How to use the flyteidl.plugins.sagemaker.hpo_job_pb2 function in flyteidl

runtime=_task_models.RuntimeMetadata(
                    type=_task_models.RuntimeMetadata.RuntimeType.FLYTE_SDK,
                    version=__version__,
                    flavor='sagemaker'
                ),
                discoverable=cacheable,
                timeout=timeout,
                retries=_literal_models.RetryStrategy(retries=retries),
                interruptible=interruptible,
                discovery_version=cache_version,
                deprecated_error_message="",
            ),
            interface=_interface.TypedInterface(
                inputs={
                    "hpo_job_config": _interface_model.Variable(
                        _sdk_types.Types.Proto(_hpo_job_pb2.HPOJobConfig).to_flyte_literal_type(), ""
                    ),
                },
                outputs={
                    "model": _interface_model.Variable(
                        type=_idl_types.LiteralType(
                            blob=_core_types.BlobType(
                                format="",
                                dimensionality=_core_types.BlobType.BlobDimensionality.SINGLE
                            )
                        ),
                        description=""
                    )
                }
            ),
            custom=MessageToDict(hpo_job),
        )

def to_flyte_idl(self):

        if self._tuning_strategy == _sdk_sagemaker_types.HyperparameterTuningStrategy.BAYESIAN:
            idl_strategy = _idl_hpo_job.HPOJobConfig.HyperparameterTuningStrategy.BAYESIAN
        elif self._tuning_strategy == _sdk_sagemaker_types.HyperparameterTuningStrategy.RANDOM:
            idl_strategy = _idl_hpo_job.HPOJobConfig.HyperparameterTuningStrategy.RANDOM
        else:
            raise _user_exceptions.FlyteValidationException(
                "Invalid Hyperparameter Tuning Strategy: {}".format(self._tuning_strategy))

        if self._training_job_early_stopping_type == _sdk_sagemaker_types.TrainingJobEarlyStoppingType.OFF:
            idl_training_early_stopping_type = _idl_hpo_job.HPOJobConfig.TrainingJobEarlyStoppingType.OFF
        elif self._training_job_early_stopping_type == _sdk_sagemaker_types.TrainingJobEarlyStoppingType.AUTO:
            idl_training_early_stopping_type = _idl_hpo_job.HPOJobConfig.TrainingJobEarlyStoppingType.AUTO
        else:
            raise _user_exceptions.FlyteValidationException(
                "Invalid Training Job Early Stopping Type (in HPO Config): {}".format(
                    self._training_job_early_stopping_type))

        return _idl_hpo_job.HPOJobConfig(
            hyperparameter_ranges=self._hyperparameter_ranges.to_flyte_idl(),
            tuning_strategy=idl_strategy,
            tuning_objective=self._tuning_objective.to_flyte_idl(),
            training_job_early_stopping_type=idl_training_early_stopping_type,
        )

def to_flyte_idl(self):
        return _idl_hpo_job.HPOJob(
            max_number_of_training_jobs=self._max_number_of_training_jobs,
            max_parallel_training_jobs=self._max_parallel_training_jobs,
            training_job=self._training_job,
        )

def to_flyte_idl(self):

        if self.objective_type == _sdk_sagemaker_types.HyperparameterTuningObjectiveType.MINIMIZE:
            objective_type = _idl_hpo_job.HyperparameterTuningObjective.MINIMIZE
        elif self.objective_type == _sdk_sagemaker_types.HyperparameterTuningObjectiveType.MAXIMIZE:
            objective_type = _idl_hpo_job.HyperparameterTuningObjective.MAXIMIZE
        else:
            raise _user_exceptions.FlyteValidationException(
                "Invalid SageMaker Hyperparameter Tuning Objective Type Specified"
            )

        return _idl_hpo_job.HyperparameterTuningObjective(
            objective_type=objective_type,
            metric_name=self._metric_name,
        )

def to_flyte_idl(self):

        if self.objective_type == _sdk_sagemaker_types.HyperparameterTuningObjectiveType.MINIMIZE:
            objective_type = _idl_hpo_job.HyperparameterTuningObjective.MINIMIZE
        elif self.objective_type == _sdk_sagemaker_types.HyperparameterTuningObjectiveType.MAXIMIZE:
            objective_type = _idl_hpo_job.HyperparameterTuningObjective.MAXIMIZE
        else:
            raise _user_exceptions.FlyteValidationException(
                "Invalid SageMaker Hyperparameter Tuning Objective Type Specified"
            )

        return _idl_hpo_job.HyperparameterTuningObjective(
            objective_type=objective_type,
            metric_name=self._metric_name,
        )