serialization_alias: Union[str, None] = None,
        title: Optional[str] = None,
        description: Optional[str] = None,
        gt: Optional[float] = None,
        ge: Optional[float] = None,
        lt: Optional[float] = None,
        le: Optional[float] = None,
        min_length: Optional[int] = None,
        max_length: Optional[int] = None,
        pattern: Optional[str] = None,
        regex: Annotated[

def get_path_param_gt(item_id: float = Path(gt=3)):
    return item_id


@app.get("/path/param-gt0/{item_id}")
def get_path_param_gt0(item_id: float = Path(gt=0)):
    return item_id


@app.get("/path/param-ge/{item_id}")
def get_path_param_ge(item_id: float = Path(ge=3)):
    return item_id


@app.get("/path/param-lt/{item_id}")
def get_path_param_lt(item_id: float = Path(lt=3)):
    return item_id

	VFMULVV		X10, V2, V3			// ERROR "expected vector register in vs1 position"
	VFMULVF		X10, V2, V3			// ERROR "expected float register in rs1 position"
	VFDIVVV		X10, V2, V3			// ERROR "expected vector register in vs1 position"
	VFDIVVF		X10, V2, V3			// ERROR "expected float register in rs1 position"
	VFRDIVVF	X10, V2, V3			// ERROR "expected float register in rs1 position"

from fastapi.testclient import TestClient

app = FastAPI()


@app.post("/")
async def get(
    x: Annotated[float, Query(allow_inf_nan=True)] = 0,
    y: Annotated[float, Query(allow_inf_nan=False)] = 0,
    z: Annotated[float, Query()] = 0,
    b: Annotated[float, Body(allow_inf_nan=False)] = 0,
) -> str:
    return "OK"


client = TestClient(app)


@pytest.mark.parametrize(

            """
        ),
    ] = None,
    gt: Annotated[
        Optional[float],
        Doc(
            """
            Greater than. If set, value must be greater than this. Only applicable to
            numbers.
            """
        ),
    ] = None,
    ge: Annotated[
        Optional[float],
        Doc(
            """

TEST(CAPI, TestFromProto) {
  Tensor t_cc(DT_FLOAT, TensorShape({2, 3}));
  t_cc.flat<float>().setConstant(1.0);
  tensorflow::TensorProto t_proto;
  t_cc.AsProtoField(&t_proto);

  TF_Buffer* t_buffer = TF_NewBuffer();
  TF_CHECK_OK(MessageToBuffer(t_proto, t_buffer));

  const int num_bytes = 6 * sizeof(float);
  float* values =
      reinterpret_cast<float*>(tensorflow::cpu_allocator()->AllocateRaw(

from typing import Union

from fastapi import FastAPI
from pydantic import BaseModel


class Item(BaseModel):
    name: str
    description: Union[str, None] = None
    price: float
    tax: Union[float, None] = None


app = FastAPI()


@app.post("/items/")
async def create_item(item: Item):

from typing import Union

from fastapi import Body, FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: Union[str, None] = None
    price: float
    tax: Union[float, None] = None


@app.put("/items/{item_id}")
async def update_item(
    item_id: int,
    item: Item = Body(
        examples=[
            {
                "name": "Foo",

from contextlib import asynccontextmanager

from fastapi import FastAPI


def fake_answer_to_everything_ml_model(x: float):
    return x * 42


ml_models = {}


@asynccontextmanager
async def lifespan(app: FastAPI):
    # Load the ML model
    ml_models["answer_to_everything"] = fake_answer_to_everything_ml_model
    yield
    # Clean up the ML models and release the resources
    ml_models.clear()

app = FastAPI()


class Item(BaseModel):
    name: str = Field(examples=["Foo"])
    description: Union[str, None] = Field(default=None, examples=["A very nice Item"])
    price: float = Field(examples=[35.4])
    tax: Union[float, None] = Field(default=None, examples=[3.2])


@app.put("/items/{item_id}")
async def update_item(item_id: int, item: Item):
    results = {"item_id": item_id, "item": item}