Mejora el soporte del editor para modelos de Pydantic, con:

* autocompletado
* chequeo de tipos
* refactorización
* búsqueda
* inspecciones

///

## Usa el modelo { #use-the-model }

Dentro de la función, puedes acceder a todos los atributos del objeto modelo directamente:

{* ../../docs_src/body/tutorial002_py310.py *}

Aynı şekilde bu veritabanı bir Pydantic model'i (attribute'lara sahip bir obje) de kabul etmez; yalnızca bir `dict` kabul eder.

Bunun için `jsonable_encoder` kullanabilirsiniz.

Bir Pydantic model gibi bir obje alır ve JSON ile uyumlu bir versiyonunu döndürür:

{* ../../docs_src/encoder/tutorial001_py310.py hl[4,21] *}

Bu örnekte, Pydantic model'i bir `dict`'e, `datetime`'ı da bir `str`'e dönüştürür.

Pydantic ne prend pas en charge le fait d'avoir un modèle Pydantic v2 contenant des champs eux-mêmes définis comme des modèles Pydantic v1, et inversement.

```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

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

/// info

For a more complete explanation of `**user_dict` check back in [the documentation for **Extra Models**](../extra-models.md#about-user-in-dict).

///

## Return the token { #return-the-token }

The response of the `token` endpoint must be a JSON object.

    page = context.new_page()
    page.goto("http://localhost:8000/docs")
    page.get_by_role("link", name="/items/").click()
    # Manually add the screenshot
    page.screenshot(path="docs/en/docs/img/tutorial/cookie-param-models/image01.png")

    # ---------------------
    context.close()
    browser.close()


process = subprocess.Popen(
    ["fastapi", "run", "docs_src/cookie_param_models/tutorial001.py"]
)
try:

## ドキュメントを見る { #see-it-in-the-docs }

自動ドキュメントを見ると、入力モデルと出力モデルがそれぞれ独自のJSON Schemaを持っていることが確認できます:

<img src="/img/tutorial/response-model/image01.png">

そして、両方のモデルは対話型のAPIドキュメントに使用されます:

<img src="/img/tutorial/response-model/image02.png">

## その他の戻り値の型アノテーション { #other-return-type-annotations }

Pydanticフィールドとして有効ではないものを返し、ツール（エディタやmypyなど）が提供するサポートを得るためだけに、関数でそれをアノテーションするケースがあるかもしれません。

如此，你就能同時擁有兩種好處：具備工具支援的型別註解，以及資料過濾。

## 在文件中查看 { #see-it-in-the-docs }

在自動文件中，你可以看到輸入模型與輸出模型各自都有自己的 JSON Schema：

<img src="/img/tutorial/response-model/image01.png">

而且兩個模型都會用在互動式 API 文件中：

<img src="/img/tutorial/response-model/image02.png">

## 其他回傳型別註解 { #other-return-type-annotations }

有時你回傳的東西不是有效的 Pydantic 欄位，你仍會在函式上加上註解，只為了獲得工具（編輯器、mypy 等）提供的支援。

    *   `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`,

specific language governing permissions and limitations
under the License.
-->

<model xmlns="http://codehaus-plexus.github.io/MODELLO/2.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xsi:schemaLocation="http://codehaus-plexus.github.io/MODELLO/2.0.0 https://codehaus-plexus.github.io/modello/xsd/modello-2.0.0.xsd"
  xml.namespace="http://maven.apache.org/LIFECYCLE/${version}"

    /**
     * Injects plugin executions induced by lifecycle bindings into the specified model. The model has already undergone
     * injection of plugin management so any plugins that are injected by lifecycle bindings and are not already present
     * in the model's plugin section need to be subjected to the model's plugin management.
     *
     * @param model The model into which to inject the default plugin executions for its packaging, must not be