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
        "IdenticalDataAndAlgorithm" and parents as the union of provided list of ``additional_parents`` and the ``self``

        Args:
            additional_parents (set{str}): Set of additional parents along with the self to be used in warm starting
            the identical dataset and algorithm tuner.

        Returns:
            sagemaker.tuner.HyperparameterTuner: HyperparameterTuner instance which can be used to launch identical
            dataset and algorithm tuning job.

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

def identical_dataset_and_algorithm_tuner(self, additional_parents=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
        "IdenticalDataAndAlgorithm" and parents as the union of provided list of ``additional_parents`` and the ``self``

        Args:
            additional_parents (set{str}): Set of additional parents along with the self to be used in warm starting
            the identical dataset and algorithm tuner.

        Returns:
            sagemaker.tuner.HyperparameterTuner: HyperparameterTuner instance which can be used to launch identical
            dataset and algorithm tuning job.

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

        return self._create_warm_start_tuner(additional_parents=additional_parents,
                                             warm_start_type=WarmStartTypes.IDENTICAL_DATA_AND_ALGORITHM)