def test_path_operation_img(client: TestClient):
    shutil.copy("./docs/en/docs/img/favicon.png", "./image.png")
    response = client.get("/items/foo?img=1")
    assert response.status_code == 200, response.text
    assert response.headers["Content-Type"] == "image/png"
    assert len(response.content)
    os.remove("./image.png")


def test_openapi_schema(client: TestClient):
    response = client.get("/openapi.json")

def test_path_operation_img(client: TestClient):
    shutil.copy("./docs/en/docs/img/favicon.png", "./image.png")
    response = client.get("/items/foo?img=1")
    assert response.status_code == 200, response.text
    assert response.headers["Content-Type"] == "image/png"
    assert len(response.content)
    os.remove("./image.png")


def test_openapi_schema(client: TestClient):
    response = client.get("/openapi.json")

}


app = FastAPI()


@app.get(
    "/items/{item_id}",
    response_model=Item,
    responses={**responses, 200: {"content": {"image/png": {}}}},
)
async def read_item(item_id: str, img: bool | None = None):
    if img:
        return FileResponse("image.png", media_type="image/png")
    else:

    "/items/{item_id}",
    response_model=Item,
    responses={
        200: {
            "content": {"image/png": {}},
            "description": "Return the JSON item or an image.",
        }
    },
)
async def read_item(item_id: str, img: bool | None = None):
    if img:
        return FileResponse("image.png", media_type="image/png")
    else:

///

## Bodys aus reinen Listen { #bodies-of-pure-lists }

Wenn das äußerste Element des JSON-Bodys, das Sie erwarten, ein JSON-`array` (eine Python-`list`) ist, können Sie den Typ im Funktionsparameter deklarieren, mit der gleichen Syntax wie in Pydantic-Modellen:

```Python
images: list[Image]
```

so wie in:

Observa cómo `Offer` tiene una lista de `Item`s, que a su vez tienen una lista opcional de `Image`s

///

## Cuerpos de listas puras { #bodies-of-pure-lists }

Si el valor superior del cuerpo JSON que esperas es un `array` JSON (una `list` en Python), puedes declarar el tipo en el parámetro de la función, al igual que en los modelos Pydantic:

```Python
images: list[Image]
```

como en:

```Python
images: list[Image]
```

as in:

{* ../../docs_src/body_nested_models/tutorial008_py39.py hl[13] *}

## Editor support everywhere { #editor-support-everywhere }

And you get editor support everywhere.

Even for items inside of lists:

<img src="/img/tutorial/body-nested-models/image01.png">

///

## Тела с чистыми списками элементов { #bodies-of-pure-lists }

Если верхний уровень значения тела JSON-объекта представляет собой JSON `array` (в Python — `list`), вы можете объявить тип в параметре функции, так же как в моделях Pydantic:

```Python
images: list[Image]
```

например так:

For example, you can add an additional media type of `image/png`, declaring that your *path operation* can return a JSON object (with media type `application/json`) or a PNG image:

{* ../../docs_src/additional_responses/tutorial002_py310.py hl[17:22,26] *}

/// note

Notice that you have to return the image using a `FileResponse` directly.

///

/// info

  top: -10px;
  right: 0;
  font-size: 0.5rem;
  color: #e6e6e6;
  background-color: #666;
  border-radius: 10px;
  padding: 0 10px;
  z-index: 10;
}

.announce-wrapper .sponsor-image {
  display: block;
  border-radius: 20px;
}

.announce-wrapper>div {
  min-height: 40px;
  display: flex;
  align-items: center;
}

.twitter {
  color: #00acee;
}