Los modelos Pydantic tienen un método `.model_dump()` que devuelve un `dict` con los datos del modelo.

Así que, si creamos un objeto Pydantic `user_in` como:

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

y luego llamamos a:

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

У Pydantic-моделей есть метод `.model_dump()`, 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.model_dump()
```

Os modelos Pydantic possuem um método `.model_dump()` 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.model_dump()
```

    email: str | None = None
    full_name: str | None = None
    disabled: 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: str = Depends(oauth2_scheme)):
    user = fake_decode_token(token)
    return user


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

    email: str | None = None
    full_name: str | None = None
    disabled: 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")

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

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

und dann aufrufen:

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

from inline_snapshot import snapshot

if TYPE_CHECKING:  # pragma: no cover
    from collections.abc import AsyncGenerator


class DummyClient:
    async def get_people(self) -> list:
        return ["John Doe", "Jane Doe"]

    async def close(self) -> None:
        pass


async def get_client() -> AsyncGenerator[DummyClient, None]:
    client = DummyClient()
    yield client
    await client.close()

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

接著呼叫：

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

此時變數 `user_dict` 會是一個承載資料的 `dict`（也就是 `dict`，而非 Pydantic 模型物件）。

若再呼叫：

```Python
print(user_dict)
```

我們會得到一個 Python `dict`：

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

client = TestClient(app)


def test_security_http_basic():
    response = client.get("/users/me", auth=("john", "secret"))
    assert response.status_code == 200, response.text
    assert response.json() == {"username": "john", "password": "secret"}


def test_security_http_basic_no_credentials():
    response = client.get("/users/me")

						} else {
							fv, _ := ref.PrimaryKey.ValueOf(db.Statement.Context, elem)
							db.AddError(ref.ForeignKey.Set(db.Statement.Context, joinValue, fv))
						}
					}
					joins = reflect.Append(joins, joinValue)
				}

				identityMap := map[string]bool{}
				appendToElems := func(v reflect.Value) {
					if _, zero := rel.Field.ValueOf(db.Statement.Context, v); !zero {