["username", "email", "full_name"],
                        # TODO: remove when deprecating Pydantic v1
                        ["username", "email"],
                    ),
                    "type": "object",
                    "properties": {
                        "username": {"title": "Username", "type": "string"},
                        "email": {
                            "title": "Email",

                    "required": ["username", "email", "password"],
                    "type": "object",
                    "properties": {
                        "username": {"title": "Username", "type": "string"},
                        "email": {
                            "title": "Email",
                            "type": "string",
                            "format": "email",
                        },

```

就会生成如下结果：

```Python
UserInDB(
    username="john",
    password="secret",
    email="******@****.***",
    full_name=None,
)
```

或更精准，直接把可能会用到的内容与 `user_dict` 一起使用：

```Python
UserInDB(
    username = user_dict["username"],
    password = user_dict["password"],
    email = user_dict["email"],
    full_name = user_dict["full_name"],
)
```

#### 用其它模型中的内容生成 Pydantic 模型

    return config.Settings()


@app.get("/info")
async def info(settings: Annotated[config.Settings, Depends(get_settings)]):
    return {
        "app_name": settings.app_name,
        "admin_email": settings.admin_email,
        "items_per_user": settings.items_per_user,

class Settings(BaseSettings):
    app_name: str = "Awesome API"
    admin_email: str
    items_per_user: int = 50


settings = Settings()
app = FastAPI()


@app.get("/info")
async def info():
    return {
        "app_name": settings.app_name,
        "admin_email": settings.admin_email,
        "items_per_user": settings.items_per_user,

    HostAndPort hp = HostAndPort.fromParts("gmail.com", 81);
    assertEquals("gmail.com", hp.getHost());
    assertTrue(hp.hasPort());
    assertEquals(81, hp.getPort());

    try {
      HostAndPort.fromParts("gmail.com:80", 81);
      fail("Expected IllegalArgumentException");
    } catch (IllegalArgumentException expected) {
    }

    try {
      HostAndPort.fromParts("gmail.com", -1);

@needs_pydanticv1
def test_settings(monkeypatch: MonkeyPatch):
    monkeypatch.setenv("ADMIN_EMAIL", "******@****.***")
    from docs_src.settings.tutorial001_pv1 import app

    client = TestClient(app)
    response = client.get("/info")
    assert response.status_code == 200, response.text
    assert response.json() == {
        "app_name": "Awesome API",
        "admin_email": "******@****.***",
        "items_per_user": 50,

```Python
UserInDB(
    username="john",
    password="secret",
    email="******@****.***",
    full_name=None,
)
```

Oder, präziser, `user_dict` wird direkt verwendet, welche Werte es auch immer haben mag:

```Python
UserInDB(
    username = user_dict["username"],
    password = user_dict["password"],
    email = user_dict["email"],
    full_name = user_dict["full_name"],
)
```

	}
}

// RMetricName - name of replication metric
type RMetricName string

const (
	// Large - objects larger than 128MiB
	Large RMetricName = "Large"
	// Small - objects smaller than 128MiB
	Small RMetricName = "Small"
	// Total - metric pertaining to totals
	Total RMetricName = "Total"
)

// ReplQNodeStats holds queue stats for replication per node
type ReplQNodeStats struct {

                "User": {
                    "title": "User",
                    "required": ["username", "email"],
                    "type": "object",
                    "properties": {
                        "username": {"title": "Username", "type": "string"},
                        "email": {"title": "Email", "type": "string"},
                    },
                },
                "ValidationError": {