HandleInfo info = new HandleInfo("/test/file.txt", testGuid, testFileId, HandleType.PERSISTENT, 0, testLeaseKey);

        String str = info.toString();
        assertTrue(str.contains("/test/file.txt"));
        assertTrue(str.contains("PERSISTENT"));
        assertTrue(str.contains(testGuid.toString()));
    }

If it doesn't see an `Authorization` header, or the value doesn't have a `Bearer ` token, it will respond with a 401 status code error (`UNAUTHORIZED`) directly.

You don't even have to check if the token exists to return an error. You can be sure that if your function is executed, it will have a `str` in that token.

You can try it already in the interactive docs:

## 응답 모델 인코딩 매개변수

응답 모델은 아래와 같이 기본값을 가질 수 있습니다:

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

* `description: Optional[str] = None`은 기본값으로 `None`을 갖습니다.
* `tax: float = 10.5`는 기본값으로 `10.5`를 갖습니다.
* `tags: List[str] = []` 빈 리스트의 기본값으로: `[]`.

그러나 실제로 저장되지 않았을 경우 결과에서 값을 생략하고 싶을 수 있습니다.

예를 들어, NoSQL 데이터베이스에 많은 선택적 속성이 있는 모델이 있지만, 기본값으로 가득 찬 매우 긴 JSON 응답을 보내고 싶지 않습니다.

        String str = cmd.toString();
        // toString uses Hexdump.toHexString(informationLevel, 3)
        assertTrue(str.contains(Hexdump.toHexString(level, 3)), "toString should contain hex level");
        assertTrue(str.contains("Trans2QueryFSInformation["), "should start with class name");
        assertTrue(str.contains(",informationLevel=0x"), "should contain level field marker");
    }

from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    price: float
    is_offer: Union[bool, None] = None


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: Union[str, None] = None):
    return {"item_id": item_id, "q": q}


@app.put("/items/{item_id}")

```Python hl_lines="1"
async def needy_dependency(fresh_value: Annotated[str, Depends(get_value, use_cache=False)]):
    return {"fresh_value": fresh_value}
```

////

//// tab | Python 3.8+ non-Annotated

/// tip | Dica

Utilize a versão com `Annotated` se possível.

///

```Python hl_lines="1"
async def needy_dependency(fresh_value: str = Depends(get_value, use_cache=False)):
    return {"fresh_value": fresh_value}

```Python
from datetime import date
from pydantic import BaseModel

# Оголошення змінної як str
# з підтримкою автодоповнення у редакторі
def main(user_id: str):
    return user_id

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

Приклад використання цієї моделі:

```Python

from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    price: float
    is_offer: bool = None


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: str = None):
    return {"item_id": item_id, "q": q}


@app.put("/items/{item_id}")

* Pass the internal type(s) as "type parameters" using square brackets: `[` and `]`

In Python 3.9 it would be:

```Python
my_list: list[str]
```

In versions of Python before 3.9, it would be:

```Python
from typing import List

my_list: List[str]
```

That's all standard Python syntax for type declarations.

Use that same standard syntax for model attributes with internal types.

В Python версии 3.9 это будет выглядеть так:

```Python
my_list: list[str]
```

В версиях Python до 3.9 это будет выглядеть так:

```Python
from typing import List

my_list: List[str]
```

Это всё стандартный синтаксис Python для объявления типов.

Используйте этот же стандартный синтаксис для атрибутов модели с внутренними типами.