Multiset<?> multisetToModify, Multiset<?> multisetToRetain) {
    return retainOccurrencesImpl(multisetToModify, multisetToRetain);
  }

  /** Delegate implementation which cares about the element type. */
  private static <E extends @Nullable Object> boolean retainOccurrencesImpl(
      Multiset<E> multisetToModify, Multiset<?> occurrencesToRetain) {
    checkNotNull(multisetToModify);

      Multiset<?> multisetToModify, Multiset<?> multisetToRetain) {
    return retainOccurrencesImpl(multisetToModify, multisetToRetain);
  }

  /** Delegate implementation which cares about the element type. */
  private static <E extends @Nullable Object> boolean retainOccurrencesImpl(
      Multiset<E> multisetToModify, Multiset<?> occurrencesToRetain) {
    checkNotNull(multisetToModify);

            buildContext
                    .getResult()
                    .addBuildSummary(new BuildFailure(mavenProject, clock.execTime(), clock.wallTime(), t));

            // notify listeners about "soft" project build failures only
            if (t instanceof Exception exception && !(t instanceof RuntimeException)) {
                eventCatapult.fire(ExecutionEvent.Type.ProjectFailed, session, null, exception);

    // being resumed. To avoid a flake in this scenario, calculate how long that thread actually
    // waited and assert based on that time. Empirically, the race where the thread ends up waiting
    // for 5.5 seconds happens about 2% of the time.
    boolean longWait = NANOSECONDS.toSeconds(thread.timeSpentBlocked) >= 5;
    // Count how long it actually took to return; we'll accept any number between the expected delay

 * indexes} is generally less than the total time taken to compute each of them separately, and
 * sometimes much less. For example, on a large enough dataset, computing the 90th and 99th
 * percentiles together takes about 55% as long as computing them separately.
 *
 * <p>When calling {@link ScaleAndIndex#compute} (in {@linkplain ScaleAndIndexes#compute either

      for (LockGraphNode acquiredLock : acquiredLocks) {
        checkAcquiredLock(policy, acquiredLock);
      }
    }

    /**
     * Checks the acquisition-ordering between {@code this}, which is about to be acquired, and the
     * specified {@code acquiredLock}.
     *
     * <p>When this method returns, the {@code acquiredLock} should be in either the {@code

    checkNotNull(strategy);

    if (expectedInsertions == 0) {
      expectedInsertions = 1;
    }
    /*
     * TODO(user): Put a warning in the javadoc about tiny fpp values, since the resulting size
     * is proportional to -log(p), but there is not much of a point after all, e.g.
     * optimalM(1000, 0.0000000000000001) = 76680 which is less than 10kb. Who cares!
     */

   *     {@code toIndex > fromIndex}
   * @since 32.0.0
   */
  public static void rotate(double[] array, int distance, int fromIndex, int toIndex) {
    // See Ints.rotate for more details about possible algorithms here.
    checkNotNull(array);
    checkPositionIndexes(fromIndex, toIndex, array.length);
    if (array.length <= 1) {
      return;
    }

    int length = toIndex - fromIndex;

	unique, ok := columnType.Unique()
	if !ok || field.PrimaryKey {
		return nil // skip primary key
	}
	// By default, ColumnType's Unique is not affected by UniqueIndex, so we don't care about UniqueIndex.
	return m.RunWithValue(value, func(stmt *gorm.Statement) error {
		// We're currently only receiving boolean values on `Unique` tag,
		// so the UniqueConstraint name is fixed

  //     then total_size is the cumulative number
  //     of dimensions of all shapes in the list.
  // (5) Otherwise, total_size is undefined.
  int64_t total_size;
} TF_AttrMetadata;

// Returns metadata about the value of the attribute `attr_name` of `oper`.
TF_CAPI_EXPORT extern TF_AttrMetadata TF_OperationGetAttrMetadata(
    TF_Operation* oper, const char* attr_name, TF_Status* status);