* numbers of keys and values without rehashing.
   *
   * @param expectedKeys the expected number of distinct keys
   * @param expectedValuesPerKey the expected average number of values per key
   * @throws IllegalArgumentException if {@code expectedKeys} or {@code expectedValuesPerKey} is
   *     negative
   */
  public static <K extends @Nullable Object, V extends @Nullable Object>

import java.util.stream.DoubleStream;
import java.util.stream.IntStream;
import java.util.stream.LongStream;
import javax.annotation.CheckForNull;

/**
 * A bundle of statistical summary values -- sum, count, mean/average, min and max, and several
 * forms of variance -- that were computed from a single set of zero or more floating-point values.
 *
 * <p>There are two ways to obtain a {@code Stats} instance:
 *
 * <ul>

   * Creates a {@code RateLimiter} with the specified stable throughput, given as "permits per
   * second" (commonly referred to as <i>QPS</i>, queries per second).
   *
   * <p>The returned {@code RateLimiter} ensures that on average no more than {@code
   * permitsPerSecond} are issued during any given second, with sustained requests being smoothly
   * spread over each second. When the incoming request rate exceeds {@code permitsPerSecond} the

    // twice as many reads and writes. But benchmarking shows that they usually perform better than
    // Dolphin. Reversal is about as good as Successive on average, and it is much simpler,
    // especially since we already have a `reverse` method.
    checkNotNull(array);
    checkPositionIndexes(fromIndex, toIndex, array.length);
    if (array.length <= 1) {
      return;

}

// ServerHTTPAPIStats holds total number of HTTP operations from/to the server,
// including the average duration the call was spent.
type ServerHTTPAPIStats struct {
	APIStats map[string]int `json:"apiStats"`
}

// ServerHTTPStats holds all type of http operations performed to/from the server
// including their average execution time.
type ServerHTTPStats struct {

- This PR will leverage subtests on the existing table tests for the scheduler units. ([#63665](https://github.com/kubernetes/kubernetes/pull/63665), [@xchapter7x](https://github.com/xchapter7x))

			// Experiments (using IndexPeriodic) suggest
			// the cutover is about 16 byte skips.
			// TODO: if large prefixes of sep are matching
			// we should cutover at even larger average skips,
			// because Equal becomes that much more expensive.
			// This code does not take that effect into account.
			j := bytealg.IndexRabinKarp(s[i:], sep)
			if j < 0 {
				return -1
			}

     * traverse. When nodes would otherwise be changed, new nodes are created to replace them. This
     * works well for hash tables since the bin lists tend to be short. (The average length is less
     * than two.)
     *
     * Read operations can thus proceed without locking, but rely on selected uses of volatiles to

     * traverse. When nodes would otherwise be changed, new nodes are created to replace them. This
     * works well for hash tables since the bin lists tend to be short. (The average length is less
     * than two.)
     *
     * Read operations can thus proceed without locking, but rely on selected uses of volatiles to

- Scheduler volumebinding: leverage PreFilterResult to reduce down to only eligible node(s) for pod with bound claim(s) to local PersistentVolume(s) ([#109877](https://github.com/kubernetes/kubernetes/pull/109877), [@yibozhuang](https://github.com/yibozhuang)) [SIG Scheduling, Storage...