<img src="/img/tutorial/response-status-code/image01.png">

/// note | Remarque

Certains codes de réponse (voir la section suivante) indiquent que la réponse n'a pas de corps.

FastAPI le sait et produira une documentation OpenAPI indiquant qu'il n'y a pas de corps de réponse.

///

## À propos des codes d'état HTTP { #about-http-status-codes }

/// note | Remarque

В этом случае функция задачи будет записывать данные в файл (имитируя отправку письма).

Так как операция записи не использует `async` и `await`, мы определим функцию как обычную `def`:

{* ../../docs_src/background_tasks/tutorial001_py310.py hl[6:9] *}

## Добавление фоновой задачи { #add-the-background-task }

### `typing` module { #typing-module }

For some additional use cases, you might need to import some things from the standard library `typing` module, for example when you want to declare that something has "any type", you can use `Any` from `typing`:

```python
from typing import Any


def some_function(data: Any):
    print(data)
```

//
// This method parses the token but doesn't validate the signature. It's only
// ever useful in cases where you know the signature is valid (because it has
// been checked previously in the stack) and you want to extract values from
// it.
func ParseUnverifiedStandardClaims(token []byte, claims *StandardClaims, buf []byte) (*SigningMethodHMAC, error) {
	if bytes.Count(token, []byte(".")) != 2 {

# renaming the wheels as uploading to PyPI would fail with "File already exists"
# error. In order to upload to PyPI, we unpack the wheel and change the tag
# inside a metadata file to the one we want (e.g cp38-cp38-win_amd) and then
# re-pack it to generate it as a platform specific wheel with this new wheel
#tag.
python3 -m wheel unpack "${pure_python_whl}"

# Remove the pure python wheel.
rm -rf "${pure_python_whl}"

          object : WebSocketListener() {
            override fun onOpen(
              webSocket: WebSocket,
              response: Response,
            ) {
              try {
                latch.await() // The server can't respond to pings!
              } catch (e: InterruptedException) {
                throw AssertionError(e)
              }
            }
          },
        ).build(),
    )

    return {"item_id": item_id, "q": q}
```

<details markdown="1">
<summary>Або використайте <code>async def</code>...</summary>

Якщо ваш код використовує `async` / `await`, скористайтеся `async def`:

```Python hl_lines="7  12"
from fastapi import FastAPI

app = FastAPI()


@app.get("/")
async def read_root():
    return {"Hello": "World"}

{* ../../docs_src/response_model/tutorial001_01_py310.py hl[16,21] *}

Comme cela se passe côté Rust, la performance sera bien meilleure que si cela était fait avec le Python classique et la classe `JSONResponse`.

data: {"name": "Plumbus", "price": 32.99}

```

SSE is commonly used for AI chat streaming, live notifications, logs and observability, and other cases where the server pushes updates to the client.

/// tip

If you want to stream binary data, for example video or audio, check the advanced guide: [Stream Data](../advanced/stream-data.md).

///

## Stream SSE with FastAPI { #stream-sse-with-fastapi }

name: Contributor CI Build

on:
  push:
    branches:
      - master
  pull_request:
    branches:
      - master

concurrency:
  # On master/release, we don't want any jobs cancelled so the sha is used to name the group
  group: ${{ (github.ref == 'refs/heads/master' || github.ref == 'refs/heads/release' ) && format('contributor-pr-base-{0}', github.sha) || format('contributor-pr-{0}', github.ref) }}
  cancel-in-progress: false

env: