🖼 🔛 👥 🤚 `user_dict` ⚪️➡️ `user_in.dict()`, 👉 📟:

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

🔜 🌓:

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

...↩️ `user_in.dict()` `dict`, & ⤴️ 👥 ⚒ 🐍 "🎁" ⚫️ 🚶‍♀️ ⚫️ `UserInDB` 🔠 ⏮️ `**`.

, 👥 🤚 Pydantic 🏷 ⚪️➡️ 💽 ➕1️⃣ Pydantic 🏷.

#### 🎁 `dict` & ➕ 🇨🇻

#### 用其它模型中的内容生成 Pydantic 模型

上例中 ，从 `user_in.dict()` 中得到了 `user_dict`，下面的代码：

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

等效于：

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

……因为 `user_in.dict()` 是字典，在传递给 `UserInDB` 时，把 `**` 加在  `user_in.dict()` 前，可以让 Python 进行**解包**。

这样，就可以用其它 Pydantic 模型中的数据生成 Pydantic 模型。

#### 解包 `dict` 和更多关键字

So, if we create a Pydantic object `user_in` like:

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

and then we call:

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

we now have a `dict` with the data in the variable `user_dict` (it's a `dict` instead of a Pydantic model object).

And if we call:

```Python

    @asynccontextmanager
    async def lifespan(app: FastAPI) -> AsyncGenerator[Dict[str, bool], None]:
        state.app_startup = True
        yield {"app": True}
        state.app_shutdown = True

    @asynccontextmanager
    async def router_lifespan(app: FastAPI) -> AsyncGenerator[Dict[str, bool], None]:
        state.router_startup = True
        yield {"router": True}

                "although still supported. Use examples instead."
            ),
        ] = _Unset,
        openapi_examples: Optional[Dict[str, Example]] = None,
        deprecated: Union[deprecated, str, bool, None] = None,
        include_in_schema: bool = True,
        json_schema_extra: Union[Dict[str, Any], None] = None,
        **extra: Any,
    ):
        if example is not _Unset:
            warnings.warn(

    if license_info:
        info["license"] = license_info
    output: Dict[str, Any] = {"openapi": openapi_version, "info": info}
    if servers:
        output["servers"] = servers
    components: Dict[str, Dict[str, Any]] = {}
    paths: Dict[str, Dict[str, Any]] = {}
    webhook_paths: Dict[str, Dict[str, Any]] = {}
    operation_ids: Set[str] = set()

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

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

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

#### Pydantic-модель из содержимого другой модели

Как в примере выше мы получили `user_dict` из `user_in.dict()`, этот код:

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

будет равнозначен такому:

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

...потому что `user_in.dict()` - это `dict`, и затем мы указываем, чтобы Python его "распаковал", когда передаём его в `UserInDB` и ставим перед ним `**`.

Como no exemplo acima, obtivemos o `user_dict` a partir do `user_in.dict()`, este código:

```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 `**`.

# input the branch name and paste the cherry-pick commit and click Run. A PR
# will be created.

name: Release Branch Cherrypick
on:
  workflow_dispatch:
    inputs:
      # We use this instead of the "run on branch" argument because GitHub looks
      # on that branch for a workflow.yml file, and we'd have to cherry-pick
      # this file into those branches.
      release_branch: