func shuffleWithDist[T any](input []T, distribution []int) []T {
	if distribution == nil {
		return input
	}
	shuffled := make([]T, len(input))
	for index := range input {
		blockIndex := distribution[index]
		shuffled[blockIndex-1] = input[index]
	}
	return shuffled
}

// Return shuffled partsMetadata depending on distribution.
func shufflePartsMetadata(partsMetadata []FileInfo, distribution []int) []FileInfo {

    for (int attempts = 0; attempts < reduceIterationsIfGwt(1000); attempts++) {
      ArrayList<Integer> elements = createOrderedList(10);
      shuffle(elements, random);
      MinMaxPriorityQueue<Integer> queue = MinMaxPriorityQueue.create(elements);
      shuffle(elements, random);
      for (Integer element : elements) {
        assertThat(queue.remove(element)).isTrue();
        assertIntact(queue);

    for (int attempts = 0; attempts < reduceIterationsIfGwt(1000); attempts++) {
      ArrayList<Integer> elements = createOrderedList(10);
      shuffle(elements, random);
      MinMaxPriorityQueue<Integer> queue = MinMaxPriorityQueue.create(elements);
      shuffle(elements, random);
      for (Integer element : elements) {
        assertThat(queue.remove(element)).isTrue();
        assertIntact(queue);

			NextCycle: oldCache.Info.NextCycle,
		},
		Cache: make(map[string]dataUsageEntry, len(oldCache.Cache)),
	}

	// Put all buckets into channel.
	bucketCh := make(chan BucketInfo, len(buckets))

	// Shuffle buckets to ensure total randomness of buckets, being scanned.
	// Otherwise same set of buckets get scanned across erasure sets always.
	// at any given point in time. This allows different buckets to be scanned

pkg math, func RoundToEven(float64) float64
pkg math/big, const MaxBase = 62
pkg math/big, method (*Float) Sqrt(*Float) *Float
pkg math/big, method (*Int) CmpAbs(*Int) int
pkg math/rand, func Shuffle(int, func(int, int))
pkg math/rand, method (*Rand) Shuffle(int, func(int, int))
pkg net, method (*TCPListener) SyscallConn() (syscall.RawConn, error)
pkg net, method (*UnixListener) SyscallConn() (syscall.RawConn, error)

		}
		buf = xlMetaV2TrimData(buf)

		vers = append(vers, metaCacheEntry{
			name:     "a",
			metadata: buf,
		})
	}

	// Shuffle...
	for i := range 100 {
		rng := rand.New(rand.NewSource(int64(i)))
		rng.Shuffle(len(vers), func(i, j int) {
			vers[i], vers[j] = vers[j], vers[i]
		})
		var entries []chan metaCacheEntry
		for _, v := range vers {
			v.cached = nil

 * randomly ordered data (the probability decreases faster than exponentially in N), but if you are
 * passing in unsanitized user data then a malicious user could force it. A light shuffle of the
 * data using an unpredictable seed should normally be enough to thwart this attack.
 *
 * <p>The time taken to compute multiple quantiles on the same dataset using {@link Scale#indexes

mapreduce.fileoutputcommitter.algorithm.version=2 # Use the latest committer version
mapreduce.job.reduce.slowstart.completedmaps=0.99 # 99% map, then reduce
mapreduce.reduce.shuffle.input.buffer.percent=0.9 # Min % buffer in RAM
mapreduce.reduce.shuffle.merge.percent=0.9 # Minimum % merges in RAM
mapreduce.reduce.speculative=false # Disable speculation for reducing
mapreduce.task.io.sort.factor=999 # Threshold before writing to disk

    for (int i = 0; i < 10; i++) {
      hashCodes.add(HashCode.fromLong(random.nextLong()));
    }
    HashCode hashCode1 = Hashing.combineOrdered(hashCodes);
    Collections.shuffle(hashCodes, random);
    HashCode hashCode2 = Hashing.combineOrdered(hashCodes);

    assertFalse(hashCode1.equals(hashCode2));
  }

  public void testCombineUnordered_empty() {
    assertThrows(

            return null;
          });
    }
    assertEquals(allTasks.size() + 1, barrier.getParties());
    for (int i = 0; i < 1000; i++) {
      Collections.shuffle(allTasks);
      AbstractFuture<String> future = new AbstractFuture<String>() {};
      currentFuture.set(future);
      for (Callable<?> task : allTasks) {