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

就能以如下方式调用：

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

现在，变量 `user_dict`中的就是包含数据的**字典**（变量 `user_dict` 是字典，不是 Pydantic 模型对象）。

以如下方式调用：

```Python
print(user_dict)
```

输出的就是 Python **字典**：

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

    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")

    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],
    }

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)

from pydantic import BaseModel


class User(BaseModel):
    id: int
    name: str = "John Doe"
    signup_ts: Union[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)

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

& ⤴️ 👥 🤙:

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

👥 🔜 ✔️ `dict` ⏮️ 💽 🔢 `user_dict` (⚫️ `dict` ↩️ Pydantic 🏷 🎚).

& 🚥 👥 🤙:

```Python
print(user_dict)
```

👥 🔜 🤚 🐍 `dict` ⏮️:

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

    full_name: Union[str, None] = None
    disabled: Union[bool, None] = None


def fake_decode_token(token):
    return User(
        username=token + "fakedecoded", email="john@example.com", full_name="John Doe"
    )


async def get_current_user(token: Annotated[str, Depends(oauth2_scheme)]):
    user = fake_decode_token(token)
    return user


@app.get("/users/me")

from pydantic import BaseModel


class User(BaseModel):
    id: int
    name: str = "John Doe"
    signup_ts: Union[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)

У Pydantic-моделей есть метод `.dict()`, который возвращает `dict` с данными модели.

Поэтому, если мы создадим Pydantic-объект `user_in` таким способом:

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

и затем вызовем:

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

Os modelos Pydantic possuem um método `.dict()` que retorna um `dict` com os dados do modelo.

Então, se criarmos um objeto Pydantic `user_in` como:

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

e depois chamarmos:

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