!!! info
    Keep in mind that dataclasses can't do everything Pydantic models can do.

    So, you might still need to use Pydantic models.

    But if you have a bunch of dataclasses laying around, this is a nice trick to use them to power a web API using FastAPI. 🤓

## Dataclasses in `response_model`

You can also use `dataclasses` in the `response_model` parameter:

```Python hl_lines="1  7-13  19"

val startScripts = tasks.register<GradleStartScriptGenerator>("startScripts") {
    startScriptsDir = layout.buildDirectory.dir("startScripts")
    launcherJar.from(tasks.jar)
    agentJars.from(agentsClasspath)
    // The trick below is to use the templates from the current code instead of the wrapper. It does not cover the case where the generation logic is updated though.

      result = (byte) (result ^ checksum.getValue());
    }
    return result;
  }

  // Helpers + main

  private byte runHashFunction(int reps, HashFunction hashFunction) {
    byte result = 0x01;
    // Trick the JVM to prevent it from using the hash function non-polymorphically
    result ^= Hashing.crc32().hashInt(reps).asBytes()[0];
    result ^= Hashing.adler32().hashInt(reps).asBytes()[0];
    for (int i = 0; i < reps; i++) {

      result = (byte) (result ^ checksum.getValue());
    }
    return result;
  }

  // Helpers + main

  private byte runHashFunction(int reps, HashFunction hashFunction) {
    byte result = 0x01;
    // Trick the JVM to prevent it from using the hash function non-polymorphically
    result ^= Hashing.crc32().hashInt(reps).asBytes()[0];
    result ^= Hashing.adler32().hashInt(reps).asBytes()[0];
    for (int i = 0; i < reps; i++) {

    Daher müssen Sie möglicherweise weiterhin Pydantic-Modelle verwenden.

    Wenn Sie jedoch eine Menge Datenklassen herumliegen haben, ist dies ein guter Trick, um sie für eine Web-API mithilfe von FastAPI zu verwenden. 🤓

## Datenklassen als `response_model`

Sie können `dataclasses` auch im Parameter `response_model` verwenden:

```Python hl_lines="1  7-13  19"

    ```

## Order the parameters as you need, tricks

!!! tip
    This is probably not as important or necessary if you use `Annotated`.

Here's a **small trick** that can be handy, but you won't need it often.

If you want to:

* declare the `q` query parameter without a `Query` nor any default value
* declare the path parameter `item_id` using `Path`

Nevertheless, we can declare the expected schema for the request body.

### Custom OpenAPI content type

Using this same trick, you could use a Pydantic model to define the JSON Schema that is then included in the custom OpenAPI schema section for the *path operation*.

And you could do this even if the data type in the request is not JSON.

Dennoch können wir das zu erwartende Schema für den Requestbody deklarieren.

### Benutzerdefinierter OpenAPI-Content-Type

Mit demselben Trick könnten Sie ein Pydantic-Modell verwenden, um das JSON-Schema zu definieren, das dann im benutzerdefinierten Abschnitt des OpenAPI-Schemas für die *Pfadoperation* enthalten ist.

    assert state["finally"] is False
    response = client.put("/invalid-user/rick", json="Morty")
    assert response.status_code == 400, response.text
    assert response.json() == {"detail": "Invalid user"}
    assert state["except"] is True
    assert state["finally"] is True
    assert fake_database["rick"] == "Rick Sanchez"


def test_dependency_no_exception():
    assert state["except"] is False

    assert response.status_code == 400, response.text
    assert response.json() == {"detail": "Owner error: Rick"}


def test_get_item():
    response = client.get("/items/portal-gun")
    assert response.status_code == 200, response.text