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.

	if ix < 0 {
		return ""
	}
	name := exportName[ix+1:]
	if strings.HasPrefix(name, blankMarker) {
		return "_"
	}
	return name
}

// The name used for dictionary parameters or local variables.

		val.AsApproximateFloat64(),
		namespace, name, nodeName, schedulerName, priority, string(resourceName), unit,
	)
}

// podRequestsAndLimitsByLifecycle returns a dictionary of all defined resources summed up for all
// containers of the pod. Pod overhead is added to the
// total container resource requests and to the total container limits which have a

	ErrSecNotTrusted         = -67843
)

type OSStatus struct {
	call   string
	status int32
}

func (s OSStatus) Error() string {
	return s.call + " error: " + strconv.Itoa(int(s.status))
}

// Dictionary keys are defined as build-time strings with CFSTR, but the Go
// linker's internal linking mode can't handle CFSTR relocations. Create our
// own dynamic strings instead and just never release them.
//

                    }
                },
                "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.

                    }
                },
                "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!}
```

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*.

```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*.

import com.dd.plist.NSDictionary;
import org.gradle.api.internal.PropertyListTransformer;
import org.gradle.plugins.ide.internal.generator.PropertyListPersistableConfigurationObject;

public class XcodeProjectFile extends PropertyListPersistableConfigurationObject<NSDictionary> {

    public XcodeProjectFile(PropertyListTransformer<NSDictionary> transformer) {
        super(NSDictionary.class, transformer);
    }

    }

    @Override
    protected NSDictionary newRootObject() {
        return new NSDictionary();
    }

    @Override
    protected void store(NSDictionary rootObject) {
    }

    @Override
    protected void load(NSDictionary rootObject) {
    }

    @Override
    protected String getDefaultResourceName() {
        return "default.xcsettings";
    }