()
return {
            "consider_prior": True,
            "prior_weight": 1.0,
            "consider_magic_clip": True,
            "consider_endpoints": False,
            "n_startup_trials": 20,
            "n_ei_candidates": 24,
            "gamma": hyperopt_default_gamma,
            "weights": default_weights,
        }
Return the the default parameters of hyperopt (v0.1.2).

        :class:`~optuna.samplers.TPESampler` can be instantiated with the parameters returned
        by this method.

        Example:

            Create a :class:`~optuna.samplers.TPESampler` instance with the default
            parameters of `hyperopt <https://github.com/hyperopt/hyperopt/tree/0.1.2>`_.

            .. testcode::

                import optuna
                from optuna.samplers import TPESampler


                def objective(trial):
                    x = trial.suggest_float("x", -10, 10)
                    return x ** 2


                sampler = TPESampler(**TPESampler.hyperopt_parameters())
                study = optuna.create_study(sampler=sampler)
                study.optimize(objective, n_trials=10)

        Returns:
            A dictionary containing the default parameters of hyperopt.
Return the the default parameters of hyperopt (v0.1.2).
Return the the default parameters of hyperopt ( v0 . 1 . 2 ) .
def hyperopt_parameters() -> Dict[str, Any]:
        """Return the the default parameters of hyperopt (v0.1.2).

        :class:`~optuna.samplers.TPESampler` can be instantiated with the parameters returned
        by this method.

        Example:

            Create a :class:`~optuna.samplers.TPESampler` instance with the default
            parameters of `hyperopt <https://github.com/hyperopt/hyperopt/tree/0.1.2>`_.

            .. testcode::

                import optuna
                from optuna.samplers import TPESampler


                def objective(trial):
                    x = trial.suggest_float("x", -10, 10)
                    return x ** 2


                sampler = TPESampler(**TPESampler.hyperopt_parameters())
                study = optuna.create_study(sampler=sampler)
                study.optimize(objective, n_trials=10)

        Returns:
            A dictionary containing the default parameters of hyperopt.

        """

        return {
            "consider_prior": True,
            "prior_weight": 1.0,
            "consider_magic_clip": True,
            "consider_endpoints": False,
            "n_startup_trials": 20,
            "n_ei_candidates": 24,
            "gamma": hyperopt_default_gamma,
            "weights": default_weights,
        }
def hyperopt_parameters ( ) -> Dict [ str , Any ] : return { "consider_prior" : True , "prior_weight" : 1.0 , "consider_magic_clip" : True , "consider_endpoints" : False , "n_startup_trials" : 20 , "n_ei_candidates" : 24 , "gamma" : hyperopt_default_gamma , "weights" : default_weights , }