How to use the rasa.cli.utils.get_validated_path function in rasa

def test_validate_valid_path():
    tempdir = tempfile.mkdtemp()

    assert get_validated_path(tempdir, "out", "default") == tempdir

def test_validate_if_default_is_valid():
    tempdir = tempfile.mkdtemp()

    assert get_validated_path(None, "out", tempdir) == tempdir

def split_nlu_data(args: argparse.Namespace) -> None:
    from rasa.nlu.training_data.loading import load_data
    from rasa.nlu.training_data.util import get_file_format

    data_path = rasa.cli.utils.get_validated_path(args.nlu, "nlu", DEFAULT_DATA_PATH)
    data_path = data.get_nlu_directory(data_path)

    nlu_data = load_data(data_path)
    fformat = get_file_format(data_path)

    train, test = nlu_data.train_test_split(args.training_fraction, args.random_seed)

    train.persist(args.out, filename=f"training_data.{fformat}")
    test.persist(args.out, filename=f"test_data.{fformat}")

def split_nlu_data(args):
    from rasa.nlu.training_data.loading import load_data
    from rasa.nlu.training_data.util import get_file_format

    data_path = get_validated_path(args.nlu, "nlu", DEFAULT_DATA_PATH)
    data_path = data.get_nlu_directory(data_path)

    nlu_data = load_data(data_path)
    fformat = get_file_format(data_path)

    train, test = nlu_data.train_test_split(args.training_fraction)

    train.persist(args.out, filename=f"training_data.{fformat}")
    test.persist(args.out, filename=f"test_data.{fformat}")

def _get_credentials_and_endpoints_paths(
    args: argparse.Namespace
) -> Tuple[Optional[Text], Optional[Text]]:
    config_endpoint = args.config_endpoint
    if config_endpoint:
        loop = asyncio.get_event_loop()
        endpoints_config_path, credentials_path = loop.run_until_complete(
            _pull_runtime_config_from_server(config_endpoint)
        )

    else:
        endpoints_config_path = cli_utils.get_validated_path(
            args.endpoints, "endpoints", DEFAULT_ENDPOINTS_PATH, True
        )
        credentials_path = None

    return credentials_path, endpoints_config_path

def train_nlu(
    args: argparse.Namespace, train_path: Optional[Text] = None
) -> Optional[Text]:
    from rasa.train import train_nlu

    output = train_path or args.out

    config = _get_valid_config(args.config, CONFIG_MANDATORY_KEYS_NLU)
    nlu_data = get_validated_path(
        args.nlu, "nlu", DEFAULT_DATA_PATH, none_is_valid=True
    )

    return train_nlu(
        config=config,
        nlu_data=nlu_data,
        output=output,
        train_path=train_path,
        fixed_model_name=args.fixed_model_name,
        persist_nlu_training_data=args.persist_nlu_data,
    )

def get_provided_model(arg_model: Text):
    model_path = get_validated_path(arg_model, "model", DEFAULT_MODELS_PATH)

    if os.path.isdir(model_path):
        model_path = get_latest_model(model_path)

    return model_path

def perform_interactive_learning(args, zipped_model) -> None:
    from rasa.core.train import do_interactive_learning

    if zipped_model and os.path.exists(zipped_model):
        args.model = zipped_model

        with model.unpack_model(zipped_model) as model_path:
            args.core, args.nlu = model.get_model_subdirectories(model_path)
            stories_directory = data.get_core_directory(args.data)

            args.endpoints = get_validated_path(
                args.endpoints, "endpoints", DEFAULT_ENDPOINTS_PATH, True
            )

            do_interactive_learning(args, stories_directory)
    else:
        print_error(
            "Interactive learning process cannot be started as no initial model was "
            "found.  Use 'rasa train' to train a model."

def _prepare_credentials_for_rasa_x(
    credentials_path: Optional[Text], rasa_x_url: Optional[Text] = None
) -> Text:
    credentials_path = cli_utils.get_validated_path(
        credentials_path, "credentials", DEFAULT_CREDENTIALS_PATH, True
    )
    if credentials_path:
        credentials = io_utils.read_config_file(credentials_path)
    else:
        credentials = {}

    # this makes sure the Rasa X is properly configured no matter what
    if rasa_x_url:
        credentials["rasa"] = {"url": rasa_x_url}
    dumped_credentials = yaml.dump(credentials, default_flow_style=False)
    tmp_credentials = io_utils.create_temporary_file(dumped_credentials, "yml")

    return tmp_credentials