## Use Case

Let's start with an example **use case** and then see how to solve it with this.

Let's imagine that you have some **machine learning models** that you want to use to handle requests. 🤖

!!! tip
    If you want something quick to copy and paste, don't use this example, use the last one below.

Then, when you create an instance of that `Settings` class (in this case, in the `settings` object), Pydantic will read the environment variables in a case-insensitive way, so, an upper-case variable `APP_NAME` will still be read for the attribute `app_name`.

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`.

So, a Python class is also a **callable**.

Then, in **FastAPI**, you could use a Python class as a dependency.

To create the `SessionLocal` class, use the function `sessionmaker`:

```Python hl_lines="11"
{!../../../docs_src/sql_databases/sql_app/database.py!}
```

### Create a `Base` class

Now we will use the function `declarative_base()` that returns a class.

Later we will inherit from this class to create each of the database models or classes (the ORM models):

```Python hl_lines="13"

The contents that you return from your *path operation function* will be put inside of that `Response`.

And if that `Response` has a JSON media type (`application/json`), like is the case with the `JSONResponse` and `UJSONResponse`, the data you return will be automatically converted (and filtered) with any Pydantic `response_model` that you declared in the *path operation decorator*.

!!! note

    Any HTTP (error) status code 401 "UNAUTHORIZED" is supposed to also return a `WWW-Authenticate` header.

    In the case of bearer tokens (our case), the value of that header should be `Bearer`.

    You can actually skip that extra header and it would still work.

    But it's provided here to be compliant with the specifications.

    * As the `q` parameter is declared with `= None`, it is optional.
    * Without the `None` it would be required (as is the body in the case with `PUT`).
* For `PUT` requests to `/items/{item_id}`, Read the body as JSON:
    * Check that it has a required attribute `name` that should be a `str`.

        ```

    * So, the colon in `main:app` would be equivalent to the Python `import` part in `from main import app`.

* `--workers`: The number of worker processes to use, each will run a Uvicorn worker, in this case, 4 workers.

* `--worker-class`: The Gunicorn-compatible worker class to use in the worker processes.
    * Here we pass the class that Gunicorn can import and use with:

        ```Python

In this case, if you had **multiple containers**, by default, when Prometheus came to **read the metrics**, it would get the ones for **a single container each time** (for the container that handled that particular request), instead of getting the **accumulated metrics** for all the replicated containers.

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

## Recap

Use multiple Pydantic models and inherit freely for each case.