* So, by using FastAPI you are saving development time, bugs, lines of code, and you would probably get the same performance (or better) you would if you didn't use it (as you would have to implement it all in your code).

    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.

# Exceptions - `HTTPException` and `WebSocketException`

These are the exceptions that you can raise to show errors to the client.

When you raise an exception, as would happen with normal Python, the rest of the execution is aborted. This way you can raise these exceptions from anywhere in the code to abort a request and show the error to the client.

You can use:

* `HTTPException`
* `WebSocketException`

    This allows you to use `pytest` directly without complications.

!!! note "Technical Details"
    You could also use `from starlette.testclient import TestClient`.

    **FastAPI** provides the same `starlette.testclient` as `fastapi.testclient` just as a convenience for you, the developer. But it comes directly from Starlette.

!!! tip

* Then check that the tests **pass** after the PR. ✅

* Many PRs don't have tests, you can **remind** them to add tests, or you can even **suggest** some tests yourself. That's one of the things that consume most time and you can help a lot with that.

* Then also comment what you tried, that way I'll know that you checked it. 🤓

## Create a Pull Request

```

</div>

### Check the responses

Now, if you go to the URL with the port for Uvicorn: <a href="http://127.0.0.1:8000/app" class="external-link" target="_blank">http://127.0.0.1:8000/app</a>, you will see the normal response:

```JSON
{
    "message": "Hello World",
    "root_path": "/api/v1"
}
```

!!! tip

## Dataclasses in Nested Data Structures

You can also combine `dataclasses` with other type annotations to make nested data structures.

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.

Let's say you have a custom exception `UnicornException` that you (or a library you use) might `raise`.

And you want to handle this exception globally with FastAPI.

You could add a custom exception handler with `@app.exception_handler()`:

```Python hl_lines="5-7  13-18  24"
{!../../../docs_src/handling_errors/tutorial003.py!}
```

    {!> ../../../docs_src/security/tutorial003.py!}
    ```

!!! tip
    By the spec, you should return a JSON with an `access_token` and a `token_type`, the same as in this example.

    This is something that you have to do yourself in your code, and make sure you use those JSON keys.

    It's almost the only thing that you have to remember to do correctly yourself, to be compliant with the specifications.

Here is the reference for it and its parameters.

You can import it directly from `fastapi`:

```python
from fastapi import Depends
```

::: fastapi.Depends

## `Security()`

For many scenarios, you can handle security (authorization, authentication, etc.) with dependencies, using `Depends()`.

But when you want to also declare OAuth2 scopes, you can use `Security()` instead of `Depends()`.