5. You can use other standard type annotations with dataclasses as the request body.

    In this case, it's a list of `Item` dataclasses.

6. Here we are returning a dictionary that contains `items` which is a list of dataclasses.

    FastAPI is still capable of <abbr title="converting the data to a format that can be transmitted">serializing</abbr> the data to JSON.

Import the `Response` class (sub-class) you want to use and declare it in the *path operation decorator*.

For large responses, returning a `Response` directly is much faster than returning a dictionary.

For example, you could want to **return a dictionary** or a database object, but **declare it as a Pydantic model**. This way the Pydantic model would do all the data documentation, validation, etc. for the object that you returned (e.g. a dictionary or database object).

                    }
                },
                "x-aperture-labs-portal": "blue"
            }
        }
    }
}
```

### Benutzerdefiniertes OpenAPI-*Pfadoperation*-Schema

Das Dictionary in `openapi_extra` wird mit dem automatisch generierten OpenAPI-Schema für die *Pfadoperation* zusammengeführt (mittels Deep Merge).

Sie können dem automatisch generierten Schema also zusätzliche Daten hinzufügen.

labels.joblog_delete_all_link=Delete All
labels.joblog_delete_all_confirmation=Do you really want to delete all?
labels.joblog_delete_all_cancel=Cancel
labels.dict_configuration=Dictionary List
labels.dict_list_title=Dictionary List
labels.dict_list_link=Dictionaries
labels.dictionary_name=Name
labels.dictionary_type=Type
labels.dict_synonym_configuration=Synonym List
labels.dict_synonym_title=Synonym List

{* ../../docs_src/events/tutorial003.py hl[16,19] *}

Here we are simulating the expensive *startup* operation of loading the model by putting the (fake) model function in the dictionary with machine learning models before the `yield`. This code will be executed **before** the application **starts taking requests**, during the *startup*.

        if (StringUtil.isNotBlank(fessConfig.getIndexDictionaryPrefix())) {
            String dictionaryPath = System.getProperty("fess.dictionary.path", StringUtil.EMPTY);
            if (StringUtil.isBlank(dictionaryPath)) {
                System.setProperty("fess.dictionary.path", fessConfig.getIndexDictionaryPrefix() + "/");
            } else {
                if (!dictionaryPath.endsWith("/")) {

            """
        ),
    ] = None,
    init_oauth: Annotated[
        Optional[Dict[str, Any]],
        Doc(
            """
            A dictionary with Swagger UI OAuth2 initialization configurations.
            """
        ),
    ] = None,
    swagger_ui_parameters: Annotated[
        Optional[Dict[str, Any]],
        Doc(
            """

                    }
                },
                "x-aperture-labs-portal": "blue"
            }
        }
    }
}
```

### Custom OpenAPI *path operation* schema

The dictionary in `openapi_extra` will be deeply merged with the automatically generated OpenAPI schema for the *path operation*.

So, you could add additional data to the automatically generated schema.

```Python hl_lines="16  19"
{!../../docs_src/events/tutorial003.py!}
```

Hier simulieren wir das langsame *Hochfahren*, das Laden des Modells, indem wir die (Fake-)Modellfunktion vor dem `yield` in das Dictionary mit Modellen für maschinelles Lernen einfügen. Dieser Code wird ausgeführt, **bevor** die Anwendung **beginnt, Requests entgegenzunehmen**, während des *Hochfahrens*.