tensorflow::GradientDef gdef;
  gdef.set_function_name(func->record->fdef().signature().name());
  gdef.set_gradient_func(grad->record->fdef().signature().name());
  status->status = g->graph.AddGradientDef(std::move(gdef));
}

int TF_GraphNumFunctions(TF_Graph* g) {
  tensorflow::mutex_lock l(g->mu);
  return g->graph.flib_def().num_functions();
}

int TF_GraphGetFunctions(TF_Graph* g, TF_Function** funcs, int max_func,

    std::function<void(FunctionDef*)>* mutate_proto_func, TF_Status* status) {
  tensorflow::GraphDef gdef;
  if (!tensorflow::protobuf::TextFormat::ParseFromString(text_proto, &gdef)) {
    status->status = tensorflow::errors::Internal(
        "Invalid text proto for GraphDef: ", text_proto);
    return {};
  }
  const auto& fdef_lib = gdef.library();
  if (fdef_lib.gradient_size() > 0) {
    status->status = tensorflow::errors::Internal(

// Experimental C API for TensorFlow.
//
// The API here is subject to changes in the future.
// --------------------------------------------------------------------------

#ifdef __cplusplus
extern "C" {
#endif

// When `enable` is true, set
// tensorflow.ConfigProto.OptimizerOptions.global_jit_level to ON_1, and also
// set XLA flag values to prepare for XLA compilation. Otherwise set

		switch in.Stack.Text() {
		case "else", "endif", "ifdef", "ifndef", "line":
			// Press on.
		default:
			return false
		}
	}
	switch in.Stack.Text() {
	case "define":
		in.define()
	case "else":
		in.else_()
	case "endif":
		in.endif()
	case "ifdef":
		in.ifdef(true)
	case "ifndef":
		in.ifdef(false)
	case "include":
		in.include()
	case "line":
		in.line()

else:  # pragma: no cover
    from asyncio import iscoroutinefunction


def noop_wrap(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
        return func(*args, **kwargs)

    return wrapper


def noop_wrap_async(func):
    if inspect.isgeneratorfunction(func):

        @wraps(func)
        async def gen_wrapper(*args, **kwargs):

# Without aliases


@app.get("/required-list-str")
async def read_required_list_str(p: Annotated[list[str], Query()]):
    return {"p": p}


class QueryModelRequiredListStr(BaseModel):
    p: list[str]


@app.get("/model-required-list-str")
def read_model_required_list_str(p: Annotated[QueryModelRequiredListStr, Query()]):
    return {"p": p.p}

        return sa or None

    @property
    def required(self) -> bool:
        return self.field_info.is_required()

    @property
    def default(self) -> Any:
        return self.get_default()

    @property
    def type_(self) -> Any:
        return self.field_info.annotation

    def __post_init__(self) -> None:
        with warnings.catch_warnings():

    params=[
        "tutorial001_py39",
        pytest.param("tutorial001_py310", marks=needs_py310),
    ],
)
def get_client(request: pytest.FixtureRequest):
    mod = importlib.import_module(f"docs_src.body.{request.param}")

    client = TestClient(mod.app)
    return client


def test_body_float(client: TestClient):
    response = client.post("/items/", json={"name": "Foo", "price": 50.5})

router = APIRouter()


async def common_parameters(q: str, skip: int = 0, limit: int = 100):
    return {"q": q, "skip": skip, "limit": limit}


@app.get("/main-depends/")
async def main_depends(commons: dict = Depends(common_parameters)):
    return {"in": "main-depends", "params": commons}


@app.get("/decorator-depends/", dependencies=[Depends(common_parameters)])
async def decorator_depends():

@app.post("/optional-list-str", operation_id="optional_list_str")
async def read_optional_list_str(
    p: Annotated[Optional[list[str]], Body(embed=True)] = None,
):
    return {"p": p}


class BodyModelOptionalListStr(BaseModel):
    p: Optional[list[str]] = None


@app.post("/model-optional-list-str", operation_id="model_optional_list_str")
async def read_model_optional_list_str(p: BodyModelOptionalListStr):
    return {"p": p.p}