return True
    current_status_code = int(status_code)
    return not (current_status_code < 200 or current_status_code in {204, 205, 304})


def get_path_param_names(path: str) -> Set[str]:
    return set(re.findall("{(.*?)}", path))


def create_response_field(
    name: str,
    type_: Type[Any],
    class_validators: Optional[Dict[str, Validator]] = None,
    default: Optional[Any] = Undefined,

    browser.close()


process = subprocess.Popen(
    ["uvicorn", "docs_src.separate_openapi_schemas.tutorial001:app"]
)
try:
    with sync_playwright() as playwright:
        run(playwright)
finally:

    * No escribirías una aplicación directamente en Uvicorn. Eso significaría que tu código tendría que incluir más o menos, al menos, todo el código proporcionado por Starlette (o **FastAPI**). Y si hicieras eso, tu aplicación final tendría la misma sobrecarga que si hubieras usado un framework y minimizado el código de tu aplicación y los errores.
    * Si estás comparando Uvicorn, compáralo con los servidores de aplicaciones Daphne, Hypercorn, uWSGI, etc.

    * You wouldn't write an application in Uvicorn directly. That would mean that your code would have to include more or less, at least, all the code provided by Starlette (or **FastAPI**). And if you did that, your final application would have the same overhead as having used a framework and minimizing your app code and bugs.
    * If you are comparing Uvicorn, compare it against Daphne, Hypercorn, uWSGI, etc. Application servers.
* **Starlette**:

    * Wenn Sie FastAPI nicht, und direkt Starlette (oder ein anderes Tool wie Sanic, Flask, Responder, usw.) verwenden würden, müssten Sie die gesamte Datenvalidierung und Serialisierung selbst implementieren. Ihre finale Anwendung hätte also immer noch den gleichen Overhead, als ob sie mit FastAPI erstellt worden wäre. Und in vielen Fällen ist diese Datenvalidierung und Serialisierung der größte Teil des in Anwendungen geschriebenen Codes.

automatically. And if you **build** the client it will error out if you have any **mismatch** in the data used.

So, you would **detect many errors** very early in the development cycle instead of having to wait for the errors to show up to your final users in production and then trying to debug where the problem is. ✨...

    ```

=== "Python 3.8+"

    ```Python hl_lines="15-20"
    {!> ../../../docs_src/sql_databases/sql_app/main.py!}
    ```

!!! info
    我们将`SessionLocal()`请求的创建和处理放在一个`try`块中。

    然后我们在finally块中关闭它。

    通过这种方式，我们确保数据库会话在请求后始终关闭。即使在处理请求时出现异常。

Building docs for: en
Building docs for: es
Successfully built docs for: es
```

</div>

This builds all those independent MkDocs sites for each language, combines them, and generates the final output at `./site/`.

Then you can serve that with the command `serve`:

<div class="termy">

```console
// Use the command "serve" after running "build-all"
$ python ./scripts/docs.py serve

    Then APIStar stopped to exist as a server and Starlette was created, and was a new better foundation for such a system. That was the final inspiration to build **FastAPI**.

    I consider **FastAPI** a "spiritual successor" to APIStar, while improving and increasing the features, typing system, and other parts, based on the learnings from all these previous tools.

    try:
        yield dep_a
    finally:
        dep_a.close()


async def dependency_b(dep_a=Depends(dependency_a)):
    dep_b = generate_dep_b()
    try:
        yield dep_b
    finally:
        dep_b.close(dep_a)


async def dependency_c(dep_b=Depends(dependency_b)):
    dep_c = generate_dep_c()
    try:
        yield dep_c
    finally: