*/
  private static void selectInPlace(int required, double[] array, int from, int to) {
    // If we are looking for the least element in the range, we can just do a linear search for it.
    // (We will hit this whenever we are doing quantile interpolation: our first selection finds
    // the lower value, our second one finds the upper value by looking for the next least element.)

   *
   * <p>Due to the property that values in a BiMap are unique, this will tend to execute in
   * faster-than-linear time.
   *
   * @param value the object to search for in the values of this BiMap
   * @return true if a mapping exists from a key to the specified value
   */
  @Override
  public boolean containsValue(@CheckForNull Object value) {

   *
   * <p>Due to the property that values in a BiMap are unique, this will tend to execute in
   * faster-than-linear time.
   *
   * @param value the object to search for in the values of this BiMap
   * @return true if a mapping exists from a key to the specified value
   */
  @Override
  public boolean containsValue(@CheckForNull Object value) {

          @ParametricNullness E key,
          List<? extends E> list,
          int foundIndex) {
        // Of course, we have to use binary search to find the precise
        // breakpoint...
        int lower = 0;
        int upper = foundIndex;
        // Of course, we have to use binary search to find the precise breakpoint...
        // Everything between lower and upper inclusive compares at <= 0.
        while (lower < upper) {

   * there would exist a perfect hash function mapping threads to
   * slots that eliminates collisions. When we reach capacity, we
   * search for this mapping by randomly varying the hash codes of
   * colliding threads.  Because search is random, and collisions
   * only become known via CAS failures, convergence can be slow,
   * and because threads are typically not bound to CPUS forever,

   * there would exist a perfect hash function mapping threads to
   * slots that eliminates collisions. When we reach capacity, we
   * search for this mapping by randomly varying the hash codes of
   * colliding threads.  Because search is random, and collisions
   * only become known via CAS failures, convergence can be slow,
   * and because threads are typically not bound to CPUS forever,

   *
   * <pre>{@code
   * Iterables.limit(Traverser.forGraph(graph).breadthFirst(node), maxNumberOfNodes);
   * }</pre>
   *
   * <p>See <a href="https://en.wikipedia.org/wiki/Breadth-first_search">Wikipedia</a> for more
   * info.
   *
   * @throws IllegalArgumentException if {@code startNode} is not an element of the graph
   */
  public final Iterable<N> breadthFirst(N startNode) {

   * i, i + target.length)} contains exactly the same elements as {@code target}.
   *
   * @param array the array to search for the sequence {@code target}
   * @param target the array to search for as a sub-sequence of {@code array}
   */
  public static int indexOf(byte[] array, byte[] target) {
    checkNotNull(array, "array");
    checkNotNull(target, "target");

   *
   * <p>Note that this always returns {@code -1} when {@code target} contains {@code NaN}.
   *
   * @param array the array to search for the sequence {@code target}
   * @param target the array to search for as a sub-sequence of {@code array}
   */
  public static int indexOf(float[] array, float[] target) {
    checkNotNull(array, "array");
    checkNotNull(target, "target");

   *       </ul>
   *   <li>another java.util.Set delegate implementation. In most modern JDKs, normal java.util hash
   *       collections intelligently fall back to a binary search tree if hash table collisions are
   *       detected. Rather than going to all the trouble of reimplementing this ourselves, we
   *       simply switch over to use the JDK implementation wholesale if probable hash flooding is