# 声明请求示例数据 { #declare-request-example-data }

你可以为你的应用将接收的数据声明示例。

这里有几种实现方式。

## Pydantic 模型中的额外 JSON Schema 数据 { #extra-json-schema-data-in-pydantic-models }

你可以为一个 Pydantic 模型声明 `examples`，它们会被添加到生成的 JSON Schema 中。

{* ../../docs_src/schema_extra_example/tutorial001_py310.py hl[13:24] *}

这些额外信息会原样添加到该模型输出的 JSON Schema 中，并会在 API 文档中使用。

你可以使用属性 `model_config`，它接收一个 `dict`，详见 [Pydantic 文档：配置](https://docs.pydantic.dev/latest/api/config/)。

            field_info=FieldInfo(annotation=model),
            name=model.__name__,
            mode="validation",
        )
        for model in flat_validation_models
    ]
    flat_serialization_model_fields = [
        ModelField(
            field_info=FieldInfo(annotation=model),
            name=model.__name__,
            mode="serialization",
        )
        for model in flat_serialization_models
    ]

 * KIND, either express or implied.  See the License for the
 * specific language governing permissions and limitations
 * under the License.
 */
package org.apache.maven.cli.internal.extension.model;

/**
 * Describes a build extension to utilise.
 *
 * @deprecated Use {@link org.apache.maven.api.cli.extensions.CoreExtension} instead
 */
@Deprecated
@SuppressWarnings("all")

        @Test
        @DisplayName("should inherit behavior from AbstractUpgradeGoal")
        void shouldInheritBehaviorFromAbstractUpgradeGoal() {
            // This test verifies that Check inherits the model version logic from AbstractUpgradeGoal
            // The actual logic is tested in AbstractUpgradeGoalTest
            // Here we just verify that Check is properly configured as a subclass

# Response Status Code { #response-status-code }

The same way you can specify a response model, you can also declare the HTTP status code used for the response with the parameter `status_code` in any of the *path operations*:

* `@app.get()`
* `@app.post()`
* `@app.put()`
* `@app.delete()`
* etc.

{* ../../docs_src/response_status_code/tutorial001_py310.py hl[6] *}

/// note

* documentación de datos, etc.

Esto funciona de la misma manera que con los modelos de Pydantic. Y en realidad se logra de la misma manera internamente, utilizando Pydantic.

/// info | Información

Ten en cuenta que los dataclasses no pueden hacer todo lo que los modelos de Pydantic pueden hacer.

Así que, podrías necesitar seguir usando modelos de Pydantic.

* serialização de dados
* documentação de dados, etc.

Isso funciona da mesma forma que com os modelos Pydantic. E na verdade é alcançado da mesma maneira por baixo dos panos, usando Pydantic.

/// info | Informação

Lembre-se de que dataclasses não podem fazer tudo o que os modelos Pydantic podem fazer.

Então, você ainda pode precisar usar modelos Pydantic.

* callback hell: callback hell (clarify as `ад обратных вызовов`)
* on the fly: на лету
* scratch the surface: поверхностно ознакомиться
* tip: совет (or `подсказка` depending on context)
* Pydantic model: Pydantic-модель (`модель Pydantic` and `Pydantic модель` are also fine)
* declare: объявить
* have the next best performance, after: быть на следующем месте по производительности после

qui sont relativement fortement couplées.

/// check | A inspiré **FastAPI** à

Définir des validations supplémentaires pour les types de données utilisant la valeur « par défaut » des attributs du modèle. Ceci améliore le support de l'éditeur, et n'était pas disponible dans Pydantic auparavant.

            <artifactId>jackson-databind</artifactId>
            <version>${jackson.version}</version>
        </dependency>
        <dependency>
            <groupId>com.fasterxml.jackson.module</groupId>
            <artifactId>jackson-module-kotlin</artifactId>
            <version>${jackson.version}</version>
        </dependency>
        <dependency>
            <groupId>com.google.code.gson</groupId>