"price": 35.4,
            "images": {"url": "http://example.com/image.png", "name": "example image"},
        },
    )
    assert response.status_code == 422, response.text
    assert response.json() == {
        "detail": [
            {
                "loc": ["body", "images"],
                "input": {
                    "url": "http://example.com/image.png",
                    "name": "example image",

            "name": "Foo",
            "price": 35.4,
            "image": {"url": "http://example.com/image.png"},
        },
    )
    assert response.status_code == 422, response.text
    assert response.json() == {
        "detail": [
            {
                "loc": ["body", "image", "name"],
                "input": {"url": "http://example.com/image.png"},
                "msg": "Field required",

        <img class="sponsor-image" src="/img/sponsors/propelauth-banner.png" />
      </a>
    </div>
    <div class="item">
      <a title="Zuplo: Scale, Protect, Document, and Monetize your FastAPI" style="display: block; position: relative;" href="https://zuplo.link/fastapi-web" target="_blank">
        <span class="sponsor-badge">sponsor</span>
        <img class="sponsor-image" src="/img/sponsors/zuplo-banner.png" />
      </a>

            "name": "Foo",
            "price": 35.4,
            "image": {"url": "http://example.com/image.png"},
        },
    )
    assert response.status_code == 422, response.text
    assert response.json() == {
        "detail": [
            {
                "loc": ["body", "image", "name"],
                "input": {"url": "http://example.com/image.png"},
                "msg": "Field required",

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:

from fastapi import FastAPI
from pydantic import BaseModel, HttpUrl

app = FastAPI()


class Image(BaseModel):
    url: HttpUrl
    name: str


class Item(BaseModel):
    name: str
    description: Union[str, None] = None
    price: float
    tax: Union[float, None] = None
    tags: set[str] = set()
    images: Union[list[Image], None] = None


@app.put("/items/{item_id}")

from fastapi import FastAPI
from pydantic import BaseModel, HttpUrl

app = FastAPI()


class Image(BaseModel):
    url: HttpUrl
    name: str


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float | None = None
    tags: set[str] = set()
    images: list[Image] | None = None


class Offer(BaseModel):
    name: str
    description: str | None = None

  mssql:
    image: '${MSSQL_IMAGE}:latest'
    ports:
      - "127.0.0.1:9930:1433"
    environment:
      - TZ=Asia/Shanghai
      - ACCEPT_EULA=Y
      - MSSQL_SA_PASSWORD=LoremIpsum86
  tidb:
    image: 'pingcap/tidb:v6.5.0'
    ports:
      - "127.0.0.1:9940:4000"
    command: /tidb-server -store unistore -path "" -lease 0s > tidb.log 2>&1 &
  gaussdb:
    image: 'opengauss/opengauss:7.0.0-RC1.B023'

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

///

## Bodies of pure lists { #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]
```

as in:

    "/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: