<artifactId>modello-maven-plugin</artifactId>
        <configuration>
          <version>1.2.0</version>
          <models>
            <model>src/main/mdo/core-extensions.mdo</model>
          </models>
          <templates>
            <template>model.vm</template>
          </templates>
          <params>
            <param>locationTracking=true</param>

    *   `Model.fit_generator`, `Model.evaluate_generator`,
        `Model.predict_generator`, `Model.train_on_batch`,
        `Model.test_on_batch`, and `Model.predict_on_batch` methods now respect
        the `run_eagerly` property, and will correctly run using `tf.function`
        by default. Note that `Model.fit_generator`, `Model.evaluate_generator`,

     *       {@code {"--module-path", "dir/path1:dir/path2"}} on Unix or
     *       {@code {"--module-path", "dir\path1;dir\path2"}} on Windows.</li>
     *   <li>If this type was created by {@code JavaPathType.patchModule("foo.bar")}, then the method returns
     *       {@code {"--patch-module", "foo.bar=dir/path1:dir/path2"}} on Unix or
     *       {@code {"--patch-module", "foo.bar=dir\path1;dir\path2"}} on Windows.</li>

But in real cases you will get better results using the integrated [Security utilities](security/index.md).

///

## Another module with `APIRouter` { #another-module-with-apirouter }

Let's say you also have the endpoints dedicated to handling "items" from your application in the module at `app/routers/items.py`.

You have *path operations* for:

* `/items/`
* `/items/{item_id}`

            }

            for (String module : parent.getModules()) {
                module = module.replace('\\', '/');

                if (module.regionMatches(true, module.length() - 4, ".xml", 0, 4)) {
                    module = module.substring(0, module.lastIndexOf('/') + 1);
                }

                String moduleName = module;
                if (moduleName.endsWith("/")) {

            api(distributionDependencies.findLibrary(alias).get().map { module ->
                if (module.group == "org.jetbrains.kotlin") {
                    module.copy().apply {
                        version {
                            strictly(kotlinVersion)
                        }
                    }
                } else {
                    module
                }
            })
        }
    }

{* ../../docs_src/body/tutorial001_py310.py hl[2] *}

## Crea tu modelo de datos { #create-your-data-model }

Luego, declaras tu modelo de datos como una clase que hereda de `BaseModel`.

Usa tipos estándar de Python para todos los atributos:

{* ../../docs_src/body/tutorial001_py310.py hl[5:9] *}

Si vous déclarez un [Modèle de réponse](../tutorial/response-model.md), FastAPI l'utilise pour sérialiser les données en JSON, en utilisant Pydantic.

Der `app_name` wäre „ChimichangApp“.

Und `items_per_user` würde seinen Defaultwert von `50` behalten.

## Einstellungen in einem anderen Modul { #settings-in-another-module }

Sie könnten diese Einstellungen in eine andere Moduldatei einfügen, wie Sie in [Größere Anwendungen – mehrere Dateien](../tutorial/bigger-applications.md) gesehen haben.

Pydantic açısından, alanları (field) Pydantic v1 modelleriyle tanımlanmış bir Pydantic v2 modeli (ya da tersi) kullanmak **desteklenmez**.

```mermaid
graph TB
    subgraph "❌ Not Supported"
        direction TB
        subgraph V2["Pydantic v2 Model"]
            V1Field["Pydantic v1 Model"]
        end
        subgraph V1["Pydantic v1 Model"]
            V2Field["Pydantic v2 Model"]
        end
    end

    style V2 fill:#f9fff3