frequency.set(0);
    }
    backingMap.clear();
    size = 0L;
  }

  @Override
  int distinctElements() {
    return backingMap.size();
  }

  // Optimizations - Query Operations

  @Override
  public int size() {
    return Ints.saturatedCast(size);
  }

  @Override
  public Iterator<E> iterator() {
    return new MapBasedMultisetIterator();
  }

// as if passed in XLA_FLAGS. This has global effect.
TF_CAPI_EXPORT void TF_SetXlaAutoJitMode(const char* mode);

// Returns whether the single GPU or general XLA auto jit optimizations are
// enabled through MarkForCompilationPassFlags.
TF_CAPI_EXPORT unsigned char TF_GetXlaAutoJitEnabled();

// Sets XLA's minimum cluster size. This has global effect.

     */
    ModelCache getModelCache();

    /**
     * Sets the model cache to use for reuse of previously built models. This is an optional component that serves
     * performance optimizations.
     *
     * @param modelCache The model cache to use, may be {@code null}.
     * @return This request, never {@code null}.
     */
    ModelBuildingRequest setModelCache(ModelCache modelCache);

        double totalTimeMs = (end - start) / 1_000_000.0;

        System.out.printf("Overall Performance Test: completed in %.2f ms%n", totalTimeMs);

        // Should complete very quickly with all optimizations
        assertTrue(totalTimeMs < 100, "All operations should complete quickly with performance fixes");

        pool.close();
    }

* Instrument the Azure cloud provider for Prometheus monitoring. ([#58204](https://github.com/kubernetes/kubernetes/pull/58204), [@cosmincojocar](https://github.com/cosmincojocar))
* -Add scheduler optimization options, short circuit all predicates if … ([#56926](https://github.com/kubernetes/kubernetes/pull/56926), [@wgliang](https://github.com/wgliang))

    return new Inverse<>(backward, this);
  }

  void setInverse(AbstractBiMap<V, K> inverse) {
    this.inverse = inverse;
  }

  // Query Operations (optimizations)

  @Override
  public boolean containsValue(@Nullable Object value) {
    return inverse.containsKey(value);
  }

  // Modification Operations

  @CanIgnoreReturnValue
  @Override

* decodificar

* la excepción
* lanzar

* la expresión
* el statement

* el frontend
* el backend

* la discusión de GitHub
* el issue de GitHub

* el rendimiento
* la optimización de rendimiento

* el tipo de retorno
* el valor de retorno

* la seguridad
* el esquema de seguridad

* la tarea
* la tarea en segundo plano
* la función de tarea

* la plantilla

SIG Scheduling development efforts have been primarily focused on improving performance and reliability of the scheduler.
- Performance of the inter-pod affinity/anti-affinity feature is improved over 100X via algorithmic optimization.
- DaemonSet pods, which used to be scheduled by the DaemonSet controller, will be scheduled by the default scheduler in 1.12. This change allows DaemonSet pods to enjoy all the scheduling features of the default scheduler.

    return getBooleanProperty(DIRECTORY_NOTIFICATIONS_ENABLED, true);
}

public int getMaxDirectoryCacheEntries() {
    return getIntProperty(MAX_DIRECTORY_CACHE_ENTRIES, 1000);
}
```

## 7. Performance Optimizations

### 7.1 Batch Directory Operations
```java
public class BatchDirectoryOperations {
    private final DirectoryLeaseManager leaseManager;
    
    public List<SmbFile> batchExists(List<String> paths) {

import static org.mockito.Mockito.mock;
import static org.mockito.Mockito.when;

/**
 * Unit tests for the {@link InferenceStrategy} class.
 * Tests Maven 4.1.0+ inference optimizations including dependency and parent inference.
 */
@DisplayName("InferenceStrategy")
class InferenceStrategyTest {

    private InferenceStrategy strategy;

    @BeforeEach
    void setUp() {