* 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>

 * add(x)} operations <i>do not</i> create objects for the garbage collector to deal with, and for
 * every element added, the garbage collector will have to traverse {@code 1.5} references on
 * average, in the marking phase, not {@code 5.0} as in {@code java.util.HashSet}.
 *
 * <p>If there are no removals, then {@link #iterator iteration} order is the same as insertion
 * order. Any removal invalidates any ordering guarantees.

	Rotational bool
	Metrics    DiskMetrics
	Error      string // carries the error over the network
}

// DiskMetrics has the information about XL Storage APIs
// the number of calls of each API and the moving average of
// the duration of each API.
type DiskMetrics struct {
	LastMinute              map[string]AccElem `json:"apiLatencies,omitempty"`
	APICalls                map[string]uint64  `json:"apiCalls,omitempty"`

            }
        });

        RestTestTransformer transformer = new RestTestTransformer();
        // TODO: instead of flattening the FileTree here leverage FileTree.visit() so we can preserve folder hierarchy in a more robust way
        for (File file : getTestFiles().getFiles()) {
            YAMLParser yamlParser = YAML_FACTORY.createParser(file);

   * 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

labels.searchlog_log_type_search_user_hour=User Count/Hour
labels.searchlog_log_type_search_user_day=User Count/Day
labels.searchlog_log_type_search_reqtimeavg_hour=Request Average Time/Hour
labels.searchlog_log_type_search_reqtimeavg_day=Request Average Time/Day
labels.searchlog_log_type_click_count=Click Count
labels.searchlog_log_type_favorite_count=Favorite Count
labels.searchlog_log_message=Message

 * add(x)} operations <i>do not</i> create objects for the garbage collector to deal with, and for
 * every element added, the garbage collector will have to traverse {@code 1.5} references on
 * average, in the marking phase, not {@code 5.0} as in {@code java.util.HashSet}.
 *
 * <p>If there are no removals, then {@link #iterator iteration} order is the same as insertion
 * order. Any removal invalidates any ordering guarantees.

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>

    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

    // 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;