```

FastAPI will use this return type to:

* **Validate** the returned data.
    * If the data is invalid (e.g. you are missing a field), it means that *your* app code is broken, not returning what it should, and it will return a server error instead of returning incorrect data. This way you and your clients can be certain that they will receive the data and the data shape expected.
* Add a **JSON Schema** for the response, in the OpenAPI *path operation*.

And of course, it supports the same:

* data validation
* data serialization
* data documentation, etc.

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

!!! info

One of the main features needed by API systems is data "<abbr title="also called marshalling, conversion">serialization</abbr>" which is taking data from the code (Python) and converting it into something that can be sent through the network. For example, converting an object containing data from a database into a JSON object. Converting `datetime` objects into strings, etc.

    * If you didn't use FastAPI and used Starlette directly (or another tool, like Sanic, Flask, Responder, etc) you would have to implement all the data validation and serialization yourself. So, your final application would...

            generated OpenAPI, but currently it doesn't affect the parsing of the data.
            """
        ),
    ] = "multipart/form-data",
    alias: Annotated[
        Optional[str],
        Doc(
            """
            An alternative name for the parameter field.

            This will be used to extract the data and for the generated OpenAPI.

from dirty_equals import IsDict
from fastapi.testclient import TestClient

from docs_src.extra_data_types.tutorial001 import app

client = TestClient(app)


def test_extra_types():
    item_id = "ff97dd87-a4a5-4a12-b412-cde99f33e00e"
    data = {
        "start_datetime": "2018-12-22T14:00:00+00:00",
        "end_datetime": "2018-12-24T15:00:00+00:00",
        "repeat_at": "15:30:00",
        "process_after": 300,
    }

        },
    },
)
async def create_item(request: Request):
    raw_body = await request.body()
    try:
        data = yaml.safe_load(raw_body)
    except yaml.YAMLError:
        raise HTTPException(status_code=422, detail="Invalid YAML")
    try:
        item = Item.model_validate(data)
    except ValidationError as e:
        raise HTTPException(status_code=422, detail=e.errors(include_url=False))

    return client


def test_post_body_form(client: TestClient):
    response = client.post("/login/", data={"username": "Foo", "password": "secret"})
    assert response.status_code == 200
    assert response.json() == {"username": "Foo"}


def test_post_body_form_no_password(client: TestClient):
    response = client.post("/login/", data={"username": "Foo"})
    assert response.status_code == 422
    assert response.json() == IsDict(

#### Data "schema"

The term "schema" might also refer to the shape of some data, like a JSON content.

In that case, it would mean the JSON attributes, and data types they have, etc.

#### OpenAPI and JSON Schema

            ],
            "body": {"title": "towel", "size": "XL"},
        }
    )


def test_post():
    data = {"title": "towel", "size": 5}
    response = client.post("/items/", json=data)
    assert response.status_code == 200, response.text
    assert response.json() == data


def test_openapi_schema():
    response = client.get("/openapi.json")
    assert response.status_code == 200, response.text