* `alias_priority`
    * `validation_alias`
    * `serialization_alias`
    * `discriminator`
    * `strict`
    * `multiple_of`
    * `allow_inf_nan`
    * `max_digits`
    * `decimal_places`
    * `json_schema_extra`

...you can read about them in the Pydantic docs.

* The parameter `regex` has been deprecated and replaced by `pattern`.

## Multiple models

Here's a general idea of how the models could look like with their password fields and the places where they are used:

=== "Python 3.10+"

    ```Python hl_lines="7  9  14  20  22  27-28  31-33  38-39"
    {!> ../../../docs_src/extra_models/tutorial001_py310.py!}
    ```

=== "Python 3.8+"

```Python
commons: Annotated[dict, Depends(common_parameters)]
```

But because we are using `Annotated`, we can store that `Annotated` value in a variable and use it in multiple places:

=== "Python 3.10+"

    ```Python hl_lines="12  16  21"
    {!> ../../../docs_src/dependencies/tutorial001_02_an_py310.py!}
    ```

=== "Python 3.9+"

    ```Python hl_lines="14  18  23"

Hier definieren wir die Konfiguration `env_file` innerhalb Ihrer Pydantic-`Settings`-Klasse und setzen den Wert auf den Dateinamen mit der dotenv-Datei, die wir verwenden möchten.

### Die `Settings` nur einmal laden mittels `lru_cache`

The **default** value of the **function parameter** is the **actual default** value, that's more intuitive with Python in general. 😌

* Datenvalidierung
* Automatische Dokumentation

## Defaultwerte

Da Query-Parameter nicht ein festgelegter Teil des Pfades sind, können sie optional sein und Defaultwerte haben.

Im obigen Beispiel haben sie die Defaultwerte `skip=0` und `limit=10`.

Wenn Sie also zur URL:

```
http://127.0.0.1:8000/items/
```

In this case, it requires the scope `me` (it could require more than one scope).

!!! note
    You don't necessarily need to add different scopes in different places.

    We are doing it here to demonstrate how **FastAPI** handles scopes declared at different levels.

=== "Python 3.10+"

    ```Python hl_lines="4  139  170"

    * Porque las estructuras de datos de Pydantic son solo <abbr title='En español: ejemplares. Aunque a veces los llaman incorrectamente "instancias"'>instances</abbr> de clases que tu defines, el auto-completado, el linting, mypy y tu intuición deberían funcionar bien con tus datos validados.
* Valida **estructuras complejas**:

## Kontextmanager

### Was sind „Kontextmanager“

„Kontextmanager“ (Englisch „Context Manager“) sind bestimmte Python-Objekte, die Sie in einer `with`-Anweisung verwenden können.

> You have to clean a big, dirty house.

*Yep, that's the whole story*.

---

There's no waiting 🕙 anywhere, just a lot of work to be done, on multiple places of the house.

You could have turns as in the burgers example, first the living room, then the kitchen, but as you are not waiting 🕙 for anything, just cleaning and cleaning, the turns wouldn't affect anything.