return request.param


@pytest.fixture(name="client")
def get_client(mod_name: str):
    mod = importlib.import_module(f"docs_src.body_nested_models.{mod_name}")

    client = TestClient(mod.app)
    return client


def test_put_all(client: TestClient, mod_name: str):
    if mod_name.startswith("tutorial003"):
        tags_expected = IsList("foo", "bar", check_order=False)
    else:

## 사전정의 값

만약 *경로 매개변수*를 받는 *경로 작동*이 있지만, *경로 매개변수*로 가능한 값들을 미리 정의하고 싶다면 파이썬 표준 <abbr title="열거형(Enumeration)">`Enum`</abbr>을 사용할 수 있습니다.

### `Enum` 클래스 생성

`Enum`을 임포트하고 `str`과 `Enum`을 상속하는 서브 클래스를 만듭니다.

`str`을 상속함으로써 API 문서는 값이 `string` 형이어야 하는 것을 알게 되고 이는 문서에 제대로 표시됩니다.

가능한 값들에 해당하는 고정된 값의 클래스 어트리뷰트들을 만듭니다:

{* ../../docs_src/path_params/tutorial005.py hl[1,6:9] *}

/// info | 정보

from typing import Union

from fastapi import FastAPI, Query

app = FastAPI()


@app.get("/items/")
async def read_items(
    q: Union[str, None] = Query(default=None, min_length=3, max_length=50),
):
    results = {"items": [{"item_id": "Foo"}, {"item_id": "Bar"}]}
    if q:
        results.update({"q": q})

from typing import Union

from fastapi import FastAPI, Query

app = FastAPI()


@app.get("/items/")
async def read_items(
    q: Union[str, None] = Query(default=None, title="Query string", min_length=3),
):
    results = {"items": [{"item_id": "Foo"}, {"item_id": "Bar"}]}
    if q:
        results.update({"q": q})

from typing import Annotated, Union

from fastapi import FastAPI, Query

app = FastAPI()


@app.get("/items/")
async def read_items(q: Annotated[Union[str, None], Query(alias="item-query")] = None):
    results = {"items": [{"item_id": "Foo"}, {"item_id": "Bar"}]}
    if q:
        results.update({"q": q})

from fastapi import FastAPI, Path, Query

app = FastAPI()


@app.get("/items/{item_id}")
async def read_items(
    *,
    item_id: int = Path(title="The ID of the item to get", ge=0, le=1000),
    q: str,
    size: float = Query(gt=0, lt=10.5),
):
    results = {"item_id": item_id}
    if q:
        results.update({"q": q})
    if size:
        results.update({"size": size})

class FilterParams(BaseModel):
    model_config = {"extra": "forbid"}

    limit: int = Field(100, gt=0, le=100)
    offset: int = Field(0, ge=0)
    order_by: Literal["created_at", "updated_at"] = "created_at"
    tags: list[str] = []


@app.get("/items/")
async def read_items(filter_query: Annotated[FilterParams, Query()]):

from typing import Annotated, Union

from fastapi import Depends, FastAPI

app = FastAPI()


async def common_parameters(
    q: Union[str, None] = None, skip: int = 0, limit: int = 100
):
    return {"q": q, "skip": skip, "limit": limit}


@app.get("/items/")
async def read_items(commons: Annotated[dict, Depends(common_parameters)]):
    return commons


@app.get("/users/")

Dazu aktualisieren wir das Pydantic-Modell `TokenData` mit einem neuen Attribut `scopes`.

Durch die Validierung der Daten mit Pydantic können wir sicherstellen, dass wir beispielsweise präzise eine `list`e von `str`s mit den Scopes und einen `str` mit dem `username` haben.

Anstelle beispielsweise eines `dict`s oder etwas anderem, was später in der Anwendung zu Fehlern führen könnte und darum ein Sicherheitsrisiko darstellt.

    return JSONResponse(
        status_code=422,
        content=jsonable_encoder({"detail": exc.errors(), "body": exc.body}),
    )


class Item(BaseModel):
    title: str
    size: int


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