def METHOD_NAME(self, tp_type: TrainingPlans):
    """Loads proper DataManager based on given TrainingPlan and
    `dataset`, `target` attributes.
    Args:
        tp_type: Enumeration instance of TrainingPlans that stands for type of training plan.
    Raises:
        FedbiomedDataManagerError: If requested DataManager does not match with given arguments.
    """
    # Training plan is type of TorcTrainingPlan
    if tp_type == TrainingPlans.TorchTrainingPlan:
        if self._target is None and isinstance(self._dataset, Dataset):
            # Create Dataset for pytorch
            self._data_manager_instance = TorchDataManager(dataset=self._dataset, **self._loader_arguments)
        elif isinstance(self._dataset, (pd.DataFrame, pd.Series, np.ndarray)) and \
                isinstance(self._target, (pd.DataFrame, pd.Series, np.ndarray)):
            # If `dataset` and `target` attributes are array-like object
            # create TabularDataset object to instantiate a TorchDataManager
            torch_dataset = TabularDataset(inputs=self._dataset, target=self._target)
            self._data_manager_instance = TorchDataManager(dataset=torch_dataset, **self._loader_arguments)
        else:
            raise FedbiomedDataManagerError(f"{ErrorNumbers.FB607.value}: Invalid arguments for torch based "
                                            f"training plan, either provide the argument  `dataset` as PyTorch "
                                            f"Dataset instance, or provide `dataset` and `target` arguments as "
                                            f"an instance one of pd.DataFrame, pd.Series or np.ndarray ")
    elif tp_type == TrainingPlans.SkLearnTrainingPlan:
        # Try to convert `torch.utils.Data.Dataset` to SkLearnBased dataset/datamanager
        if self._target is None and isinstance(self._dataset, Dataset):
            torch_data_manager = TorchDataManager(dataset=self._dataset)
            try:
                self._data_manager_instance = torch_data_manager.to_sklearn()
            except Exception as e:
                raise FedbiomedDataManagerError(f"{ErrorNumbers.FB607.value}: PyTorch based `Dataset` object "
                                                "has been instantiated with DataManager. An error occurred while"
                                                "trying to convert torch.utils.data.Dataset to numpy based "
                                                f"dataset: {str(e)}")
        # For scikit-learn based training plans, the arguments `dataset` and `target` should be an instance
        # one of `pd.DataFrame`, `pd.Series`, `np.ndarray`
        elif isinstance(self._dataset, (pd.DataFrame, pd.Series, np.ndarray)) and \
                isinstance(self._target, (pd.DataFrame, pd.Series, np.ndarray)):
            # Create Dataset for SkLearn training plans
            self._data_manager_instance = SkLearnDataManager(inputs=self._dataset, target=self._target,
                                                             **self._loader_arguments)
        else:
            raise FedbiomedDataManagerError(f"{ErrorNumbers.FB607.value}: The argument `dataset` and `target` "
                                            f"should be instance of pd.DataFrame, pd.Series or np.ndarray ")
    else:
        raise FedbiomedDataManagerError(f"{ErrorNumbers.FB607.value}: Undefined training plan")