Estes tipos que possuem tipos internos são chamados de tipos "**genéricos**". E é possível declará-los mesmo com os seus tipos internos.

Para declarar esses tipos e os tipos internos, você pode usar o módulo Python padrão `typing`. Ele existe especificamente para suportar esses type hints.

#### Versões mais recentes do Python

## ドキュメントを見る

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

## レスポンスモデルのエンコーディングパラメータ

レスポンスモデルにはデフォルト値を設定することができます:

{* ../../docs_src/response_model/tutorial004.py hl[11,13,14] *}

El `app_name` sería `"ChimichangApp"`.

Y el `items_per_user` mantendría su valor por defecto de `50`.

## Configuraciones en otro módulo

Podrías poner esas configuraciones en otro archivo de módulo como viste en [Aplicaciones Más Grandes - Múltiples Archivos](../tutorial/bigger-applications.md){.internal-link target=_blank}.

Por ejemplo, podrías tener un archivo `config.py` con:

    *   `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`,

## Resources

*   [TensorFlow.org](https://www.tensorflow.org)
*   [TensorFlow Tutorials](https://www.tensorflow.org/tutorials/)
*   [TensorFlow Official Models](https://github.com/tensorflow/models/tree/master/official)
*   [TensorFlow Examples](https://github.com/tensorflow/examples)
*   [TensorFlow Codelabs](https://codelabs.developers.google.com/?cat=TensorFlow)

     * If the JAR is modular, its module information are ignored.
     * This type is new in Maven 4.
     */
    String CLASSPATH_JAR = "classpath-jar";

    /**
     * Artifact type name for a JAR file to unconditionally place on the module path.
     * If the JAR is not modular, then it is loaded by Java as an unnamed module.
     * This type is new in Maven 4.
     */

///

## JWT-Token verarbeiten

Importieren Sie die installierten Module.

Erstellen Sie einen zufälligen geheimen Schlüssel, der zum Signieren der JWT-Tokens verwendet wird.

Um einen sicheren zufälligen geheimen Schlüssel zu generieren, verwenden Sie den folgenden Befehl:

    Map<Dependency, Path> getDependencies();

    /**
     * Returns the Java module name of the dependency at the given path.
     * The given dependency should be one of the paths returned by {@link #getDependencies()}.
     * The module name is extracted from the {@code module-info.class} file if present, otherwise from
     * the {@code "Automatic-Module-Name"} attribute of the {@code META-INF/MANIFEST.MF} file if present.
     *

  <modules>
    <module>maven-api-annotations</module>
    <module>maven-api-di</module>
    <module>maven-api-xml</module>
    <module>maven-api-model</module>
    <module>maven-api-plugin</module>
    <module>maven-api-settings</module>
    <module>maven-api-toolchain</module>
    <module>maven-api-metadata</module>
    <module>maven-api-core</module>
    <module>maven-api-spi</module>

TensorFlow
[**models**](https://developers.google.com/machine-learning/glossary/#model) (to
use a term commonly used by machine learning practitioners) are expressed as
programs that TensorFlow executes. TensorFlow programs are encoded as
computation
[**graphs**](https://developers.google.com/machine-learning/glossary/#graph).
Since models are practically programs that TensorFlow executes, using untrusted