Creates a new ``HyperParameterTuner`` 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 "TransferLearning"
        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 identical dataset and algorithm tuner.
        estimator (sagemaker.estimator.EstimatorBase): An estimator object that has been initialized with
                the desired configuration. There does not need to be a training job associated with this instance.
        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: New instance of warm started HyperparameterTuner

def create_transfer_learning_tuner(parent, additional_parents=None, estimator=None, sagemaker_session=None):
    """Creates a new ``HyperParameterTuner`` 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 "TransferLearning"
        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 identical dataset and algorithm tuner.
        estimator (sagemaker.estimator.EstimatorBase): An estimator object that has been initialized with
                the desired configuration. There does not need to be a training job associated with this instance.
        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: New instance of warm started HyperparameterTuner
    """

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