```

It is not encrypted, so, anyone could recover the information from the contents.

But it's signed. So, when you receive a token that you emitted, you can verify that you actually emitted it.

That way, you can create a token with an expiration of, let's say, 1 week. And then when the user comes back the next day with the token, you know that user is still logged in to your system.

Next it will convert and validate the data. So, when you use that `settings` object, you will have data of the types you declared (e.g. `items_per_user` will be an `int`).

### Use the `settings`

Then you can use the new `settings` object in your application:

```Python hl_lines="18-20"
{!../../../docs_src/settings/tutorial001.py!}
```

### Run the server

This is an advanced usage that you might not really need, but it's there in case you do.

## Include an `APIRouter` in another

The same way you can include an `APIRouter` in a `FastAPI` application, you can include an `APIRouter` in another `APIRouter` using:

```Python
router.include_router(other_router)
```

**FastAPI** doesn't require you to use a SQL (relational) database.

But you can use any relational database that you want.

Here we'll see an example using <a href="https://www.sqlalchemy.org/" class="external-link" target="_blank">SQLAlchemy</a>.

You can easily adapt it to any database supported by SQLAlchemy, like:

* PostgreSQL
* MySQL
* SQLite

   * following conditions:
   *
   * <ul>
   *   <li>You want to assign the same fraction of inputs to each bucket.
   *   <li>When you reduce the number of buckets, you can accept that the most recently added
   *       buckets will be removed first. More concretely, if you are dividing traffic among tasks,
   *       you can decrease the number of tasks from 15 and 10, killing off the final 5 tasks, and

* <a href="https://github.com/esnme/ultrajson" target="_blank"><code>ujson</code></a> - Required if you want to use `UJSONResponse`.
* `fastapi-cli` - to provide the `fastapi` command.

When you install `fastapi` it comes these standard dependencies.

## `fastapi-slim`

## Classes as dependencies

You might notice that to create an instance of a Python class, you use that same syntax.

For example:

```Python
class Cat:
    def __init__(self, name: str):
        self.name = name


fluffy = Cat(name="Mr Fluffy")
```

In this case, `fluffy` is an instance of the class `Cat`.

And to create `fluffy`, you are "calling" `Cat`.

And it shows their true commitment to FastAPI and its **community** (you), as they not only want to provide you a **good service** but also want to make sure you have a **good and healthy framework**, FastAPI. 🙇

For example, you might want to try <a href="https://speakeasyapi.dev/?utm_source=fastapi+repo&utm_medium=github+sponsorship" class="external-link" target="_blank">Speakeasy</a>.

    That way you tell the editor that you are intentionally returning anything. But FastAPI will still do the data documentation, validation, filtering, etc. with the `response_model`.

### `response_model` Priority

If you declare both a return type and a `response_model`, the `response_model` will take priority and be used by FastAPI.

            the output should use the alias names (when provided) or the Python
            attribute names. In an API, if you set an alias, it's probably because you
            want to use it in the result, so you probably want to leave this set to
            `True`.
            """
        ),
    ] = True,
    exclude_unset: Annotated[
        bool,
        Doc(