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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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: Later On: """


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