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

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

	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.
//

            """
        ),
    ] = 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"
            }
        }
    }
}
```

### 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.

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).

  }) {devices = {TPU_REPLICATED_CORE_0 = ["/DEVICE:0", "/DEVICE:1"]}, n = 3 : i32, operandSegmentSizes = array<i32: 0, 0>} : () -> ()
}

// -----

// Check that replicate op's 'devices' attribute must consist of dictionary
// with values as list with size equal to 'n' attribute.
func.func @verifier_replicate_n_device_multiple_alias() {
  "tf_device.replicate" () ({

```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*.