class Model(BaseModel):
    param: str = Field(alias="param_alias")


@app.get("/query")
async def query_model(data: Model = Query()):
    return {"param": data.param}


@app.get("/header")
async def header_model(data: Model = Header()):
    return {"param": data.param}


@app.get("/cookie")
async def cookie_model(data: Model = Cookie()):
    return {"param": data.param}

### Files and Async { #files-and-async }

In most cases, file-like objects are not compatible with async and await by default.

For example, they don't have an `await file.read()`, or `async for chunk in file`.

        allowEmpty = false,
        description =
            "Command line option for the performance test task to enable profiling. " +
                "For example `async-profiler`, `async-profiler-heap`, `async-profiler-wall`, `async-profiler-all` or `jfr`. Use `none` for benchmarking only.",
    )
}

object AdHocPerformanceScenarioLinux : AdHocPerformanceScenario(Os.LINUX)

```Python
with open("file.txt") as file:
    file.read()
```

En versiones recientes de Python, también hay un **async context manager**. Lo usarías con `async with`:

```Python
async with lifespan(app):
    await do_stuff()
```

### Gérer les fichiers et async { #files-and-async }

Dans la plupart des cas, les objets de type fichier ne sont pas compatibles avec `async` et `await` par défaut.

Par exemple, ils n'ont pas de `await file.read()`, ni de `async for chunk in file`.

  int num_retvals = 1;
  TFE_Execute(matmul, &retvals[0], &num_retvals, status);
  EXPECT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);

  // TODO(gjn): Add support for waiting on async local mirrors
  if (!remote && !async && !remote_func_outputs) {
    auto remote_arg =
        tensorflow::TensorHandleFromInterface(tensorflow::unwrap(h1_task2));
    // The input handles should never change since they have been mirrored.

* `close()`: Schließt die Datei.

Da alle diese Methoden `async`hron sind, müssen Sie sie „await“en („erwarten“).

Zum Beispiel können Sie innerhalb einer `async` *Pfadoperation-Funktion* den Inhalt wie folgt auslesen:

```Python
contents = await myfile.read()
```

## `async` oder nicht `async` { #to-async-or-not-to-async }

Da Abhängigkeiten auch von **FastAPI** aufgerufen werden (so wie Ihre *Pfadoperation-Funktionen*), gelten beim Definieren Ihrer Funktionen die gleichen Regeln.

Sie können `async def` oder einfach `def` verwenden.

import anyio
from fastapi import FastAPI
from fastapi.responses import StreamingResponse

app = FastAPI()


async def fake_video_streamer():
    for i in range(10):
        yield b"some fake video bytes"
        await anyio.sleep(0)


@app.get("/")
async def main():

app = FastAPI()
router = APIRouter()


@router.get("/a", responses={501: {"description": "Error 1"}})
async def a():
    return "a"


@router.get(
    "/b",
    responses={
        502: {"description": "Error 2"},
        "4XX": {"description": "Error with range, upper"},
    },
)
async def b():
    return "b"


@router.get(
    "/c",
    responses={