* SipHash-2-4 algorithm</a> using a seed value of {@code k = 00 01 02 ...}.
   *
   * @since 15.0
   */
  public static HashFunction sipHash24() {
    return SipHashFunction.SIP_HASH_24;
  }

  /**
   * Returns a hash function implementing the <a href="https://131002.net/siphash/">64-bit
   * SipHash-2-4 algorithm</a> using the given seed.
   *
   * @since 15.0
   */

import com.google.common.annotations.VisibleForTesting;

/**
 * Implementation of Geoff Pike's fingerprint2011 hash function. See {@link Hashing#fingerprint2011}
 * for information on the behaviour of the algorithm.
 *
 * <p>On Intel Core2 2.66, on 1000 bytes, fingerprint2011 takes 0.9 microseconds compared to
 * fingerprint at 4.0 microseconds and md5 at 4.5 microseconds.
 *

        : ImmutableList.<E>construct(elements.clone());
  }

  /**
   * Returns an immutable list containing the given elements, sorted according to their natural
   * order. The sorting algorithm used is stable, so elements that compare as equal will stay in the
   * order in which they appear in the input.
   *
   * <p>If your data has no duplicates, or you wish to deduplicate elements, use {@code

     * and the arithmetic mean is always higher than the geometric mean.
     *
     * b) this iteration converges to floor(sqrt(x)). In fact, the number of correct digits doubles
     * with each iteration, so this algorithm takes O(log(digits)) iterations.
     *
     * We start out with a double-precision approximation, which may be higher or lower than the
     * true value. Therefore, we perform at least one Newton iteration to get a guess that's

import com.google.common.annotations.VisibleForTesting;

/**
 * Implementation of Geoff Pike's fingerprint2011 hash function. See {@link Hashing#fingerprint2011}
 * for information on the behaviour of the algorithm.
 *
 * <p>On Intel Core2 2.66, on 1000 bytes, fingerprint2011 takes 0.9 microseconds compared to
 * fingerprint at 4.0 microseconds and md5 at 4.5 microseconds.
 *

        });
    }

    /**
     * Generates a hashed ID from the provided URL ID string.
     * Encodes special characters using URL encoding or Base64 encoding as needed,
     * then applies a message digest algorithm to create a unique hash.
     *
     * @param urlId the URL ID string to generate a hash for
     * @return a hashed ID string generated from the input URL ID
     */

      return b;
    } else if (b == 0) {
      return a; // similar logic
    }
    /*
     * Uses the binary GCD algorithm; see http://en.wikipedia.org/wiki/Binary_GCD_algorithm. This is
     * >60% faster than the Euclidean algorithm in benchmarks.
     */
    int aTwos = Long.numberOfTrailingZeros(a);
    a >>= aTwos; // divide out all 2s
    int bTwos = Long.numberOfTrailingZeros(b);

 * k). In benchmarks, this implementation performs at least as well as either implementation, and
 * degrades more gracefully for worst-case input.
 *
 * <p>The implementation does not necessarily use a <i>stable</i> sorting algorithm; when multiple
 * equivalent elements are added to it, it is undefined which will come first in the output.
 *
 * @author Louis Wasserman
 */
@GwtCompatible
final class TopKSelector<
    T extends @Nullable Object> {

 * subtype of {@code Network} that provides methods for adding and removing nodes and edges. If you
 * do not need to mutate a network (e.g. if you write a method than runs a read-only algorithm on
 * the network), you should use the non-mutating {@link Network} interface, or an {@link
 * ImmutableNetwork}.
 *
 * <p>You can create an immutable copy of an existing {@code Network} using {@link

    //     fgh[b]c|[a]de -> fgh[a]c|[b]de. Finally we need to swap [c] with [b]:
    //     fgha[c]|[b]de -> fgha[b]|[c]de. Because these two blocks are the same size, we are done.
    // 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: