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")

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)

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)

        DefaultToolchain toolchain = newDefaultToolchain(model);
        toolchain.addProvideToken("name", RequirementMatcherFactory.createExactMatcher("Jane Doe"));

        assertFalse(toolchain.matchesRequirements(Collections.singletonMap("name", "John Doe")));
        verify(logger).debug("Toolchain {} doesn't match required property: {}", toolchain, "name");
    }

    @Test
    void testEquals() {

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)

```

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',
    'email': 'john.doe@example.com',
    'full_name': None,
}
```

Pydantic-Modelle haben eine `.dict()`-Methode, die ein `dict` mit den Daten des Modells zurückgibt.

Wenn wir also ein Pydantic-Objekt `user_in` erstellen, etwa so:

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

und wir rufen seine `.dict()`-Methode auf:

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

`user_in`は`UserIn`クラスのPydanticモデルです。

Pydanticモデルには、モデルのデータを含む`dict`を返す`.dict()`メソッドがあります。

そこで、以下のようなPydanticオブジェクト`user_in`を作成すると:

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

そして呼び出すと:

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

これで変数`user_dict`のデータを持つ`dict`ができました。（これはPydanticモデルのオブジェクトの代わりに`dict`です）。

そして呼び出すと:

```Python
print(user_dict)

from fastapi.security import OAuth2PasswordBearer, OAuth2PasswordRequestForm
from pydantic import BaseModel

fake_users_db = {
    "johndoe": {
        "username": "johndoe",
        "full_name": "John Doe",
        "email": "******@****.***",
        "hashed_password": "fakehashedsecret",
        "disabled": False,
    },
    "alice": {
        "username": "alice",
        "full_name": "Alice Wonderson",

## Мотивация

Давайте начнем с простого примера:

```Python
{!../../../docs_src/python_types/tutorial001.py!}
```

Вызов этой программы выводит:

```
John Doe
```

Функция делает следующее:

* Принимает `first_name` и `last_name`.
* Преобразует первую букву содержимого каждой переменной в верхний регистр с `title()`.