docs: 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)