from typing_extensions import Annotated

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(

TEST_P(GradientCheckerTest, TestMatMul) {
  float A_vals[] = {1.0f, 2.0f, 3.0f, 4.0f};
  int64_t A_dims[] = {2, 2};
  AbstractTensorHandlePtr A;
  {
    AbstractTensorHandle* A_raw;
    absl::Status s = TestTensorHandleWithDims<float, TF_FLOAT>(
        ctx_.get(), A_vals, A_dims, 2, &A_raw);
    ASSERT_EQ(errors::OK, s.code()) << s.message();
    A.reset(A_raw);
  }
  float B_vals[] = {.5f, -1.0f, 1.0f, 1.0f};

class Item(BaseModel):
    name: str
    description: Union[str, None] = None
    price: float
    tax: Union[float, None] = None
    tags: Set[str] = set()
    images: Union[List[Image], None] = None


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


@app.post("/offers/")
async def create_offer(offer: Offer):

        display: block;
        float: left;
        width: 300px;
        height: 50px;
        padding: 2px;
        font-family: Verdana, sans-serif;
        font-size: 10pt;
        color: #000000;
        text-decoration: none;
    }
    a.plain {
        display: inline;
        float: none;
        width: auto;
        height: auto;
    }
    a.sort {
        display: block;
        float: left;
        width: 100px;

    std::unique_ptr<ParallelTensor> float_tensors =
        parallel_device.ScalarsFromSequence<float>({10.0, 11.0}, context.get(),
                                                   status.get());
    ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
    ExpectScalarEq<float>(float_tensors->tensor(0), 10.0);
    ExpectScalarEq<float>(float_tensors->tensor(1), 11.0);
  }

  {

            final float boost, final String field, final String text) {
        context.addFieldLog(field, text);
        context.addHighlightedQuery(text);
        return buildMatchPhraseQuery(field, text).boost(boost);
    }

    protected QueryBuilder convertKeywordQuery(final FessConfig fessConfig, final QueryContext context, final TermQuery termQuery,
            final float boost, final String field, final String text) {

* Ontworpen op basis van deze standaarden, na zorgvuldig onderzoek. In plaats van achteraf deze laag er bovenop te bouwen.
* Dit maakt het ook mogelijk om automatisch **clientcode te genereren** in verschillende programmeertalen.

### Automatische documentatie

from typing import Union

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


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


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

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float | None = None
    tags: list[str] = []


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


@app.get("/items/")
async def read_items() -> list[Item]:
    return [