Datasets:

SwashBuckler001
/

idiombench

# Library code (within type stub or inline)
# In this library, passing a resolver means that init must be False,
# and the overload with Literal[False] enforces that.
@overload
def model_field(
        *,
        default: Optional[Any] = ...,
        resolver: Callable[[], Any],
        init: Literal[False] = False,
    ) -> Any: ...

@overload
def model_field(
        *,
        default: Optional[Any] = ...,
        resolver: None = None,
        init: bool = True,
    ) -> Any: ...

@typing.dataclass_transform(
    kw_only_default=True,
    field_specifiers=(model_field, ))
def create_model(
    *,
    init: bool = True,
) -> Callable[[Type[_T]], Type[_T]]: ...

# Code that imports this library:
@create_model(init=False)
class CustomerModel:
    id: int = model_field(resolver=lambda : 0)
    name: str

	# Library code (within type stub or inline)
	# In this library, passing a resolver means that init must be False,
	# and the overload with Literal[False] enforces that.
	@overload
	def model_field(
	*,
	default: Optional[Any] = ...,
	resolver: Callable[[], Any],
	init: Literal[False] = False,
	) -> Any: ...

	@overload
	def model_field(
	*,
	default: Optional[Any] = ...,
	resolver: None = None,
	init: bool = True,
	) -> Any: ...

	@typing.dataclass_transform(
	kw_only_default=True,
	field_specifiers=(model_field, ))
	def create_model(
	*,
	init: bool = True,
	) -> Callable[[Type[_T]], Type[_T]]: ...

	# Code that imports this library:
	@create_model(init=False)
	class CustomerModel:
	id: int = model_field(resolver=lambda : 0)
	name: str