def test_get_enums_lenet():
    response = client.get("/models/lenet")
    assert response.status_code == 200
    assert response.json() == {"model_name": "lenet", "message": "LeCNN all the images"}


def test_get_enums_resnet():
    response = client.get("/models/resnet")
    assert response.status_code == 200
    assert response.json() == {"model_name": "resnet", "message": "Have some residuals"}

## 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]
```

or in Python 3.9 and above:

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

as in:

=== "Python 3.9+"

{!../../../docs_src/body_nested_models/tutorial007.py!}
```

!!! info "정보"
    `Offer`가 선택사항 `Image` 리스트를 차례로 갖는 `Item` 리스트를 어떻게 가지고 있는지 주목하세요

## 순수 리스트의 본문

예상되는 JSON 본문의 최상위 값이 JSON `array`(파이썬 `list`)면, Pydantic 모델에서와 마찬가지로 함수의 매개변수에서 타입을 선언할 수 있습니다:

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

이를 아래처럼:

```Python hl_lines="15"
{!../../../docs_src/body_nested_models/tutorial008.py!}
```

# Static Files - `StaticFiles`

You can use the `StaticFiles` class to serve static files, like JavaScript, CSS, images, etc.

Read more about it in the [FastAPI docs for Static Files](https://fastapi.tiangolo.com/tutorial/static-files/).

You can import it directly from `fastapi.staticfiles`:

```python
from fastapi.staticfiles import StaticFiles
```

    ```

!!! info
    👀 ❔ `Offer` ✔️ 📇 `Item`Ⓜ, ❔ 🔄 ✔️ 📦 📇 `Image`Ⓜ

## 💪 😁 📇

🚥 🔝 🎚 💲 🎻 💪 👆 ⌛ 🎻 `array` (🐍 `list`), 👆 💪 📣 🆎 🔢 🔢, 🎏 Pydantic 🏷:

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

⚖️ 🐍 3️⃣.9️⃣ & 🔛:

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

:

=== "🐍 3️⃣.6️⃣ & 🔛"

    ```Python hl_lines="15"

        "rock",
        "metal",
        "bar"
    ],
    "images": [
        {
            "url": "http://example.com/baz.jpg",
            "name": "The Foo live"
        },
        {
            "url": "http://example.com/dave.jpg",
            "name": "The Baz"
        }
    ]
}
```

!!! info
    请注意 `images` 键现在具有一组 image 对象是如何发生的。

## 深度嵌套模型

你可以定义任意深度的嵌套模型：

{!../../../docs_src/body_nested_models/tutorial007.py!}
```

!!! info "情報"
    `Offer`は`Item`のリストであり、オプションの`Image`のリストを持っていることに注目してください。

## 純粋なリストのボディ

期待するJSONボディのトップレベルの値がJSON`array`（Pythonの`list`）であれば、Pydanticモデルと同じように、関数のパラメータで型を宣言することができます:

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

以下のように:

```Python hl_lines="15"
{!../../../docs_src/body_nested_models/tutorial008.py!}
```

        "bar"
    ],
    "images": [
        {
            "url": "http://example.com/baz.jpg",
            "name": "The Foo live"
        },
        {
            "url": "http://example.com/dave.jpg",
            "name": "The Baz"
        }
    ]
}
```

!!! info "informação"
    Note como o campo `images` agora tem uma lista de objetos de image.

## Modelos profundamente aninhados

    if model_name is ModelName.alexnet:
        return {"model_name": model_name, "message": "Deep Learning FTW!"}

    if model_name.value == "lenet":
        return {"model_name": model_name, "message": "LeCNN all the images"}

    page.get_by_role("tab", name="Schema").click()
    page.get_by_label("Schema").get_by_role("button", name="Expand all").click()
    page.screenshot(
        path="docs/en/docs/img/tutorial/separate-openapi-schemas/image03.png"
    )

    # ---------------------
    context.close()
    browser.close()


process = subprocess.Popen(
    ["uvicorn", "docs_src.separate_openapi_schemas.tutorial001:app"]
)
try: