sectorSize := uint64(512)
	kib := float64(1 << 10)
	diffInSeconds := time.Now().UTC().Sub(lastDriveStatsRefresh).Seconds()
	if diffInSeconds == 0 {
		// too soon to update the stats
		return
	}
	diffStats := getDiffStats(latestStats, currentStats)

	updateResourceMetrics(driveSubsystem, readsPerSec, float64(diffStats.ReadIOs)/diffInSeconds, labels, false)

    if (strategy == ResumePriority.AfterOtherTasks && otherTasksStarted) {
      return yieldUntil(strategy, condition)
    }
  }

  private enum class ResumePriority {
    /** Resumes as soon as the condition is satisfied. */
    BeforeOtherTasks,

    /** Resumes after the already-enqueued tasks. */
    AfterEnqueuedTasks,

    routePlanner.autoGeneratePlans = true
    routePlanner.defaultConnectionIdleAtNanos = expireTime
    val address = routePlanner.address
    val pool = routePlanner.pool

    // Connections are created as soon as a policy is set
    setPolicy(pool, address, ConnectionPool.AddressPolicy(2))
    assertThat(pool.connectionCount()).isEqualTo(2)

    // Connections are replaced if they idle out or are evicted from the pool

			}
			generations := []string{firstVersion}
			targetResource := config.Key(
				targetSchema.Group(), targetSchema.Version(), targetSchema.Kind(),
				nameflag, namespace)
			for {
				// run the check here as soon as we start
				// because tickers won't run immediately
				present, notpresent, sdcnum, err := poll(cliCtx, cmd, generations, targetResource, proxyFlag, opts)

 * to mutate the event parameters, or be re-entrant back into the client. Any IO - writing to files
 * or network should be done asynchronously.
 */
abstract class EventListener {
  /**
   * Invoked as soon as a call is enqueued or executed by a client. In case of thread or stream
   * limits, this call may be executed well before processing the request is able to begin.
   *

# API IMPORTS PLACEHOLDER

# WRAPPER_PLACEHOLDER

if "dev" in __version__:   # pylint: disable=undefined-variable
  _logging.warning("""

  TensorFlow's `tf-nightly` package will soon be updated to TensorFlow 2.0.

  Please upgrade your code to TensorFlow 2.0:
    * https://www.tensorflow.org/guide/migrate

  Or install the latest stable TensorFlow 1.X release:
    * `pip install -U "tensorflow==1.*"`

     *
     * <p>Prefer the equivalent method {@link #buildOrThrow()} to make it explicit that the method
     * will throw an exception if there are duplicate key pairs. The {@code build()} method will
     * soon be deprecated.
     *
     * @throws IllegalArgumentException if duplicate key pairs were added
     */
    public ImmutableTable<R, C, V> build() {
      return buildOrThrow();
    }

    /**

    // (c) Occupying the monitor and awaiting the tearDownLatch.
    //
    // Except for (c), every thread should occupy the monitor very briefly, and every thread leaves
    // the monitor with the guard satisfied. Therefore as soon as tearDownLatch is triggered, we
    // should be able to enter the monitor, and then we set the guard to satisfied for the benefit
    // of any remaining waiting threads.

    tearDownLatch.countDown();
    assertTrue(

    // (c) Occupying the monitor and awaiting the tearDownLatch.
    //
    // Except for (c), every thread should occupy the monitor very briefly, and every thread leaves
    // the monitor with the guard satisfied. Therefore as soon as tearDownLatch is triggered, we
    // should be able to enter the monitor, and then we set the guard to satisfied for the benefit
    // of any remaining waiting threads.

    tearDownLatch.countDown();
    assertTrue(

 *       Future} returned by this class, the next task may still have to wait.</i>.
 *   <li>Once an {@code AsyncCallable} returns a {@code Future}, this class considers that task to
 *       be "done" as soon as <i>that</i> {@code Future} completes in any way. Notably, a {@code
 *       Future} is "completed" even if it is cancelled while its underlying work continues on a