// is reached and keep reusing the same source string - that
	// should therefore be always resident in the L1 cache - until we
	// have completed the construction of the result.
	// This yields significant speedups (up to +100%) in cases where
	// the result length is large (roughly, over L2 cache size).
	const chunkLimit = 8 * 1024
	chunkMax := n
	if chunkMax > chunkLimit {
		chunkMax = chunkLimit / len(b) * len(b)

## How to test this PR?


## Types of changes
- [ ] Bug fix (non-breaking change which fixes an issue)
- [ ] New feature (non-breaking change which adds functionality)
- [ ] Optimization (provides speedup with no functional changes)
- [ ] Breaking change (fix or feature that would cause existing functionality to change)

## Checklist:
- [ ] Fixes a regression (If yes, please add `commit-id` or `PR #` here)

            AVX512_BF16, AMX, etc.
            ([Intel Cascade Lake](https://www.intel.com/content/www/us/en/products/platforms/details/cascade-lake.html)
            and newer CPUs.)
            *   [Example performance speedups.](https://medium.com/intel-analytics-software/leverage-intel-deep-learning-optimizations-in-tensorflow-129faa80ee07)
        *   For older CPUs, oneDNN optimizations are disabled by default.

   * AtomicIntegerFieldUpdater}, which will prevent us from selecting the {@code SafeAtomicHelper}
   * strategy.
   *
   * <p>Stashing this in a static field avoids loading it over and over again and speeds up test
   * execution significantly.
   */
  private static final ClassLoader NO_ATOMIC_FIELD_UPDATER =
      getClassLoader(
          ImmutableSet.of(
              AtomicIntegerFieldUpdater.class.getName(),

 */

@NullUnmarked
public class AbstractFutureFallbackAtomicHelperTest extends TestCase {

  // stash these in static fields to avoid loading them over and over again (speeds up test
  // execution significantly)

  /**
   * This classloader disallows {@link java.lang.invoke.VarHandle}, which will prevent us from
   * selecting the {@code VarHandleAtomicHelper} strategy.
   */

#
# Docker is enabled by default on Linux. You may disable it if you prefer:
# export TFCI=py311,linux_x86,no_docker

# Advanced: Use Remote Build Execution (RBE) (internal developers only)
#
#   RBE dramatically speeds up builds and testing. It also gives you a
#   public URL to share your build results with collaborators. However,
#   it is only available to a limited set of internal TensorFlow developers.
#

}),ce.speed=function(e,t,n){var r=e&&"object"==typeof e?ce.extend({},e):{complete:n||!n&&t||v(e)&&e,duration:e,easing:n&&t||t&&!v(t)&&t};return ce.fx.off?r.duration=0:"number"!=typeof r.duration&&(r.duration in ce.fx.speeds?r.duration=ce.fx.speeds[r.duration]:r.duration=ce.fx.speeds._default),null!=r.queue&&!0!==r.queue||(r.queue="fx"),r.old=r.complete,r.complete=function(){v(r.old)&&r.old.call(this),r.queue&&ce.dequeue(this,r.queue)},r},ce.fn.extend({fadeTo:function(e,t,n,r){return this.filter(...

      write_to_bazelrc('build --define=%s=%s' % (varname, environ_cp[varname]))


def set_windows_build_flags(environ_cp):
  """Set Windows specific build options."""

  # First available in VS 16.4. Speeds up Windows compile times by a lot. See
  # https://groups.google.com/a/tensorflow.org/d/topic/build/SsW98Eo7l3o/discussion
  # pylint: disable=line-too-long
  write_to_bazelrc(

tion(a,b,c){var d=a&&"object"==typeof a?n.extend({},a):{complete:c||!c&&b||n.isFunction(a)&&a,duration:a,easing:c&&b||b&&!n.isFunction(b)&&b};return d.duration=n.fx.off?0:"number"==typeof d.duration?d.duration:d.duration in n.fx.speeds?n.fx.speeds[d.duration]:n.fx.speeds._default,(null==d.queue||d.queue===!0)&&(d.queue="fx"),d.old=d.complete,d.complete=function(){n.isFunction(d.old)&&d.old.call(this),d.queue&&n.dequeue(this,d.queue)},d},n.fn.extend({fadeTo:function(a,b,c,d){return this.filter(S)...

      totalCount += count;
      return this;
    }

    AvlNode<E> add(
        Comparator<? super E> comparator, @ParametricNullness E e, int count, int[] result) {
      /*
       * It speeds things up considerably to unconditionally add count to totalCount here,
       * but that destroys failure atomicity in the case of count overflow. =(
       */
      int cmp = comparator.compare(e, getElement());