The same as when declaring query parameters, when a model attribute has a default value, it is not required. Otherwise, it is required. Use `None` to make it just optional.

For example, this model above declares a JSON "`object`" (or Python `dict`) like:

```JSON
{
    "name": "Foo",
    "description": "An optional description",
    "price": 45.2,
    "tax": 3.5
}
```

**Swagger UI** использует файлы:

* <a href="https://cdn.jsdelivr.net/npm/swagger-ui-dist@5/swagger-ui-bundle.js" class="external-link" target="_blank">`swagger-ui-bundle.js`</a>
* <a href="https://cdn.jsdelivr.net/npm/swagger-ui-dist@5/swagger-ui.css" class="external-link" target="_blank">`swagger-ui.css`</a>

А **ReDoc** использует файл:

    user = get_user(fake_db, username)
    if not user:
        return False
    if not verify_password(password, user.hashed_password):
        return False
    return user


def create_access_token(data: dict, expires_delta: timedelta | None = None):
    to_encode = data.copy()
    if expires_delta:
        expire = datetime.now(timezone.utc) + expires_delta
    else:

    user = get_user(fake_db, username)
    if not user:
        return False
    if not verify_password(password, user.hashed_password):
        return False
    return user


def create_access_token(data: dict, expires_delta: Union[timedelta, None] = None):
    to_encode = data.copy()
    if expires_delta:
        expire = datetime.now(timezone.utc) + expires_delta
    else:

    class Cat(BaseModel):
        pet_type: str = "cat"
        meows: int

    class Dog(BaseModel):
        pet_type: str = "dog"
        barks: float

    def get_pet_type(v):
        assert isinstance(v, dict)
        return v.get("pet_type", "")

    Pet = Annotated[
        Union[Annotated[Cat, Tag("cat")], Annotated[Dog, Tag("dog")]],
        Discriminator(get_pet_type),
    ]

    app = FastAPI()

    user = get_user(fake_db, username)
    if not user:
        return False
    if not verify_password(password, user.hashed_password):
        return False
    return user


def create_access_token(data: dict, expires_delta: timedelta | None = None):
    to_encode = data.copy()
    if expires_delta:
        expire = datetime.now(timezone.utc) + expires_delta
    else:

    user = get_user(fake_db, username)
    if not user:
        return False
    if not verify_password(password, user.hashed_password):
        return False
    return user


def create_access_token(data: dict, expires_delta: Union[timedelta, None] = None):
    to_encode = data.copy()
    if expires_delta:
        expire = datetime.now(timezone.utc) + expires_delta
    else:

Y luego puedes establecer cookies en ese objeto de response *temporal*.

{* ../../docs_src/response_cookies/tutorial002_py39.py hl[1, 8:9] *}

Y entonces puedes devolver cualquier objeto que necesites, como normalmente lo harías (un `dict`, un modelo de base de datos, etc).

Y si declaraste un `response_model`, todavía se utilizará para filtrar y convertir el objeto que devolviste.

python_init_rules()

load("@local_xla//third_party/py:python_init_repositories.bzl", "python_init_repositories")

python_init_repositories(
    default_python_version = "system",
    local_wheel_dist_folder = "dist",
    local_wheel_inclusion_list = [
        "tensorflow*",
        "tf_nightly*",
    ],
    local_wheel_workspaces = ["//:WORKSPACE"],
    requirements = {
        "3.9": "//:requirements_lock_3_9.txt",

/// info | Информация

В Pydantic версии 1 конфигурация задавалась во внутреннем классе `Config`, в Pydantic версии 2 — в атрибуте `model_config`. Этот атрибут принимает `dict`, и чтобы получить автозавершение и ошибки «на лету», вы можете импортировать и использовать `SettingsConfigDict` для описания этого `dict`.

///