* 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>

    /** The key of the message: Failed to download an elevate word file. */
    public static final String ERRORS_failed_to_download_elevate_file = "{errors.failed_to_download_elevate_file}";

    /** The key of the message: Failed to upload an elevate word file. */
    public static final String ERRORS_failed_to_upload_elevate_file = "{errors.failed_to_upload_elevate_file}";

import java.util.stream.DoubleStream;
import java.util.stream.IntStream;
import java.util.stream.LongStream;
import org.jspecify.annotations.Nullable;

/**
 * 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>

    // 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;

page.request.header.max.fetch.size=1000
# Maximum number of scheduled job records to fetch per page.
page.scheduled.job.max.fetch.size=100
# Maximum number of elevate word records to fetch per page.
page.elevate.word.max.fetch.size=1000
# Maximum number of bad word records to fetch per page.
page.bad.word.max.fetch.size=1000
# Maximum number of dictionary records to fetch per page.

}

// 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 {

    static final int MAX_RUN_MULTIPLIER = 13;

    /**
     * Checks the whole hash table for poor hash distribution. Takes O(n) in the worst case, O(n /
     * log n) on average.
     *
     * <p>The online hash flooding detecting in RegularSetBuilderImpl.add can detect e.g. many
     * exactly matching hash codes, which would cause construction to take O(n^2), but can't detect

			// 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
			}

     */
    Integer getPageScheduledJobMaxFetchSizeAsInteger();

    /**
     * Get the value for the key 'page.elevate.word.max.fetch.size'. <br>
     * The value is, e.g. 1000 <br>
     * comment: Maximum number of elevate word records to fetch per page.
     * @return The value of found property. (NotNull: if not found, exception but basically no way)
     */

     * 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