*
 * <p>Keep a strong reference to this object until all of the associated referents have been
 * finalized. If this object is garbage collected earlier, the backing thread will not invoke {@code
 * finalizeReferent()} on the remaining references.
 *
 * <p>As an example of how this is used, imagine you have a class {@code MyServer} that creates a

                    if (step.status.compareAndSet(CREATED, SKIPPED)) {
                        logger.debug(
                                "Running after:* step {} for cleanup but marking it as SKIPPED because a predecessor failed",
                                step);
                        executor.execute(() -> {
                            try {
                                executeStep(step);

    assertThat(request.tag(String::class.java)).isSameAs(stringTag)
    assertThat(request.tag(Long::class.javaObjectType)).isSameAs(longTag)
  }

  /** Confirm that we don't accidentally share the backing map between objects. */
  @Test
  fun tagsAreImmutable() {
    val builder =
      Request
        .Builder()
        .url("https://square.com")
    val requestA = builder.tag(String::class.java, "a").build()

import java.util.TreeSet;
import org.jspecify.annotations.Nullable;

/**
 * An immutable builder for {@link Multimap} instances, letting you independently select the desired
 * behaviors (for example, ordering) of the backing map and value-collections. Example:
 *
 * {@snippet :
 * ListMultimap<UserId, ErrorResponse> errorsByUser =
 *     MultimapBuilder.linkedHashKeys().arrayListValues().build();

      @Override
      public void run() {
        lock.lock();
        try {
          /*
           * requireNonNull is safe because Task isn't run (or at least it doesn't succeed in taking
           * the lock) until after it's scheduled and the runningTask field is set.
           */
          if (requireNonNull(runningTask).isCancelled()) {

			return true
		}

		goOffline := func(err error, spent time.Duration) {
			if p.health.status.CompareAndSwap(diskHealthOK, diskHealthFaulty) {
				storageLogAlwaysIf(ctx, fmt.Errorf("node(%s): taking drive %s offline: %v", globalLocalNodeName, p.storage.String(), err))
				p.health.waiting.Add(1)
				go p.monitorDiskStatus(spent, fn)
			}
		}

		// Offset checks a bit.

   * <p>Due to the property that values in a BiMap are unique, this will tend to execute in
   * faster-than-linear time.
   *
   * @param value the object to search for in the values of this BiMap
   * @return true if a mapping exists from a key to the specified value
   */
  @Override
  public boolean containsValue(@Nullable Object value) {
    return findEntryByValue(value) != ABSENT;
  }

  @Override

With **FastAPI** you can take advantage of concurrency that is very common for web development (the same main attraction of NodeJS).

But you can also exploit the benefits of parallelism and multiprocessing (having multiple processes running in parallel) for **CPU bound** workloads like those in Machine Learning systems.

    
    // Schedule reconnection attempt
    scheduleReconnection(1000);  // 1 second delay
}

private void handleClientMove(WitnessNotification notification) {
    // Server is asking client to move to different node
    log.info("Client move requested for resource: {}", notification.getResourceName());
    
    List<WitnessNotification.WitnessIPAddress> newAddresses = notification.getNewIPAddresses();

   * world. Figure out what sort of space-time tradeoff we're actually going to get here with the
   * *Map variants. This class is particularly hard to benchmark, because the benefit is not only in
   * less allocation, but also having the GC do less work to scan the heap because of fewer
   * references, which is particularly hard to quantify.
   */

  /** Creates an empty {@code CompactHashMap} instance. */