Creates a new tuner by copying the request fields from the provided parent to the new instance of
        ``HyperparameterTuner`` followed by addition of warm start configuration with the type as
        "IdenticalDataAndAlgorithm" and ``parents`` as the union of provided list of ``additional_parents`` and the
        ``parent``.

    Args:
        parent (str): Primary parent tuning job's name from which the Tuner and Estimator configuration has to be copied
        additional_parents (set{str}): Set of additional parent tuning job's names along with the primary parent tuning
            job name to be used in warm starting the transfer learning tuner.
        sagemaker_session (sagemaker.session.Session): Session object which manages interactions with
                Amazon SageMaker APIs and any other AWS services needed. If not specified, one is created
                using the default AWS configuration chain.

    Returns:
        sagemaker.tuner.HyperparameterTuner: a new ``HyperparameterTuner`` object for the warm-started
        hyperparameter tuning job

def create_identical_dataset_and_algorithm_tuner(parent, additional_parents=None, sagemaker_session=None):
    """Creates a new tuner by copying the request fields from the provided parent to the new instance of
        ``HyperparameterTuner`` followed by addition of warm start configuration with the type as
        "IdenticalDataAndAlgorithm" and ``parents`` as the union of provided list of ``additional_parents`` and the
        ``parent``.

    Args:
        parent (str): Primary parent tuning job's name from which the Tuner and Estimator configuration has to be copied
        additional_parents (set{str}): Set of additional parent tuning job's names along with the primary parent tuning
            job name to be used in warm starting the transfer learning tuner.
        sagemaker_session (sagemaker.session.Session): Session object which manages interactions with
                Amazon SageMaker APIs and any other AWS services needed. If not specified, one is created
                using the default AWS configuration chain.

    Returns:
        sagemaker.tuner.HyperparameterTuner: a new ``HyperparameterTuner`` object for the warm-started
        hyperparameter tuning job
    """

    parent_tuner = HyperparameterTuner.attach(tuning_job_name=parent, sagemaker_session=sagemaker_session)
    return parent_tuner.identical_dataset_and_algorithm_tuner(additional_parents=additional_parents)