tensorflow::FunctionDef fdef;
    ASSERT_TRUE(GetFunctionDef(func_, &fdef));
    VerifyFDefNodes(fdef, nodes);
    VerifyFDefInputs(fdef, inputs);
    VerifyFDefOutputs(fdef, outputs);
    VerifyFDefEdges(fdef, e_edges, c_edges, is_exact_edges);
  }

  // Serialize func_ to fdef and import it back
  void Reincarnate() {
    // func_ -> fdef
    tensorflow::FunctionDef fdef;

    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(

        //        searchName = Srl.substringFirstRear(classificationName, ".");
        //    } else {
        //        return null;
        //    }
        //}
        //try {
        //    return CDef.DefMeta.valueOf(searchName);
        //} catch (IllegalArgumentException ignored) { // not found
        //    return null; // handled later
        //}
    }

    @Override

		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):

from fastapi.testclient import TestClient

app = FastAPI()


def function_dependency(value: str) -> str:
    return value


async def async_function_dependency(value: str) -> str:
    return value


def gen_dependency(value: str) -> Generator[str, None, None]:
    yield value


async def async_gen_dependency(value: str) -> AsyncGenerator[str, None]:
    yield value

# Without aliases


@app.get("/optional-str")
async def read_optional_str(p: Annotated[Optional[str], Cookie()] = None):
    return {"p": p}


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


@app.get("/model-optional-str")
async def read_model_optional_str(p: Annotated[CookieModelOptionalStr, Cookie()]):
    return {"p": p.p}

# Without aliases


@app.get("/optional-list-str")
async def read_optional_list_str(
    p: Annotated[Optional[list[str]], Header()] = None,
):
    return {"p": p}


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


@app.get("/model-optional-list-str")
async def read_model_optional_list_str(
    p: Annotated[HeaderModelOptionalListStr, Header()],
):

# 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 nullptr;
}

void TF_GraphToGraphDef(TF_Graph* graph, TF_Buffer* output_graph_def,
                        TF_Status* status) {
  GraphDef def;
  {
    mutex_lock l(graph->mu);
    graph->graph.ToGraphDef(&def);
  }
  status->status = MessageToBuffer(def, output_graph_def);
}

void TF_GraphGetOpDef(TF_Graph* graph, const char* op_name,
                      TF_Buffer* output_op_def, TF_Status* status) {