You can declare the type of a path parameter in the function, using standard Python type annotations:

```Python hl_lines="7"
{!../../../docs_src/path_params/tutorial002.py!}
```

In this case, `item_id` is declared to be an `int`.

!!! check
    This will give you editor support inside of your function, with error checks, completion, etc.

In some cases, you might still have to use Pydantic's version of `dataclasses`. For example, if you have errors with the automatically generated API documentation.

In that case, you can simply swap the standard `dataclasses` with `pydantic.dataclasses`, which is a drop-in replacement:

```{ .python .annotate hl_lines="1  5  8-11  14-17  23-25  28" }
{!../../../docs_src/dataclasses/tutorial003.py!}
```

!!! tip
    The next sections are **not necessarily "advanced"**.

    And it's possible that for your use case, the solution is in one of them.

## Read the Tutorial first

The next sections assume you already read the main [Tutorial - User Guide: Security](../../tutorial/security/index.md){.internal-link target=_blank}.

!!! info
    A "bearer" token is not the only option.

    But it's the best one for our use case.

    And it might be the best for most use cases, unless you are an OAuth2 expert and know exactly why there's another option that suits better your needs.

    In that case, **FastAPI** also provides you with the tools to build it.

And you can use any model or data for the security requirements (in this case, a Pydantic model `User`).

But you are not restricted to using some specific data model, class or type.

Do you want to have an `id` and `email` and not have any `username` in your model? Sure. You can use these same tools.

```Python hl_lines="11  14-16"
{!../../../docs_src/sub_applications/tutorial001.py!}
```

### Mount the sub-application

In your top-level application, `app`, mount the sub-application, `subapi`.

In this case, it will be mounted at the path `/subapi`:

```Python hl_lines="11  19"
{!../../../docs_src/sub_applications/tutorial001.py!}
```

### Check the automatic API docs

{!../../../docs_src/async_sql_databases/tutorial001.py!}
```

!!! 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.

大部分标准请求头用**连字符**分隔，即**减号**（`-`）。

但是 `user-agent` 这样的变量在 Python 中是无效的。

因此，默认情况下，`Header` 把参数名中的字符由下划线（`_`）改为连字符（`-`）来提取并存档请求头 。

同时，HTTP 的请求头不区分大小写，可以使用 Python 标准样式（即 **snake_case**）进行声明。

因此，可以像在 Python 代码中一样使用 `user_agent` ，无需把首字母大写为 `User_Agent` 等形式。

如需禁用下划线自动转换为连字符，可以把 `Header` 的 `convert_underscores` 参数设置为 `False`：

=== "Python 3.10+"

    ```Python hl_lines="10"

Here I'm also highlighting contributions from sponsors.

    {!> ../../../docs_src/extra_models/tutorial005.py!}
    ```

## Recap

Use multiple Pydantic models and inherit freely for each case.