How to use the questionary.form function in questionary

def example_form(inp):
    return form(
        q1=questionary.confirm("Hello?", input=inp, output=DummyOutput()),
        q2=questionary.select(
            "World?", choices=["foo", "bar"], input=inp, output=DummyOutput()
        ),

def _request_export_info() -> Tuple[Text, Text, Text]:
    """Request file path and export stories & nlu data to that path"""

    # export training data and quit
    questions = questionary.form(
        export_stories=questionary.text(
            message="Export stories to (if file exists, this "
                    "will append the stories)",
            default=PATHS["stories"]),
        export_nlu=questionary.text(
            message="Export NLU data to (if file exists, this will "
                    "merge learned data with previous training examples)",
            default=PATHS["nlu"]),
        export_domain=questionary.text(
            message="Export domain file to (if file exists, this "
                    "will be overwritten)",
            default=PATHS["domain"]),
    )

    answers = questions.ask()
    if not answers:

def _request_export_info() -> Tuple[Text, Text, Text]:
    """Request file path and export stories & nlu data to that path"""

    # export training data and quit
    questions = questionary.form(
        export_stories=questionary.text(
            message="Export stories to (if file exists, this "
            "will append the stories)",
            default=PATHS["stories"],
            validate=io_utils.file_type_validator(
                [".md"],
                "Please provide a valid export path for the stories, e.g. 'stories.md'.",
            ),
        ),
        export_nlu=questionary.text(
            message="Export NLU data to (if file exists, this will "
            "merge learned data with previous training examples)",
            default=PATHS["nlu"],
            validate=io_utils.file_type_validator(
                [".md", ".json"],
                "Please provide a valid export path for the NLU data, e.g. 'nlu.md'.",

def configure_channel(channel):
    from rasa_core.utils import print_error, print_success
    import rasa_core.utils

    credentials_file = questionary.text(
        "Please enter a path where to store the credentials file",
        default="credentials.yml").ask()

    if channel == "facebook":
        fb_config = questionary.form(
            verify=questionary.text(
                "Facebook verification string (choosen during "
                "webhook creation)"),
            secret=questionary.text(
                "Facebook application secret"),
            access_token=questionary.text(
                "Facebook access token"),
        ).ask()

        credentials = {
            "verify": fb_config["verify"],
            "secret": fb_config["secret"],
            "page-access-token": fb_config["access_token"]}

        rasa_core.utils.dump_obj_as_yaml_to_file(
            credentials_file,

def _request_export_info() -> Tuple[Text, Text, Text]:
    """Request file path and export stories & nlu data to that path"""

    # export training data and quit
    questions = questionary.form(
        export_stories=questionary.text(
            message="Export stories to (if file exists, this "
            "will append the stories)",
            default=PATHS["stories"],
            validate=io_utils.file_type_validator(
                [".md"],
                "Please provide a valid export path for the stories, e.g. 'stories.md'.",
            ),
        ),
        export_nlu=questionary.text(
            message="Export NLU data to (if file exists, this will "
            "merge learned data with previous training examples)",
            default=PATHS["nlu"],
            validate=io_utils.file_type_validator(
                [".md", ".json"],
                "Please provide a valid export path for the NLU data, e.g. 'nlu.md'.",

def _request_export_info() -> Tuple[Text, Text, Text]:
    """Request file path and export stories & nlu data to that path"""

    # export training data and quit
    questions = questionary.form(
        export_stories=questionary.text(
            message="Export stories to (if file exists, this "
                    "will append the stories)",
            default=PATHS["stories"]),
        export_nlu=questionary.text(
            message="Export NLU data to (if file exists, this will "
                    "merge learned data with previous training examples)",
            default=PATHS["nlu"]),
        export_domain=questionary.text(
            message="Export domain file to (if file exists, this "
                    "will be overwritten)",
            default=PATHS["domain"]),
    )

    answers = questions.ask()
    if not answers: