* by two things.
   *
   * <ul>
   *   <li>This is only called when a waiting thread times out or is interrupted. Both of which
   *       should be rare.
   *   <li>The waiters list should be very short.
   * </ul>
   */
  private void removeWaiter(Waiter node) {
    node.thread = null; // mark as 'deleted'
    restart:
    while (true) {
      Waiter pred = null;
      Waiter curr = waitersField;

 * either of these extremes, {@code Striped} allows the user to trade between required concurrency
 * and memory footprint. For example, if a set of tasks are CPU-bound, one could easily create a
 * very compact {@code Striped<Lock>} of {@code availableProcessors() * 4} stripes, instead of
 * possibly thousands of locks which could be created in a {@code Map<K, Lock>} structure.
 *
 * @author Dimitris Andreou
 * @since 13.0

)

var gzipHandler = func() func(http.Handler) http.HandlerFunc {
	gz, err := gzhttp.NewWrapper(gzhttp.MinSize(1000), gzhttp.CompressionLevel(gzip.BestSpeed))
	if err != nil {
		// Static params, so this is very unlikely.
		logger.Fatal(err, "Unable to initialize server")
	}
	return gz
}()

// Set of handler options as bit flags
type hFlag uint8

const (
	// this flag disables gzip compression of responses

      K key = entry.getKey();
      ImmutableSet.Builder<? extends V> values = (ImmutableSet.Builder<V>) entry.getValue();
      // If orderValuesBy got called at the very end, we may need to do the ImmutableSet to
      // ImmutableSortedSet copy for each of these.
      ImmutableSet<V> set = valueSet(valueComparator, values.build());
      if (!set.isEmpty()) {
        builder.put(key, set);

    }

    assertMoreInvariants(map);
  }

  @SuppressWarnings("CollectionIncompatibleType")
  private void assertEntrySetNotContainsString(Set<Entry<K, V>> entrySet) {
    // Very unlikely that a buggy collection would ever return true. It might accidentally throw.
    assertFalse(entrySet.contains("foo"));
  }

  /**

I'll tell you a bit more about these **concepts** here, and that would hopefully give you the **intuition** you would need to decide how to deploy your API in very different environments, possibly even in **future** ones that don't exist yet.

By considering these concepts, you will be able to **evaluate and design** the best way to deploy **your own APIs**.

 *       hand, is multiple-use, and does implement {@link Iterable}.
 *   <li>Streams offer many features not found here, including {@code min/max}, {@code distinct},
 *       {@code reduce}, {@code sorted}, the very powerful {@code collect}, and built-in support for
 *       parallelizing stream operations.
 *   <li>{@code FluentIterable} contains several features not available on {@code Stream}, which are

    // The Dolphin algorithm is attractive because it does the fewest array reads and writes: each
    // array slot is read and written exactly once. However, it can have very poor memory locality:
    // benchmarking shows it can take 7 times longer than the other two in some cases. The other two
    // do n swaps, minus a delta (0 or 2 for Reversal, gcd(d, n) for Successive), so that's about

   *       simply switch over to use the JDK implementation wholesale if probable hash flooding is
   *       detected, sacrificing the compactness guarantee in very rare cases in exchange for much
   *       more reliable worst-case behavior.
   *   <li>null, if no entries have yet been added to the map
   * </ul>
   */
  private transient @Nullable Object table;

  /**

 *       hand, is multiple-use, and does implement {@link Iterable}.
 *   <li>Streams offer many features not found here, including {@code min/max}, {@code distinct},
 *       {@code reduce}, {@code sorted}, the very powerful {@code collect}, and built-in support for
 *       parallelizing stream operations.
 *   <li>{@code FluentIterable} contains several features not available on {@code Stream}, which are