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 {

            EndpointPair.ordered(1, 2))
        .inOrder();
  }

  /**
   * Populates the graph with nodes and edges in a star shape with node `1` in the middle.
   *
   * <p>Note that the edges are added in a shuffled order to properly test the effect of the
   * insertion order.
   */
  private void populateStarShapedGraph() {
    putEdge(2, 1);
    putEdge(1, 4);
    putEdge(1, 3);
    putEdge(5, 1);

        .inOrder();
  }

  /**
   * Populates the graph with nodes and edges in a star shape with node `1` in the middle.
   *
   * <p>Note that the edges are added in a shuffled order to properly test the effect of the
   * insertion order.
   */
  private void populateTShapedGraph() {
    putEdge(2, 1);
    putEdge(1, 4);
    putEdge(1, 3);
    putEdge(1, 2); // Duplicate

        .inOrder();
  }

  /**
   * Populates the graph with nodes and edges in a star shape with node `1` in the middle.
   *
   * <p>Note that the edges are added in a shuffled order to properly test the effect of the
   * insertion order.
   */
  private void populateTShapedGraph() {
    putEdge(2, 1);
    putEdge(1, 4);
    putEdge(1, 3);
    putEdge(1, 2); // Duplicate

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) {

    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);

      indexes.add(index);
      expectedBuilder.put(index, expectedLargeDatasetPercentile(index));
    }
    Random random = new Random(770683168895677741L);
    Collections.shuffle(indexes, random);
    assertThat(percentiles().indexes(Ints.toArray(indexes)).compute(PSEUDORANDOM_DATASET))
        .comparingValuesUsing(QUANTILE_CORRESPONDENCE)
        .containsExactlyEntriesIn(expectedBuilder.buildOrThrow());