operation_ids: set[str],
    model_name_map: ModelNameMap,
    field_mapping: dict[
        tuple[ModelField, Literal["validation", "serialization"]], dict[str, Any]
    ],
    separate_input_output_schemas: bool = True,
) -> tuple[dict[str, Any], dict[str, Any], dict[str, Any]]:
    path = {}
    security_schemes: dict[str, Any] = {}
    definitions: dict[str, Any] = {}
    assert route.methods is not None, "Methods must be a list"

import org.codelibs.fess.app.web.CrudMode;
import org.codelibs.fess.app.web.admin.dict.kuromoji.UploadForm;
import org.codelibs.fess.app.web.api.ApiResult;
import org.codelibs.fess.app.web.api.admin.FessApiAdminAction;
import org.codelibs.fess.dict.kuromoji.KuromojiFile;
import org.codelibs.fess.dict.kuromoji.KuromojiItem;
import org.lastaflute.web.Execute;
import org.lastaflute.web.response.JsonResponse;

import org.codelibs.fess.app.web.CrudMode;
import org.codelibs.fess.app.web.admin.dict.protwords.UploadForm;
import org.codelibs.fess.app.web.api.ApiResult;
import org.codelibs.fess.app.web.api.admin.FessApiAdminAction;
import org.codelibs.fess.dict.protwords.ProtwordsFile;
import org.codelibs.fess.dict.protwords.ProtwordsItem;
import org.lastaflute.web.Execute;
import org.lastaflute.web.response.JsonResponse;

    pass


class OtherDependencyError(Exception):
    pass


async def asyncgen_state(state: dict[str, str] = Depends(get_state)):
    state["/async"] = "asyncgen started"
    yield state["/async"]
    state["/async"] = "asyncgen completed"


def generator_state(state: dict[str, str] = Depends(get_state)):
    state["/sync"] = "generator started"
    yield state["/sync"]

```

#### 別のモデルからつくるPydanticモデル

上述の例では`user_in.dict()`から`user_dict`をこのコードのように取得していますが:

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

これは以下と同等です:

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

...なぜなら`user_in.dict()`は`dict`であり、`**`を付与して`UserInDB`を渡してPythonに「展開」させているからです。

そこで、別のPydanticモデルのデータからPydanticモデルを取得します。

#### `dict`の展開と追加引数

En Pydantic v1 el método se llamaba `.dict()`, fue deprecado (pero aún soportado) en Pydantic v2, y renombrado a `.model_dump()`.

Los ejemplos aquí usan `.dict()` para compatibilidad con Pydantic v1, pero deberías usar `.model_dump()` en su lugar si puedes usar Pydantic v2.

///

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

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

Und wenn wir aufrufen:

```Python
print(user_dict)
```

würden wir ein Python-`dict` erhalten mit:

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

#### Ein `dict` entpacken { #unpacking-a-dict }

## Response with arbitrary `dict` { #response-with-arbitrary-dict }

You can also declare a response using a plain arbitrary `dict`, declaring just the type of the keys and values, without using a Pydantic model.

This is useful if you don't know the valid field/attribute names (that would be needed for a Pydantic model) beforehand.

In this case, you can use `typing.Dict` (or just `dict` in Python 3.9 and above):

    @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(