## Use Case

Let's start with an example **use case** and then see how to solve it with this.

Let's imagine that you have some **machine learning models** that you want to use to handle requests. 🤖

The same models are shared among requests, so, it's not one model per request, or one per user or something similar.

     */
    void injectDefaultValues(Model model, ModelBuildingRequest request, ModelProblemCollector problems);

    org.apache.maven.api.model.Model mergeDuplicates(
            org.apache.maven.api.model.Model model, ModelBuildingRequest request, ModelProblemCollector problems);

    org.apache.maven.api.model.Model injectDefaultValues(
            org.apache.maven.api.model.Model model, ModelBuildingRequest request, ModelProblemCollector problems);

 */
package org.apache.maven.model.superpom;

import org.apache.maven.model.Model;

/**
 * Provides the super POM that all models implicitly inherit from.
 *
 * @deprecated use {@link org.apache.maven.api.services.ModelBuilder} instead
 */
@Deprecated(since = "4.0.0")
public interface SuperPomProvider {

    /**

            </goals>
            <configuration>
              <version>1.1.0</version>
              <models>
                <model>../../api/maven-api-metadata/src/main/mdo/metadata.mdo</model>
              </models>
              <templates>
                <template>model-v3.vm</template>
              </templates>
            </configuration>
          </execution>
        </executions>

{* ../../docs_src/body/tutorial001.py hl[4] *}

## Créez votre modèle de données

Déclarez ensuite votre modèle de données en tant que classe qui hérite de `BaseModel`.

Utilisez les types Python standard pour tous les attributs :

{* ../../docs_src/body/tutorial001.py hl[7:11] *}

     * @return the transformed model, or the input model if no transformation is needed
     * @throws ModelTransformerException
     */
    @Nonnull
    default Model transformFileModel(@Nonnull Model model) throws ModelTransformerException {
        return model;
    }

    /**
     * Apply a transformation on the raw models.
     *
     * This method will be called on each raw model being loaded,

import org.apache.maven.api.annotations.Nonnull;
import org.apache.maven.api.model.Model;

/**
 * Provides the super POM that all models implicitly inherit from.
 *
 * @since 4.0.0
 */
@Experimental
public interface SuperPomProvider extends Service {

    /**
     * Gets the super POM for the specified model version.
     *
     * @param version The model version to retrieve the super POM for (e.g. "4.0.0"), must not be {@code null}.

## 在文档中查看

当你查看自动化文档时，你可以检查输入模型和输出模型是否都具有自己的 JSON Schema：

<img src="https://fastapi.tiangolo.com/img/tutorial/response-model/image01.png">

并且两种模型都将在交互式 API 文档中使用：

<img src="https://fastapi.tiangolo.com/img/tutorial/response-model/image02.png">

## 响应模型编码参数

你的响应模型可以具有默认值，例如：

```Python hl_lines="11  13-14"
{!../../docs_src/response_model/tutorial004.py!}
```

Da mesma forma, este banco de dados não receberia um modelo Pydantic (um objeto com atributos), apenas um `dict`.

Você pode usar a função `jsonable_encoder` para resolver isso.

A função recebe um objeto, como um modelo Pydantic e retorna uma versão compatível com JSON:

//// tab | Python 3.10+

```Python hl_lines="4  21"

              <version>2.0.0</version>
              <velocityBasedir>${project.basedir}/../../src/mdo</velocityBasedir>
              <models>
                <model>src/main/mdo/settings.mdo</model>
              </models>
              <templates>
                <template>model.vm</template>
              </templates>
              <params>
                <param>packageModelV4=org.apache.maven.api.settings</param>