pkg bytes, func IndexAny([]uint8, string) int
pkg bytes, func IndexByte([]uint8, uint8) int
pkg bytes, func IndexFunc([]uint8, func(int32) bool) int
pkg bytes, func IndexRune([]uint8, int32) int
pkg bytes, func Join([][]uint8, []uint8) []uint8
pkg bytes, func LastIndex([]uint8, []uint8) int
pkg bytes, func LastIndexAny([]uint8, string) int
pkg bytes, func LastIndexFunc([]uint8, func(int32) bool) int

    return User(name="John", surname="Doe")


@app.get("/no_response_model-no_annotation-return_dict")
def no_response_model_no_annotation_return_dict():
    return {"name": "John", "surname": "Doe"}


@app.get("/response_model-no_annotation-return_same_model", response_model=User)
def response_model_no_annotation_return_same_model():
    return User(name="John", surname="Doe")

Моделі Pydantic мають метод `.model_dump()`, який повертає `dict` з даними моделі.

Отже, якщо ми створимо об’єкт Pydantic `user_in` так:

```Python
user_in = UserIn(username="john", password="secret", email="john******@****.***")
```

і викличемо:

```Python
user_dict = user_in.model_dump()
```

тепер ми маємо `dict` з даними у змінній `user_dict` (це `dict`, а не об’єкт моделі Pydantic).

    response = client.get("/user")
    assert response.json() == {
        "user": "john",
        "scopes": ["foo", "bar"],
        "data": [1, 2, 3],
    }


def test_override_data():
    app.dependency_overrides[get_data] = get_data_override
    response = client.get("/user")
    assert response.json() == {
        "user": "john",
        "scopes": ["foo", "bar"],
        "data": [3, 4, 5],
    }

Pydantic models have a `.model_dump()` method that returns a `dict` with the model's data.

So, if we create a Pydantic object `user_in` like:

```Python
user_in = UserIn(username="john", password="secret", email="john******@****.***")
```

and then we call:

```Python
user_dict = user_in.model_dump()
```

from pydantic import BaseModel


class User(BaseModel):
    id: int
    name: str = "John Doe"
    signup_ts: datetime | None = None
    friends: list[int] = []


external_data = {
    "id": "123",
    "signup_ts": "2017-06-01 12:22",
    "friends": [1, "2", b"3"],
}
user = User(**external_data)
print(user)
# > User id=123 name='John Doe' signup_ts=datetime.datetime(2017, 6, 1, 12, 22) friends=[1, 2, 3]
print(user.id)

Les modèles Pydantic ont une méthode `.model_dump()` qui renvoie un `dict` avec les données du modèle.

Ainsi, si nous créons un objet Pydantic `user_in` comme :

```Python
user_in = UserIn(username="john", password="secret", email="john******@****.***")
```

et que nous appelons ensuite :

```Python
user_dict = user_in.model_dump()
```

            "user": {"username": "johndoe", "full_name": "John Doe"},
        },
    )
    assert response.status_code == 200
    assert response.json() == {
        "item_id": 5,
        "item": {
            "name": "Foo",
            "price": 50.5,
            "description": "Some Foo",
            "tax": 0.1,
        },
        "user": {"username": "johndoe", "full_name": "John Doe"},
    }

Pydantic modellerinde, model verilerini içeren bir `dict` döndüren `.model_dump()` metodu bulunur.

Yani, şöyle bir Pydantic nesnesi `user_in` oluşturursak:

```Python
user_in = UserIn(username="john", password="secret", email="john******@****.***")
```

ve sonra şunu çağırırsak:

```Python
user_dict = user_in.model_dump()
```

```Python
user_in = UserIn(username="john", password="secret", email="john******@****.***")
```

就能以如下方式调用：

```Python
user_dict = user_in.model_dump()
```

现在，变量 `user_dict` 中的是包含数据的 `dict`（它是 `dict`，不是 Pydantic 模型对象）。

以如下方式调用：

```Python
print(user_dict)
```

输出的就是 Python `dict`：

```Python
{
    'username': 'john',
    'password': 'secret',