If it was in a type annotation we could have used the vertical bar, as:

```Python
some_variable: PlaneItem | CarItem
```

But if we put that in `response_model=PlaneItem | CarItem` we would get an error, because Python would try to perform an **invalid operation** between `PlaneItem` and `CarItem` instead of interpreting that as a type annotation.

* **Uvicorn**:
    * Will have the best performance, as it doesn't have much extra code apart from the server itself.
    * You wouldn't write an application in Uvicorn directly. That would mean that your code would have to include more or less, at least, all the code provided by Starlette (or **FastAPI**). And if you did that, your final application would have the same overhead as having used a framework and minimizing your app code and bugs.

    `OAuth2PasswordBearer` makes **FastAPI** know that it is a security scheme. So it is added that way to OpenAPI.

    But `OAuth2PasswordRequestForm` is just a class dependency that you could have written yourself, or you could have declared `Form` parameters directly.

    But as it's a common use case, it is provided by **FastAPI** directly, just to make it easier.

### Use the form data

!!! tip

<img src="/img/tutorial/extending-openapi/image03.png">

## Change the Theme

The same way you could set the syntax highlighting theme with the key `"syntaxHighlight.theme"` (notice that it has a dot in the middle):

```Python hl_lines="3"
{!../../../docs_src/configure_swagger_ui/tutorial002.py!}
```

That configuration would change the syntax highlighting color theme:

<img src="/img/tutorial/extending-openapi/image04.png">

You could load it at the top level of the module/file, but that would also mean that it would **load the model** even if you are just running a simple automated test, then that test would be **slow** because it would have to wait for the model to load before being able to run an independent part of the code.

For example, you could want to **return a dictionary** or a database object, but **declare it as a Pydantic model**. This way the Pydantic model would do all the data documentation, validation, etc. for the object that you returned (e.g. a dictionary or database object).

│   └── main.py
```

In the file `main.py` you have your **FastAPI** app:


```Python
{!../../../docs_src/app_testing/main.py!}
```

### Testing file

Then you could have a file `test_main.py` with your tests. It could live on the same Python package (the same directory with a `__init__.py` file):

``` hl_lines="5"
.
├── app
│   ├── __init__.py
│   ├── main.py
│   └── test_main.py
```

    In Pydantic version 1, you would use an internal class `Config` and `schema_extra`, as described in <a href="https://docs.pydantic.dev/1.10/usage/schema/#schema-customization" class="external-link" target="_blank">Pydantic's docs: Schema customization</a>.

    You can set `schema_extra` with a `dict` containing any additional data you would like to show up in the generated JSON Schema, including `examples`.

!!! tip

That way, the sub-application will know to use that path prefix for the docs UI.

And the sub-application could also have its own mounted sub-applications and everything would work correctly, because FastAPI handles all these `root_path`s automatically.

```

!!! tip
    If you were connecting to a different database (e.g. PostgreSQL), you would need to change the `DATABASE_URL`.

## Create the tables

In this case, we are creating the tables in the same Python file, but in production, you would probably want to create them with Alembic, integrated with migrations, etc.

Here, this section would run directly, right before starting your **FastAPI** application.

* Create an `engine`.