}

  @Footprint
  Object sizeOfStriped() {
    return impl.get(numStripes);
  }

  // a place to put the locks in sizeOfPopulatedStriped so they don't get GC'd before we measure
  final List<Lock> locks = new ArrayList<>(numStripes);

  @Footprint
  Object sizeOfPopulatedStriped() {
    locks.clear();
    Striped<Lock> striped = impl.get(numStripes);
    for (int i : stripes) {

  private val eventsForMatching = ConcurrentLinkedDeque<CallEvent>()

  private val forbiddenLocks = mutableListOf<Any>()

  /** The timestamp of the last taken event, used to measure elapsed time between events. */
  private var lastTimestampNs: Long? = null

  /** Confirm that the thread does not hold a lock on `lock` during the callback. */
  fun forbidLock(lock: Any) {
    forbiddenLocks.add(lock)

      label: "Orthogonality: How does this change interact or overlap with existing features?"
      description: "Is the goal of this change a performance improvement? If so, what quantifiable improvement should we expect? How would we measure it?"
    validations:
      required: false

  - type: textarea
    id: learning-curve
    attributes:
      label: "Would this change make Go easier or harder to learn, and why?"

  - type: textarea

  private void weaklyReferenceQueue() {
    frq = new FinalizableReferenceQueue();
    queueReference = new WeakReference<>(frq.queue);

    /*
     * Queue and clear a reference for good measure. We test later on that
     * the finalizer thread stopped, but we should test that it actually
     * started first.
     */
    reference =
        new FinalizableWeakReference<Object>(new Object(), frq) {

          if ((hash1 & (1 << k)) == (hash2 & (1 << k))) {
            same[k] += 1;
          } else {
            diff[k] += 1;
          }
        }
      }
      // measure probability and assert it's within margin of error
      for (int j = 0; j < hashBits; j++) {
        double prob = (double) diff[j] / (double) (diff[j] + same[j]);
        assertThat(prob).isWithin(epsilon).of(0.50d);

          if ((hash1 & (1 << k)) == (hash2 & (1 << k))) {
            same[k] += 1;
          } else {
            diff[k] += 1;
          }
        }
      }
      // measure probability and assert it's within margin of error
      for (int j = 0; j < hashBits; j++) {
        double prob = (double) diff[j] / (double) (diff[j] + same[j]);
        assertThat(prob).isWithin(epsilon).of(0.50d);

      if (contentType != null) {
        sink
          .writeUtf8("Content-Type: ")
          .writeUtf8(contentType.toString())
          .write(CRLF)
      }

      // We can't measure the body's size without the sizes of its components.
      val contentLength = body.contentLength()
      if (contentLength == -1L && countBytes) {
        byteCountBuffer!!.clear()
        return -1L
      }

    
    SmbFile file = new SmbFile("smb://server/share/largefile.dat", context);
    
    // Measure single channel throughput
    long singleChannelTime = measureReadTime(file, false);
    
    // Measure multi-channel throughput
    long multiChannelTime = measureReadTime(file, true);
    
    // Multi-channel should be faster

 * <p>A high-quality hash function strives for some subset of the following virtues:
 *
 * <ul>
 *   <li><b>collision-resistant:</b> while the definition above requires making at least <i>some</i>
 *       token attempt, one measure of the quality of a hash function is <i>how well</i> it succeeds
 *       at this goal. Important note: it may be easy to achieve the theoretical minimum collision

 * <p>A high-quality hash function strives for some subset of the following virtues:
 *
 * <ul>
 *   <li><b>collision-resistant:</b> while the definition above requires making at least <i>some</i>
 *       token attempt, one measure of the quality of a hash function is <i>how well</i> it succeeds
 *       at this goal. Important note: it may be easy to achieve the theoretical minimum collision