(
        self, params: Dict[str, Any], user_attrs: Optional[Dict[str, Any]] = None
    )
Enqueue a trial with given parameter values.

        You can fix the next sampling parameters which will be evaluated in your
        objective function.

        Example:

            .. testcode::

                import optuna


                def objective(trial):
                    x = trial.suggest_float("x", 0, 10)
                    return x ** 2


                study = optuna.create_study()
                study.enqueue_trial({"x": 5})
                study.enqueue_trial({"x": 0}, user_attrs={"memo": "optimal"})
                study.optimize(objective, n_trials=2)

                assert study.trials[0].params == {"x": 5}
                assert study.trials[1].params == {"x": 0}
                assert study.trials[1].user_attrs == {"memo": "optimal"}

        Args:
            params:
                Parameter values to pass your objective function.
            user_attrs:
                A dictionary of user-specific attributes other than ``params``.

        .. seealso::
            Please refer to :ref:`specify_params` for the tutorial of specifying hyperparameters
            manually.
Enqueue a trial with given parameter values.
Enqueue a trial with given parameter values .
def enqueue_trial(
        self, params: Dict[str, Any], user_attrs: Optional[Dict[str, Any]] = None
    ) -> None:
        """Enqueue a trial with given parameter values.

        You can fix the next sampling parameters which will be evaluated in your
        objective function.

        Example:

            .. testcode::

                import optuna


                def objective(trial):
                    x = trial.suggest_float("x", 0, 10)
                    return x ** 2


                study = optuna.create_study()
                study.enqueue_trial({"x": 5})
                study.enqueue_trial({"x": 0}, user_attrs={"memo": "optimal"})
                study.optimize(objective, n_trials=2)

                assert study.trials[0].params == {"x": 5}
                assert study.trials[1].params == {"x": 0}
                assert study.trials[1].user_attrs == {"memo": "optimal"}

        Args:
            params:
                Parameter values to pass your objective function.
            user_attrs:
                A dictionary of user-specific attributes other than ``params``.

        .. seealso::
            Please refer to :ref:`specify_params` for the tutorial of specifying hyperparameters
            manually.
        """

        self.add_trial(
            create_trial(
                state=TrialState.WAITING,
                system_attrs={"fixed_params": params},
                user_attrs=user_attrs,
            )
        )
def enqueue_trial ( self , params : Dict [ str , Any ] , user_attrs : Optional [ Dict [ str , Any ] ] = None ) -> None : self . add_trial ( create_trial ( state = TrialState . WAITING , system_attrs = { "fixed_params" : params } , user_attrs = user_attrs , ) )