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

seria equivalente a:

```Python
UserInDB(**user_in.dict())
```

...porque `user_in.dict()` é um `dict`, e depois fazemos o Python "desembrulhá-lo" passando-o para UserInDB precedido por `**`.

Então, obtemos um modelo Pydantic a partir dos dados em outro modelo Pydantic.

& 🚥 👥 🤙:

```Python
print(user_dict)
```

👥 🔜 🤚 🐍 `dict` ⏮️:

```Python
{
    'username': 'john',
    'password': 'secret',
    'email': '******@****.***',
    'full_name': None,
}
```

#### 🎁 `dict`

🚥 👥 ✊ `dict` 💖 `user_dict` & 🚶‍♀️ ⚫️ 🔢 (⚖️ 🎓) ⏮️ `**user_dict`, 🐍 🔜 "🎁" ⚫️. ⚫️ 🔜 🚶‍♀️ 🔑 & 💲 `user_dict` 🔗 🔑-💲 ❌.

    email = user_dict["email"],
    full_name = user_dict["full_name"],
)
```

#### A Pydantic model from the contents of another

As in the example above we got `user_dict` from `user_in.dict()`, this code:

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

would be equivalent to:

```Python
UserInDB(**user_in.dict())
```

Sie können mit dem Parameter `openapi_tags` auch zusätzliche Metadaten für die verschiedenen Tags hinzufügen, die zum Gruppieren Ihrer Pfadoperationen verwendet werden.

Es wird eine Liste benötigt, die für jedes Tag ein Dict enthält.

Jedes Dict kann Folgendes enthalten:

* `name` (**erforderlich**): ein `str` mit demselben Tag-Namen, den Sie im Parameter `tags` in Ihren *Pfadoperationen* und `APIRouter`n verwenden.

👆 💪 ⚙️ `jsonable_encoder` 👈.

⚫️ 📨 🎚, 💖 Pydantic 🏷, & 📨 🎻 🔗 ⏬:

=== "🐍 3️⃣.6️⃣ & 🔛"

    ```Python hl_lines="5  22"
    {!> ../../../docs_src/encoder/tutorial001.py!}
    ```

=== "🐍 3️⃣.1️⃣0️⃣ & 🔛"

    ```Python hl_lines="4  21"
    {!> ../../../docs_src/encoder/tutorial001_py310.py!}
    ```

👉 🖼, ⚫️ 🔜 🗜 Pydantic 🏷 `dict`, & `datetime` `str`.

### `Body` com vários `examples`

Alternativamente ao único `example`, você pode passar `examples` usando um `dict` com **vários examples**, cada um com informações extras que serão adicionadas no **OpenAPI** também.

As chaves do `dict` identificam cada exemplo, e cada valor é outro `dict`.

Cada `dict` de exemplo específico em `examples` pode conter:

* `summary`: Pequena descrição do exemplo.

By default, **FastAPI** would automatically convert that return value to JSON using the `jsonable_encoder` explained in [JSON Compatible Encoder](../tutorial/encoder.md){.internal-link target=_blank}.

### About `{**note.dict(), "id": last_record_id}`

`note` is a Pydantic `Note` object.

`note.dict()` returns a `dict` with its data, something like:

```Python
{
    "text": "Some note",
    "completed": False,
}
```

but it doesn't have the `id` field.

So we create a new `dict`, that contains the key-value pairs from `note.dict()` with:

```Python
{**note.dict()}

# 📨 📨 🔗

🕐❔ 👆 ✍ **FastAPI** *➡ 🛠️* 👆 💪 🛎 📨 🙆 📊 ⚪️➡️ ⚫️: `dict`, `list`, Pydantic 🏷, 💽 🏷, ♒️.

🔢, **FastAPI** 🔜 🔁 🗜 👈 📨 💲 🎻 ⚙️ `jsonable_encoder` 🔬 [🎻 🔗 🔢](../tutorial/encoder.md){.internal-link target=_blank}.

⤴️, ⛅ 🎑, ⚫️ 🔜 🚮 👈 🎻-🔗 💽 (✅ `dict`) 🔘 `JSONResponse` 👈 🔜 ⚙️ 📨 📨 👩‍💻.

✋️ 👆 💪 📨 `JSONResponse` 🔗 ⚪️➡️ 👆 *➡ 🛠️*.

⚫️ 💪 ⚠, 🖼, 📨 🛃 🎚 ⚖️ 🍪.

## 📨 `Response`

## 联合预定义响应和自定义响应

您可能希望有一些应用于许多路径操作的预定义响应，但是你想将不同的路径和自定义的相应组合在一块。
对于这些情况，你可以使用Python的技术，将 `dict` 与 `**dict_to_unpack` 解包：
```Python
old_dict = {
    "old key": "old value",
    "second old key": "second old value",
}
new_dict = {**old_dict, "new key": "new value"}
```

这里， new_dict 将包含来自 old_dict 的所有键值对加上新的键值对：
```python
{
    "old key": "old value",
    "second old key": "second old value",