@GwtIncompatible // java.io.ObjectInputStream
  private void readObject(ObjectInputStream stream) throws IOException, ClassNotFoundException {
    stream.defaultReadObject();
    @SuppressWarnings("unchecked")
    // reading data stored by writeObject
    Comparator<? super E> comparator = (Comparator<? super E>) requireNonNull(stream.readObject());
    Serialization.getFieldSetter(AbstractSortedMultiset.class, "comparator").set(this, comparator);

labels.jobStatus=Status
labels.labelTypeIds=Label
labels.lang=Sprache
labels.outputs=Ziel
labels.pos=POS
labels.purgeJobLogDay=Bereinige Suchprotokoll nach
labels.purgeUserInfoDay=Bereinige Benutzer nach
labels.reading=Lesen
labels.roleTypeIds=Rollen-ID
labels.scriptData=Skript
labels.scriptResult=Suchergebnis
labels.scriptType=Executor
labels.segmentation=Segmentierung
labels.startTime=Startzeitpunkt
labels.target=Ziel

# TODO(b/173549186): Move Google-internal TF code out of learning/brain
package_group(
    name = "internal",
    packages = [
        "//devtools/python/indexer/...",
        "//learning/brain/keras/...",
        "//learning/brain/mlir/...",
        "//learning/brain/tfrt/...",
        "//learning/lib/ami/simple_ml/...",
        "//learning/pathways/...",
        "//learning/serving/contrib/tfrt/mlir/canonical_ops/...",

		opts.VersionID = oi.VersionID
	}
	tags := newLifecycleAuditEvent(src, lcEvent).Tags()
	if lcEvent.Action.DeleteRestored() {
		// delete locally restored copy of object or object version
		// from the source, while leaving metadata behind. The data on
		// transitioned tier lies untouched and still accessible
		opts.Transition.ExpireRestored = true
		_, err := objectAPI.DeleteObject(ctx, oi.Bucket, oi.Name, opts)
		if err == nil {

* **Machine Learning**: normalmente requiere muchas multiplicaciones de "matrices" y "vectores". Imagina en una enorme hoja de cálculo con números y tener que multiplicarlos todos al mismo tiempo.

		}
		retries++
		time.Sleep(time.Duration(rand.Int63n(int64(time.Second))))
	}

	if retries == 5 {
		scannerLogOnceIf(ctx, fmt.Errorf("maximum retry reached to load the data usage cache `%s`", name), "retry-loading-data-usage-cache")
	}

	return nil
}

// Maximum running concurrent saves on server.
var maxConcurrentScannerSaves = make(chan struct{}, 4)

		cmd.Env = listEnv(w.context)
		if w.context.Dir != "" {
			cmd.Dir = w.context.Dir
		}
		cmd.Stderr = os.Stderr
		out, err := cmd.Output()
		if err != nil {
			log.Fatalf("loading imports: %v\n%s", err, out)
		}

		var stdPackages []string
		importMap := make(map[string]map[string]string)
		importDir := make(map[string]string)
		dec := json.NewDecoder(bytes.NewReader(out))
		for {

labels.labelTypeIds	=	Étiquettes
labels.lang	=	langue
labels.outputs	=	Cible
labels.pos	=	PDV
labels.purgeJobLogDay	=	Purger le journal des tâches avant
labels.purgeUserInfoDay	=	Purger l'utilisateur avant
labels.reading	=	Lecture
labels.roleTypeIds	=	ID de rôle
labels.scriptData	=	Scénario
labels.scriptResult	=	Résultat
labels.scriptType	=	Exécuteur
labels.segmentation	=	Segmentation
labels.startTime	=	Heure de début

	return p.storage.Endpoint()
}

func (p *xlStorageDiskIDCheck) Hostname() string {
	return p.storage.Hostname()
}

func (p *xlStorageDiskIDCheck) Healing() *healingTracker {
	return p.storage.Healing()
}

func (p *xlStorageDiskIDCheck) NSScanner(ctx context.Context, cache dataUsageCache, updates chan<- dataUsageEntry, scanMode madmin.HealScanMode, _ func() bool) (dataUsageCache, error) {

tent-wrapper",ge=".content-wrapper iframe",pe=".content-wrapper.iframe-mode .nav",me=".content-wrapper.iframe-mode .navbar-nav",ve=me+" .nav-item",_e=me+" .nav-link",be=".content-wrapper.iframe-mode .tab-content",ye=be+" .tab-empty",Ce=be+" .tab-loading",we=be+" .tab-pane",xe=".main-sidebar .nav-item > a.nav-link",Ie=".main-header .nav-item a.nav-link",Te=".main-header a.dropdown-item",Se="iframe-mode",je="iframe-mode-fullscreen",ke={onTabClick:function(e){return e},onTabChanged:function(e){return...