flat_dependant.security_requirements.extend(flat_sub.security_requirements)
    return flat_dependant


def get_flat_params(dependant: Dependant) -> List[ModelField]:
    flat_dependant = get_flat_dependant(dependant, skip_repeats=True)
    return (
        flat_dependant.path_params
        + flat_dependant.query_params
        + flat_dependant.header_params
        + flat_dependant.cookie_params

            if route.callbacks:
                callback_flat_models.extend(get_fields_from_routes(route.callbacks))
            params = get_flat_params(route.dependant)
            request_fields_from_routes.extend(params)

    flat_models = callback_flat_models + list(
        body_fields_from_routes + responses_from_routes + request_fields_from_routes
    )
    return flat_models


def get_openapi(
    *,

    default_cc_opt_flags = '-Wno-sign-compare'
  question = ('Please specify optimization flags to use during compilation when'
              ' bazel option "--config=opt" is specified [Default is %s]: '
             ) % default_cc_opt_flags
  cc_opt_flags = get_from_env_or_user_or_default(environ_cp, 'CC_OPT_FLAGS',
                                                 question, default_cc_opt_flags)

# pylint: disable=g-bad-import-order
from tensorflow.python import pywrap_tensorflow  # pylint: disable=unused-import

from tensorflow.python.platform import flags  # pylint: disable=g-import-not-at-top
from tensorflow.python.platform import app  # pylint: disable=g-import-not-at-top
app.flags = flags

# These symbols appear because we import the python package which
# in turn imports from tensorflow.core and tensorflow.python. They

# "contrib".
if "__all__" in vars():
  vars()["__all__"].append("contrib")

from tensorflow.python.platform import flags
# The "app" module will be imported as part of the placeholder section above.
_current_module.app.flags = flags  # pylint: disable=undefined-variable
setattr(_current_module, "flags", flags)

_major_api_version = 1

# Add module aliases from Keras to TF.
# Some tf endpoints actually lives under Keras.

    ) -> Any:
        return {}

    def get_model_definitions(
        *,
        flat_models: Set[Union[Type[BaseModel], Type[Enum]]],
        model_name_map: Dict[Union[Type[BaseModel], Type[Enum]], str],
    ) -> Dict[str, Any]:
        definitions: Dict[str, Dict[str, Any]] = {}
        for model in flat_models:
            m_schema, m_definitions, m_nested_models = model_process_schema(