* It also handles the conversion of voiced and semi-voiced sound marks.
 *
 * <p>This class provides the following functionalities:</p>
 * <ul>
 *   <li>Normalization of a string containing Hankaku Kana to Zenkaku Kana.</li>
 *   <li>Conversion of individual Hankaku Kana characters to Zenkaku Kana characters.</li>
 *   <li>Handling of voiced (ﾞ) and semi-voiced (ﾟ) sound marks to produce the correct Zenkaku Kana characters.</li>

    return user_id


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

Qui peuvent ensuite être utilisés comme ceci :

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

second_user_data = {
    "id": 4,
    "name": "Mary",
    "joined": "2018-11-30",
}

my_second_user: User = User(**second_user_data)
```

/// info

  // Test that you can not expand the readable data in a previously sliced ByteSource.
  public void testSlice_constrainedRange() throws IOException {
    long size = source.read().length;
    if (size >= 2) {
      ByteSource sliced = source.slice(1, size - 2);
      assertEquals(size - 2, sliced.read().length);
      ByteSource resliced = sliced.slice(0, size - 1);
      assertTrue(sliced.contentEquals(resliced));
    }
  }

  // Test that you can not expand the readable data in a previously sliced ByteSource.
  public void testSlice_constrainedRange() throws IOException {
    long size = source.read().length;
    if (size >= 2) {
      ByteSource sliced = source.slice(1, size - 2);
      assertEquals(size - 2, sliced.read().length);
      ByteSource resliced = sliced.slice(0, size - 1);
      assertTrue(sliced.contentEquals(resliced));
    }
  }

    return user_id


# 一个 Pydantic 模型
class User(BaseModel):
    id: int
    name: str
    joined: date
```

可以像这样来使用：

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

second_user_data = {
    "id": 4,
    "name": "Mary",
    "joined": "2018-11-30",
}

my_second_user: User = User(**second_user_data)
```


/// info | 信息

    return user_id


# 宣告一個 Pydantic model
class User(BaseModel):
    id: int
    name: str
    joined: date
```

可以像這樣來使用：

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

second_user_data = {
    "id": 4,
    "name": "Mary",
    "joined": "2018-11-30",
}

my_second_user: User = User(**second_user_data)
```

/// info

`**second_user_data` 意思是:

# Types Python avancés { #advanced-python-types }

Voici quelques idées supplémentaires qui peuvent être utiles lorsque vous travaillez avec les types Python.

## Utiliser `Union` ou `Optional` { #using-union-or-optional }

Assurez-vous d'évaluer si les **bénéfices** pour votre cas d'utilisation compensent les **inconvénients**. 🤓

///

## Bibliothèques GraphQL { #graphql-libraries }

Voici quelques bibliothèques **GraphQL** qui prennent en charge **ASGI**. Vous pouvez les utiliser avec **FastAPI** :

* [Strawberry](https://strawberry.rocks/) 🍓

* Conversion des données pour les données de réponse.
* Validation des données.
* Annotations et documentation automatiques.

## Autres types de données { #other-data-types }

Voici quelques types de données supplémentaires que vous pouvez utiliser :

* `UUID` :
    * Un « identifiant universel unique » standard, couramment utilisé comme ID dans de nombreuses bases de données et systèmes.

Historically, Gradle has shipped with some Groovy types in very prominent APIs.
This required the Kotlin DSL to add special integration to work with Groovy closures.
This has also forced plugins written in languages other than Groovy to use Groovy types for some APIs.

When the Kotlin DSL was introduced, we made an effort to add non-Groovy equivalents for all APIs.