}

        // Add to history for percentile calculation
        responseTimeHistory.addLast(responseTimeMs);
        if (responseTimeHistory.size() > 1000) { // Keep last 1000 measurements
            responseTimeHistory.removeFirst();
        }

        // Calculate moving average (simple approach)
        if (!responseTimeHistory.isEmpty()) {

			format.mustNotBe(FormatUSTAR)
		}
		needsNano := ts.Nanosecond() != 0
		if !isMtime || !fitsOctal || needsNano {
			preferPAX = true // USTAR may truncate sub-second measurements
			if paxKey == paxNone {
				whyNoPAX = fmt.Sprintf("PAX cannot encode %s=%v", name, ts)
				format.mustNotBe(FormatPAX)
			} else {
				paxHdrs[paxKey] = formatPAXTime(ts)
			}
		}

        .matches("[^\\[]+\\[status=SUCCESS, result=\\[java.lang.Boolean@\\w+\\]\\]");
  }

  /**
   * This test attempts to cause a future to wait for longer than it was requested to from a timed
   * get() call. As measurements of time are prone to flakiness, it tries to assert based on ranges
   * derived from observing how much time actually passed for various operations.
   */
  @SuppressWarnings("ThreadPriorityCheck")

        .matches("[^\\[]+\\[status=SUCCESS, result=\\[java.lang.Boolean@\\w+\\]\\]");
  }

  /**
   * This test attempts to cause a future to wait for longer than it was requested to from a timed
   * get() call. As measurements of time are prone to flakiness, it tries to assert based on ranges
   * derived from observing how much time actually passed for various operations.
   */
  @SuppressWarnings("ThreadPriorityCheck")

 * iterations, to properly test the cache benefits within each measurement period.
 */
@BenchmarkMode(Mode.AverageTime)
@OutputTimeUnit(TimeUnit.MICROSECONDS)
@Warmup(iterations = 3, time = 1)
@Measurement(iterations = 5, time = 10)
@Fork(1)
@State(Scope.Benchmark)
public class CompositeBeanHelperPerformanceTest {

    private ConverterLookup converterLookup;

import org.apache.maven.api.model.InputSource;
import org.openjdk.jmh.annotations.Benchmark;
import org.openjdk.jmh.annotations.BenchmarkMode;
import org.openjdk.jmh.annotations.Level;
import org.openjdk.jmh.annotations.Measurement;
import org.openjdk.jmh.annotations.Mode;
import org.openjdk.jmh.annotations.OutputTimeUnit;
import org.openjdk.jmh.annotations.Scope;
import org.openjdk.jmh.annotations.Setup;
import org.openjdk.jmh.annotations.State;

 * <i>can</i> be subtracted to obtain a {@link Duration} (such as by {@link Duration#between}), but
 * doing so does <i>not</i> give a reliable measurement of elapsed time, because wall time readings
 * are inherently approximate, routinely affected by periodic clock corrections. Because this class
 * (by default) uses {@link System#nanoTime}, it is unaffected by these changes.
 *

	*rate.Limiter
	NodeBandwidthPerSec int64
}

// Monitor holds the state of the global bucket monitor
type Monitor struct {
	tlock sync.RWMutex // mutex for bucket throttling
	mlock sync.RWMutex // mutex for bucket measurement

	bucketsThrottle    map[BucketOptions]*bucketThrottle
	bucketsMeasurement map[BucketOptions]*bucketMeasurement // Buckets with objects in flight

    [sampling mode API](https://www.tensorflow.org/guide/profiler#profiling_apis).

*   TFLite Profiler for Android is available. See the detailed
    [guide](https://www.tensorflow.org/lite/performance/measurement#trace_tensorflow_lite_internals_in_android)
    to learn more.

*   TensorFlow pip packages are now built with CUDA11 and cuDNN 8.0.2.

## Breaking Changes

*   TF Core: