ax/service/ax_client.py [1254:1295]:
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    @copy_doc(BestPointMixin.get_best_trial)
    def get_best_trial(
        self,
        optimization_config: Optional[OptimizationConfig] = None,
        trial_indices: Optional[Iterable[int]] = None,
        use_model_predictions: bool = True,
    ) -> Optional[Tuple[int, TParameterization, Optional[TModelPredictArm]]]:
        return self._get_best_trial(
            experiment=self.experiment,
            generation_strategy=self.generation_strategy,
            trial_indices=trial_indices,
            use_model_predictions=use_model_predictions,
        )

    @copy_doc(BestPointMixin.get_pareto_optimal_parameters)
    def get_pareto_optimal_parameters(
        self,
        optimization_config: Optional[OptimizationConfig] = None,
        trial_indices: Optional[Iterable[int]] = None,
        use_model_predictions: bool = True,
    ) -> Optional[Dict[int, Tuple[TParameterization, TModelPredictArm]]]:
        return self._get_pareto_optimal_parameters(
            experiment=self.experiment,
            generation_strategy=self.generation_strategy,
            trial_indices=trial_indices,
            use_model_predictions=use_model_predictions,
        )

    @copy_doc(BestPointMixin.get_hypervolume)
    def get_hypervolume(
        self,
        optimization_config: Optional[MultiObjectiveOptimizationConfig] = None,
        trial_indices: Optional[Iterable[int]] = None,
        use_model_predictions: bool = True,
    ) -> float:
        return BestPointMixin._get_hypervolume(
            experiment=self.experiment,
            generation_strategy=self.generation_strategy,
            optimization_config=optimization_config,
            trial_indices=trial_indices,
            use_model_predictions=use_model_predictions,
        )
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ax/service/scheduler.py [513:554]:
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    @copy_doc(BestPointMixin.get_best_trial)
    def get_best_trial(
        self,
        optimization_config: Optional[OptimizationConfig] = None,
        trial_indices: Optional[Iterable[int]] = None,
        use_model_predictions: bool = True,
    ) -> Optional[Tuple[int, TParameterization, Optional[TModelPredictArm]]]:
        return self._get_best_trial(
            experiment=self.experiment,
            generation_strategy=self.generation_strategy,
            trial_indices=trial_indices,
            use_model_predictions=use_model_predictions,
        )

    @copy_doc(BestPointMixin.get_pareto_optimal_parameters)
    def get_pareto_optimal_parameters(
        self,
        optimization_config: Optional[OptimizationConfig] = None,
        trial_indices: Optional[Iterable[int]] = None,
        use_model_predictions: bool = True,
    ) -> Optional[Dict[int, Tuple[TParameterization, TModelPredictArm]]]:
        return self._get_pareto_optimal_parameters(
            experiment=self.experiment,
            generation_strategy=self.generation_strategy,
            trial_indices=trial_indices,
            use_model_predictions=use_model_predictions,
        )

    @copy_doc(BestPointMixin.get_hypervolume)
    def get_hypervolume(
        self,
        optimization_config: Optional[MultiObjectiveOptimizationConfig] = None,
        trial_indices: Optional[Iterable[int]] = None,
        use_model_predictions: bool = True,
    ) -> float:
        return BestPointMixin._get_hypervolume(
            experiment=self.experiment,
            generation_strategy=self.generation_strategy,
            optimization_config=optimization_config,
            trial_indices=trial_indices,
            use_model_predictions=use_model_predictions,
        )
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