assert response.json() == {
        "user_me": {
            "user_me": "user_me_1",
            "current_user": {
                "user": "user_1",
                "scopes": ["me"],
                "db_session": "db_session_1",
            },
        },
        "user_items": {
            "user_items": "user_items_1",
            "user_me": {
                "user_me": "user_me_2",
                "current_user": {

///

### Acerca de `**user_in.dict()` { #about-user-in-dict }

#### `.dict()` de Pydantic { #pydantics-dict }

`user_in` es un modelo Pydantic de la clase `UserIn`.

Los modelos Pydantic tienen un método `.dict()` 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="******@****.***")

def fake_save_user(user_in: UserIn):
    hashed_password = fake_password_hasher(user_in.password)
    user_in_db = UserInDB(**user_in.model_dump(), hashed_password=hashed_password)
    print("User saved! ..not really")
    return user_in_db


@app.post("/user/", response_model=UserOut)
async def create_user(user_in: UserIn):
    user_saved = fake_save_user(user_in)

def fake_save_user(user_in: UserIn):
    hashed_password = fake_password_hasher(user_in.password)
    user_in_db = UserInDB(**user_in.model_dump(), hashed_password=hashed_password)
    print("User saved! ..not really")
    return user_in_db


@app.post("/user/", response_model=UserOut)
async def create_user(user_in: UserIn):
    user_saved = fake_save_user(user_in)

### About `**user_in.model_dump()` { #about-user-in-model-dump }

#### Pydantic's `.model_dump()` { #pydantics-model-dump }

`user_in` is a Pydantic model of class `UserIn`.

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="******@****.***")

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

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

und dann aufrufen:

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

///

### Sobre `**user_in.dict()` { #about-user-in-dict }

#### O `.dict()` do Pydantic { #pydantics-dict }

`user_in` é um modelo Pydantic da classe `UserIn`.

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="******@****.***")

	} else if len(stmt.Schema.PrimaryFields) != 1 {
		t.Errorf("should have only one primary field with embedded struct, but got %v", len(stmt.Schema.PrimaryFields))
	}

	for _, name := range []string{"user_id", "user_name", "user_email"} {
		if !DB.Migrator().HasColumn(&HNPost{}, name) {
			t.Errorf("should has prefixed column %v", name)
		}
	}

	// save embedded struct
	DB.Save(&HNPost{BasePost: BasePost{Title: "news"}})

    user_in_db = UserInDB(**user_in.model_dump(), hashed_password=hashed_password)
    print("User saved! ..not really")
    return user_in_db


@app.post("/user/", response_model=UserOut)
async def create_user(user_in: UserIn):
    user_saved = fake_save_user(user_in)

///

### `**user_in.dict()` 에 대하여

#### Pydantic의 `.dict()`

`user_in`은 Pydantic 모델 클래스인 `UserIn`입니다.

Pydantic 모델은 모델 데이터를 포함한 `dict`를 반환하는 `.dict()` 메서드를 제공합니다.

따라서, 다음과 같이 Pydantic 객체 `user_in`을 생성할 수 있습니다:

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

그 다음, 다음과 같이 호출합니다: