labels.docId=ID документа
labels.endTime=Время окончания
labels.hq=hq
labels.inputs=Входы
labels.jobLogging=Ведение журнала
labels.jobName=Имя
labels.jobStatus=Статус
labels.labelTypeIds=Метки
labels.lang=Язык
labels.outputs=Выходы
labels.pos=Часть речи
labels.purgeJobLogDay=Удалить старые журналы заданий
labels.purgeUserInfoDay=Удалить старые журналы пользователей
labels.reading=Чтение
labels.roleTypeIds=ID ролей

labels.docId=ID documento
labels.endTime=Ora di fine
labels.hq=hq
labels.inputs=Input
labels.jobLogging=Registrazione
labels.jobName=Nome
labels.jobStatus=Stato
labels.labelTypeIds=Etichette
labels.lang=Lingua
labels.outputs=Output
labels.pos=Parte del discorso
labels.purgeJobLogDay=Elimina log di lavoro precedenti
labels.purgeUserInfoDay=Elimina log utente precedenti
labels.reading=Lettura
labels.roleTypeIds=ID ruolo

labels.docId=ID du document
labels.endTime=Heure de fin
labels.hq=hq
labels.inputs=Entrées
labels.jobLogging=Journalisation
labels.jobName=Nom
labels.jobStatus=Statut
labels.labelTypeIds=Étiquettes
labels.lang=Langue
labels.outputs=Sorties
labels.pos=Partie du discours
labels.purgeJobLogDay=Supprimer les anciens journaux de tâches
labels.purgeUserInfoDay=Supprimer les anciens journaux utilisateur
labels.reading=Lecture
labels.roleTypeIds=ID de rôle

labels.dictId=字典ID
labels.docId=文档ID
labels.endTime=结束时间
labels.hq=hq
labels.inputs=输入
labels.jobLogging=日志记录
labels.jobName=名称
labels.jobStatus=状态
labels.labelTypeIds=标签
labels.lang=语言
labels.outputs=输出
labels.pos=词性
labels.purgeJobLogDay=删除以前的作业日志
labels.purgeUserInfoDay=删除以前的用户日志
labels.reading=阅读
labels.roleTypeIds=角色ID
labels.scriptData=脚本
labels.scriptResult=结果
labels.scriptType=执行方法

labels.dictId=辞書ID
labels.docId=ドキュメントID
labels.endTime=終了時間
labels.hq=hq
labels.inputs=変換元
labels.jobLogging=ロギング
labels.jobName=名前
labels.jobStatus=状態
labels.labelTypeIds=ラベル
labels.lang=言語
labels.outputs=対象
labels.pos=品詞
labels.purgeJobLogDay=以前のジョブログを削除
labels.purgeUserInfoDay=以前のユーザーログを削除
labels.reading=読み
labels.roleTypeIds=ロールID
labels.scriptData=スクリプト
labels.scriptResult=結果
labels.scriptType=実行方法

        Thread.sleep(50);

        String output1 = thread.getOutput();
        boolean contains1 = thread.contains("concurrent1");

        thread.join(1000);

        String output2 = thread.getOutput();
        boolean contains2 = thread.contains("concurrent1");

        assertNotNull(output1);
        assertNotNull(output2);
        assertTrue(contains2);
    }

    @Test

 * @param <OutputT> the type of the output (i.e. this) future
 */
@GwtCompatible
@SuppressWarnings(
    // Whenever both tests are cheap and functional, it's faster to use &, | instead of &&, ||
    "ShortCircuitBoolean")
abstract class AggregateFuture<InputT extends @Nullable Object, OutputT extends @Nullable Object>
    extends AggregateFutureState<OutputT> {

  std::unique_ptr<tensorflow::ServerInterface> server;
};
#endif  // !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD)

namespace tensorflow {

// Set the shapes and types of the output's handle.
//
// The lengths of the arrays pointed to by `shapes`, `ranks`, and `types` must
// all be equal to `num_shapes_and_types`. If `ranks[i] != -1`, (i.e., if the

///

## Bir output modeli ekleyin { #add-an-output-model }

Bunun yerine, plaintext password içeren bir input modeli ve password’ü içermeyen bir output modeli oluşturabiliriz:

{* ../../docs_src/response_model/tutorial003_py310.py hl[9,11,16] *}

    private String newOutput;

    /**
     * Constructs a new stemmer override item.
     *
     * @param id     The unique identifier of the item.
     * @param input  The input word.
     * @param output The output stem.
     */
    public StemmerOverrideItem(final long id, final String input, final String output) {
        this.id = id;
        this.input = input;
        this.output = output;