Args:
            search_space: see config_generator() documentation
            valid_data: a tuple of Tensors (X,Y), a Dataset, or a DataLoader of
                X (data) and Y (labels) for the dev split
            init_args: (list) positional args for initializing the model
            train_args: (list) positional args for training the model
            init_kwargs: (dict) keyword args for initializing the model
            train_kwargs: (dict) keyword args for training the model
            module_args: (dict) Dictionary of lists of module args
            module_kwargs: (dict) Dictionary of dictionaries of module kwargs
            max_search: see config_generator() documentation
            shuffle: see config_generator() documentation
        Returns:
            best_model: the highest performing trained model

        Note: Initialization is performed by ModelTuner instead of passing a
        pre-initialized model so that tuning may be performed over all model
        parameters, including the network architecture (which is defined before
        the train loop).

def search(
        self,
        search_space,
        valid_data,
        init_args=[],
        train_args=[],
        init_kwargs={},
        train_kwargs={},
        module_args={},
        module_kwargs={},
        max_search=None,
        shuffle=True,
        verbose=True,
        **score_kwargs,
    ):
        """
        Args:
            search_space: see config_generator() documentation
            valid_data: a tuple of Tensors (X,Y), a Dataset, or a DataLoader of
                X (data) and Y (labels) for the dev split
            init_args: (list) positional args for initializing the model
            train_args: (list) positional args for training the model
            init_kwargs: (dict) keyword args for initializing the model
            train_kwargs: (dict) keyword args for training the model
            module_args: (dict) Dictionary of lists of module args
            module_kwargs: (dict) Dictionary of dictionaries of module kwargs
            max_search: see config_generator() documentation
            shuffle: see config_generator() documentation
        Returns:
            best_model: the highest performing trained model

        Note: Initialization is performed by ModelTuner instead of passing a
        pre-initialized model so that tuning may be performed over all model
        parameters, including the network architecture (which is defined before
        the train loop).
        """
        raise NotImplementedError()