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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 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)