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 ``self``.
        Also, training image in the new tuner's estimator is updated with the provided ``training_image``.

        Args:
            additional_parents (set{str}): Set of additional parents along with the self to be used in warm starting
            the transfer learning 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.

        Returns:
            sagemaker.tuner.HyperparameterTuner: ``HyperparameterTuner`` instance which can be used to launch transfer
            learning tuning job.

        Examples:
            >>> parent_tuner = HyperparameterTuner.attach(tuning_job_name="parent-job-1")
            >>> transfer_learning_tuner = parent_tuner.transfer_learning_tuner(additional_parents={"parent-job-2"})
            Later On:
            >>> transfer_learning_tuner.fit(inputs={})

def transfer_learning_tuner(self, additional_parents=None, estimator=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 ``self``.
        Also, training image in the new tuner's estimator is updated with the provided ``training_image``.

        Args:
            additional_parents (set{str}): Set of additional parents along with the self to be used in warm starting
            the transfer learning 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.

        Returns:
            sagemaker.tuner.HyperparameterTuner: ``HyperparameterTuner`` instance which can be used to launch transfer
            learning tuning job.

        Examples:
            >>> parent_tuner = HyperparameterTuner.attach(tuning_job_name="parent-job-1")
            >>> transfer_learning_tuner = parent_tuner.transfer_learning_tuner(additional_parents={"parent-job-2"})
            Later On:
            >>> transfer_learning_tuner.fit(inputs={})
        """

        return self._create_warm_start_tuner(additional_parents=additional_parents,
                                             warm_start_type=WarmStartTypes.TRANSFER_LEARNING,
                                             estimator=estimator)