val name = lock.withLock { nextQueueName++ }
    return TaskQueue(this, "Q$name")
  }

  /**
   * Returns a snapshot of queues that currently have tasks scheduled. The task runner does not
   * necessarily track queues that have no tasks scheduled.
   */
  fun activeQueues(): List<TaskQueue> {
    lock.withLock {
      return busyQueues + readyQueues
    }
  }

  fun cancelAll() {
    lock.assertHeld()

    assertThat(testLogHandler.takeAll()).containsExactly(
      "FINE: Q10000 scheduled after 100 µs: task one",
      "FINE: Q10000 scheduled after 200 µs: task two",
    )
  }

  /**
   * We don't track the active task in scheduled tasks. This behavior might be a mistake, but it's
   * cumbersome to implement properly because the active task might be a cancel.
   */
  @Test fun scheduledTasksDoesNotIncludeRunningTask() {

	// suspended or disabled on this bucket. RenameData will replace
	// the 'null' version. We add a free-version to track its tiered
	// content for asynchronous deletion.
	//
	// Note: RestoreObject and HealObject requests don't end up replacing the
	// null version and therefore don't require the free-version to track
	// anything
	if fi.VersionID == "" && !fi.IsRestoreObjReq() && !fi.Healing() {

    @CheckForNull private Iterator<? extends T> toRemove;

    /* The iterator currently returning elements. */
    private Iterator<? extends T> iterator;

    /*
     * We track the "meta iterators," the iterators-of-iterators, below.  Usually, topMetaIterator
     * is the only one in use, but if we encounter nested concatenations, we start a deque of

				}
			}
			if err != nil {
				return toObjectErr(err, bucket, object)
			}
		}
		for i, r := range readers {
			if r == nil {
				onlineDisks[i] = OfflineDisk
			}
		}
		// Track total bytes read from disk and written to the client.
		totalBytesRead += partLength
		// partOffset will be valid only for the first part, hence reset it to 0 for
		// the remaining parts.
		partOffset = 0

    * Added 'UpdateEmbeddingCallback`
    * `DynamicEmbedding` layer allows for the continuous updating of the
      vocabulary and embeddings during the training process. This layer
      maintains a hash table to track the most up-to-date vocabulary based on
      the inputs received by the layer and the eviction policy. When this layer
      is used with an `UpdateEmbeddingCallback`, which is a time-based callback,