when (type) {
      // Special case types that have 0 or 1 flag.
      TYPE_SETTINGS, TYPE_PING -> return if (flags == FLAG_ACK) "ACK" else BINARY[flags]
      TYPE_PRIORITY, TYPE_RST_STREAM, TYPE_GOAWAY, TYPE_WINDOW_UPDATE -> return BINARY[flags]
    }
    val result = if (flags < FLAGS.size) FLAGS[flags]!! else BINARY[flags]
    // Special case types that have overlap flag values.
    return when {

            signature.append(methodSignature);
            signature.append("(");
            if (matcher.group(5).length() > 0) {
                String[] types = PARAM_DELIMITER.split(matcher.group(5));
                for (int i = 0; i < types.length; i++) {
                    String type = types[i];
                    Matcher typeMatcher = TYPE_PATTERN.matcher(type);
                    if (!typeMatcher.matches()) {

 * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
 */
package jcifs.internal.witness;

/**
 * Enumeration of SMB Witness Service types as defined in MS-SWN specification.
 * These types represent different categories of witness services available in
 * clustered file server environments.
 */
public enum WitnessServiceType {
    /**

## Support de l'éditeur

Dans votre éditeur, vous aurez des annotations de types et de l'auto-complétion partout dans votre fonction (ce qui n'aurait pas été le cas si vous aviez utilisé un classique `dict` plutôt qu'un modèle Pydantic) :

<img src="/img/tutorial/body/image03.png">

Et vous obtenez aussi de la vérification d'erreur pour les opérations incorrectes de types :

<img src="/img/tutorial/body/image04.png">

                        }
                    }
                }
            }
        }
    }
}
```

## Additional media types for the main response { #additional-media-types-for-the-main-response }

You can use this same `responses` parameter to add different media types for the same main response.

from .utils import needs_py310


@needs_py310
def test_typing():
    types = {
        list[int]: [1, 2, 3],
        dict[str, list[int]]: {"a": [1, 2, 3], "b": [4, 5, 6]},
        set[int]: [1, 2, 3],  # `set` is converted to `list`
        tuple[int, ...]: [1, 2, 3],  # `tuple` is converted to `list`
    }
    for test_type, expect in types.items():
        app = FastAPI()

        @app.post("/", response_model=test_type)

    /**
     * Gets the class loader from which to load any types referenced by the configuration. If unset, the class loader of
     * the bean class will be used.
     *
     * @return The class loader to load referenced types from or {@code null} if unset.
     */
    ClassLoader getClassLoader();

    /**
     * Sets the class loader from which to load any types referenced by the configuration. If unset, the class loader of

 * errors for using {@code get(Comparator.class)} as a {@code Comparator<Foo>}, for example.
 * Immutable empty instances are returned for collection types; {@code ""} for string; {@code 0} for
 * number types; reasonable default instance for other stateless types. For mutable types, a fresh
 * instance is created each time {@code get()} is called.
 *
 * @author Kevin Bourrillion
 * @author Ben Yu
 * @since 12.0
 */

 * Maven plugins or extensions may provide implementations of those types which will be used by maven.
 * <p>
 * A type can be marked {@link Consumer} or {@link Provider} but not both. A type is assumed to be
 * {@link Consumer} if it is not marked either {@link Consumer} or {@link Provider}.
 * <p>
 * A package can be marked {@link Consumer}. In this case, all types in the package are considered

Hug foi um dos primeiros frameworks a implementar a declaração de tipos de parâmetros de API usando anotações de tipo do Python. Isso foi uma ótima ideia que inspirou outras ferramentas a fazer o mesmo.

Ele usou tipos personalizados em suas declarações em vez dos tipos padrão do Python, mas mesmo assim foi um grande passo adiante.