"""
        ),
    ] = None,
    description: Annotated[
        str | None,
        Doc(
            """
            Human-readable description.

            Read more about it in the
            [FastAPI docs about Query parameters](https://fastapi.tiangolo.com/tutorial/query-params-str-validations/#declare-more-metadata)
            """

Les paramètres de la fonction seront reconnus comme tel :

* Si le paramètre est aussi déclaré dans le **chemin**, il sera utilisé comme paramètre de chemin.
* Si le paramètre est d'un **type singulier** (comme `int`, `float`, `str`, `bool`, etc.), il sera interprété comme un paramètre de **requête**.
* Si le paramètre est déclaré comme ayant pour type un **modèle Pydantic**, il sera interprété comme faisant partie du **corps** de la requête.

            assertEquals(1, existsExceptions.length, "exists() should declare CIFSException");
            assertEquals(CIFSException.class, existsExceptions[0], "Should declare CIFSException");
            assertEquals(1, getTypeExceptions.length, "getType() should declare CIFSException");
            assertEquals(CIFSException.class, getTypeExceptions[0], "Should declare CIFSException");
        }

        @Test

By default FastAPI returns JSON responses.

If you declare a [Response Model](../tutorial/response-model.md) FastAPI will use it to serialize the data to JSON, using Pydantic.

If you don't declare a response model, FastAPI will use the `jsonable_encoder` explained in [JSON Compatible Encoder](../tutorial/encoder.md) and put it in a `JSONResponse`.

/// tip

If you have strict type checks in your editor, mypy, etc, you can declare the function return type as `Any`.

That way you tell the editor that you are intentionally returning anything. But FastAPI will still do the data documentation, validation, filtering, etc. with the `response_model`.

///

### `response_model` Priority { #response-model-priority }

/// note | Nota

Isso é suportado desde o FastAPI versão `0.115.0`. 🤓

///

## Parâmetros de Consulta com um Modelo Pydantic { #query-parameters-with-a-pydantic-model }

Declare os **parâmetros de consulta** que você precisa em um **modelo Pydantic**, e então declare o parâmetro como `Query`:

{* ../../docs_src/query_param_models/tutorial001_an_py310.py hl[9:13,17] *}

}

interface ModuleTargetRuntimes {

    /**
     * Declare that this Gradle module runs as part of the wrapper or as part of a client process.
     * Client processes include the Tooling API client or the CLI client.
     */
    val client: Property<Boolean>

    /**
     * Declare that this Gradle module runs as part of the Gradle daemon.
     */
    val daemon: Property<Boolean>

But you still want to be able to filter and convert the data you return with a `response_model`.

For those cases, you can use a `Response` parameter.

## Use a `Response` parameter { #use-a-response-parameter }

You can declare a parameter of type `Response` in your *path operation function* (as you can do for cookies and headers).

And then you can set the `status_code` in that *temporal* response object.

# Utiliser Request directement { #using-the-request-directly }

Jusqu'à présent, vous avez déclaré les parties de la requête dont vous avez besoin, avec leurs types.

En récupérant des données depuis :

* Le chemin, sous forme de paramètres.
* En-têtes.
* Cookies.
* etc.

Et ce faisant, **FastAPI** valide ces données, les convertit et génère automatiquement la documentation de votre API.

We could do better.

We can declare a `UserBase` model that serves as a base for our other models. And then we can make subclasses of that model that inherit its attributes (type declarations, validation, etc).

All the data conversion, validation, documentation, etc. will still work as normally.