# performance by avoiding unnecessary cloning
    if cloned_types is None:
        cloned_types = _CLONED_TYPES_CACHE

    original_type = field.type_
    if is_dataclass(original_type) and hasattr(original_type, "__pydantic_model__"):
        original_type = original_type.__pydantic_model__
    use_type = original_type
    if lenient_issubclass(original_type, BaseModel):

_API_MODULE = _sys.modules[__name__].bitwise
_tf_api_dir = _os.path.dirname(_os.path.dirname(_API_MODULE.__file__))
_current_module = _sys.modules[__name__]

if not hasattr(_current_module, "__path__"):
  __path__ = [_tf_api_dir]
elif _tf_api_dir not in __path__:
  __path__.append(_tf_api_dir)

# Hook external TensorFlow modules.

# Load tensorflow-io-gcs-filesystem if enabled

_API_MODULE = _sys.modules[__name__].bitwise  # pylint: disable=undefined-variable
_current_module = _sys.modules[__name__]
_tf_api_dir = _os.path.dirname(_os.path.dirname(_API_MODULE.__file__))
if not hasattr(_current_module, "__path__"):
  __path__ = [_tf_api_dir]
elif _tf_api_dir not in __path__:
  __path__.append(_tf_api_dir)

# Hook external TensorFlow modules.

    return (
        _annotation_is_complex(annotation)
        or _annotation_is_complex(origin)
        or hasattr(origin, "__pydantic_core_schema__")
        or hasattr(origin, "__get_pydantic_core_schema__")
    )


def field_annotation_is_scalar(annotation: Any) -> bool:
    # handle Ellipsis here to make tuple[int, ...] work nicely

    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False,
    is_coroutine: bool = True,
) -> Any:
    if field:
        errors = []
        if not hasattr(field, "serialize"):
            # pydantic v1
            response_content = _prepare_response_content(
                response_content,
                exclude_unset=exclude_unset,