword arguments passed along to `optuna.create_study` or `ray.tune.run`. For more
                information see:

                - the documentation of
                  [optuna.create_study](https://optuna.readthedocs.io/en/stable/reference/generated/optuna.study.create_study.html)
                - the documentation of [tune.run](https://docs.ray.io/en/latest/tune/api_docs/execution.html#tune-run)
                - the documentation of [sigopt](https://app.sigopt.com/docs/endpoints/experiments/create)

        Returns:
            [`trainer_utils.BestRun` or `List[trainer_utils.BestRun]`]: All the information about the best run or best
            runs for multi-objective optimization. Experiment summary can be found in `run_summary` attribute for Ray
            backend.
        
dpkd/transformers/src/transformers/trainer.py