Vous pouvez retourner un dictionnaire (`dict`), une liste (`list`), des valeurs seules comme des chaines de caractères (`str`) et des entiers (`int`), etc.

Vous pouvez aussi retourner des models **Pydantic** (qui seront détaillés plus tard).

Il y a de nombreux autres objets et modèles qui seront automatiquement convertis en JSON. Essayez d'utiliser vos favoris, il est fort probable qu'ils soient déjà supportés.

    public Collection<ToolchainModel> getToolchains() {
        return getMavenSession().getRequest().getToolchains().values().stream()
                .flatMap(Collection::stream)
                .map(org.apache.maven.toolchain.model.ToolchainModel::getDelegate)
                .toList();
    }

    @Nonnull
    @Override
    public Map<String, String> getUserProperties() {

 * specific language governing permissions and limitations
 * under the License.
 */
package org.apache.maven.plugin;

import java.util.List;

import org.apache.maven.execution.MavenSession;
import org.apache.maven.model.Plugin;
import org.apache.maven.plugin.descriptor.MojoDescriptor;
import org.apache.maven.plugin.descriptor.PluginDescriptor;
import org.apache.maven.project.MavenProject;
import org.eclipse.aether.RepositorySystemSession;

        MavenProject p1 = new MavenProject();
        p1.setGroupId("g");
        p1.setArtifactId("p1");
        p1.getBuild().getPlugins().add(new Plugin(org.apache.maven.api.model.Plugin.newBuilder()
                .groupId("g").artifactId("p2")
                .
                .build()))
        MavenProject p2 = new MavenProject();
        p2.setGroupId("g");

from datetime import date

from pydantic import BaseModel

# Değişkeni str olarak belirt
# ve o fonksiyon için harika bir editör desteği al
def main(user_id: str):
    return user_id


# Pydantic modeli
class User(BaseModel):
    id: int
    name: str
    joined: date
```

Sonrasında bu şekilde kullanabilirsin

```Python
my_user: User = User(id=3, name="John Doe", joined="2018-07-19")

```

### `RequestValidationError` vs `ValidationError`

/// warning | 警告

如果您觉得现在还用不到以下技术细节，可以先跳过下面的内容。

///

`RequestValidationError` 是 Pydantic 的 <a href="https://docs.pydantic.dev/latest/concepts/models/#error-handling" class="external-link" target="_blank">`ValidationError`</a> 的子类。

**FastAPI** 调用的就是 `RequestValidationError` 类，因此，如果在 `response_model` 中使用 Pydantic 模型，且数据有错误时，在日志中就会看到这个错误。

            return null;
        }
    }

    /**
     * Implementation of {@link ModelSource} that extends {@link PathSource} with model-specific
     * functionality. This implementation uses request-scoped caching ({@link CacheRetention#REQUEST_SCOPED})
     * since it represents a POM file that is actively being built and may change during the build process.

### Memory per Process { #memory-per-process }

Now, when the program loads things in memory, for example, a machine learning model in a variable, or the contents of a large file in a variable, all that **consumes a bit of the memory (RAM)** of the server.

    full_name = user_dict["full_name"],
    disabled = user_dict["disabled"],
    hashed_password = user_dict["hashed_password"],
)
```

/// info | 说明

`user_dict` 的说明，详见[**更多模型**一章](../extra-models.md#user_indict){.internal-link target=_blank}。

///

## 返回 Token

`token` 端点的响应必须是 JSON 对象。

响应返回的内容应该包含 `token_type`。本例中用的是**Bearer**Token，因此， Token 类型应为**`bearer`**。

Wenn Sie diese Sichtweise (des *externen Entwicklers*) vorübergehend übernehmen, wird es offensichtlicher, wo die Parameter, das Pydantic-Modell für den Body, die Response, usw. für diese *externe API* hingehören.

///

### Einen Callback-`APIRouter` erstellen { #create-a-callback-apirouter }