// count == 0
    segment.setTableEntryForTesting(index, entry);
    assertNull(segment.get(key, hash));
    assertFalse(segment.containsKey(key, hash));
    assertFalse(segment.containsValue(value));

    // count == 1
    segment.count++;
    assertSame(value, segment.get(key, hash));
    assertTrue(segment.containsKey(key, hash));
    assertTrue(segment.containsValue(value));

    // count == 0
    segment.setTableEntryForTesting(index, entry);
    assertNull(segment.get(key, hash));
    assertFalse(segment.containsKey(key, hash));
    assertFalse(segment.containsValue(value));

    // count == 1
    segment.count++;
    assertSame(value, segment.get(key, hash));
    assertTrue(segment.containsKey(key, hash));
    assertTrue(segment.containsValue(value));

    return (bits + 31) & ~31;
  }

  /**
   * Returns a hash function which computes its hash code by concatenating the hash codes of the
   * underlying hash functions together. This can be useful if you need to generate hash codes of a
   * specific length.
   *
   * <p>For example, if you need 1024-bit hash codes, you could join two {@link Hashing#sha512} hash

   * objects per entry.  Instead we use an open-addressed hash table.  This design is basically
   * equivalent to RegularImmutableSet, save that instead of having a hash table containing the
   * elements directly and null for empty positions, we store indices of the keys in the hash table,
   * and ABSENT for empty positions.  We then look up the keys in alternatingKeysAndValues.
   *

  public boolean equals(@Nullable Object object) {
    return Maps.equalsImpl(this, object);
  }

  @Override
  public int hashCode() {
    // not caching hash code since it could change if map values are mutable
    // in a way that modifies their hash codes
    return entrySet().hashCode();
  }

  @Override
  public String toString() {
    return Maps.toStringImpl(this);
  }

    assertEquals(actual + " should hash like " + expected, expected.hashCode(), actual.hashCode());
  }

  public void testHashCodeForBooleanOperations() {
    Predicate<@Nullable Integer> p1 = Predicates.isNull();
    Predicate<@Nullable Integer> p2 = isOdd();

    // Make sure that hash codes are not computed per-instance.

    succeedsInMultimap(entry.getPredecessorInMultimap(), entry.getSuccessorInMultimap());
  }

  /**
   * LinkedHashMultimap entries are in no less than three coexisting linked lists: a bucket in the
   * hash table for a {@code Set<V>} associated with a key, the linked list of insertion-ordered
   * entries in that {@code Set<V>}, and the linked list of entries in the LinkedHashMultimap as a
   * whole.
   */
  @VisibleForTesting

        return this.getCount() == that.getCount()
            && Objects.equal(this.getElement(), that.getElement());
      }
      return false;
    }

    /**
     * Return this entry's hash code, following the behavior specified in {@link
     * Multiset.Entry#hashCode}.
     */
    @Override
    public int hashCode() {
      E e = getElement();

   * exists, a CAS is tried.  Retries proceed by "double hashing",
   * using a secondary hash (Marsaglia XorShift) to try to find a
   * free slot.
   *
   * The table size is capped because, when there are more threads
   * than CPUs, supposing that each thread were bound to a CPU,
   * there would exist a perfect hash function mapping threads to
   * slots that eliminates collisions. When we reach capacity, we

    /**
     * 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