```Python hl_lines="11  14-16"
{!../../../docs_src/sub_applications/tutorial001.py!}
```

### Die Unteranwendung mounten

Mounten Sie in Ihrer Top-Level-Anwendung `app` die Unteranwendung `subapi`.

In diesem Fall wird sie im Pfad `/subapi` gemountet:

```Python hl_lines="11  19"
{!../../../docs_src/sub_applications/tutorial001.py!}
```

    ```

!!! info
    Notice how `Offer` has a list of `Item`s, which in turn have an optional list of `Image`s

## Bodies of pure lists

If the top level value of the JSON body you expect is a JSON `array` (a Python `list`), you can declare the type in the parameter of the function, the same as in Pydantic models:

```Python
images: List[Image]
```

    name: str

    if PYDANTIC_V2:
        model_config = {
            "json_schema_extra": {
                "x-something-internal": {"level": 4},
            }
        }
    else:

        class Config:
            schema_extra = {
                "x-something-internal": {"level": 4},
            }


@app.get("/foo", response_model=Item)
def foo():
    return {"name": "Foo item"}

    return [pet1, pet2]


client = TestClient(app)


def test_filter_top_level_model():
    response = client.post(
        "/users", json={"email": "******@****.***", "password": "secret"}
    )
    assert response.json() == {"email": "******@****.***"}


def test_filter_second_level_model():
    response = client.get("/pets/1")
    assert response.json() == {
        "name": "Nibbler",

Let's imagine that loading the model can **take quite some time**, because it has to read a lot of **data from disk**. So you don't want to do it for every request.

    head_commit: PartialGithubEventHeadCommit


class PartialGithubEvent(BaseModel):
    workflow_run: PartialGithubEventWorkflowRun


if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)
    settings = Settings()
    logging.info(f"Using config: {settings.json()}")
    g = Github(settings.input_token.get_secret_value())
    repo = g.get_repo(settings.github_repository)
    try:

Nevertheless, as of now, Uvicorn's capabilities for handling worker processes are more limited than Gunicorn's. So, if you want to have a process manager at this level (at the Python level), then it might be better to try with Gunicorn as the process manager.

In any case, you would run it like this:

<div class="termy">

```console

## Dependency Injection

Using `BackgroundTasks` also works with the dependency injection system, you can declare a parameter of type `BackgroundTasks` at multiple levels: in a *path operation function*, in a dependency (dependable), in a sub-dependency, etc.

It includes the `tags`, `parameters`, `requestBody`, `responses`, etc.

This *path operation*-specific OpenAPI schema is normally generated automatically by **FastAPI**, but you can also extend it.

!!! tip
    This is a low level extension point.

    If you only need to declare additional responses, a more convenient way to do it is with [Additional Responses in OpenAPI](additional-responses.md){.internal-link target=_blank}.

Dieses *Pfadoperation*-spezifische OpenAPI-Schema wird normalerweise automatisch von **FastAPI** generiert, Sie können es aber auch erweitern.

!!! tip "Tipp"
    Dies ist ein Low-Level Erweiterungspunkt.

    Wenn Sie nur zusätzliche Responses deklarieren müssen, können Sie dies bequemer mit [Zusätzliche Responses in OpenAPI](additional-responses.md){.internal-link target=_blank} tun.