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 {

	if err != nil {
		removeRoots(disks)
		t.Fatal(err)
	}
	defer removeRoots(disks)
	z := objLayer.(*erasureServerPools)
	testShuffleDisks(t, z)
}

// Test shuffleDisks which returns shuffled slice of disks for their actual distribution.
func testShuffleDisks(t *testing.T, z *erasureServerPools) {
	disks := z.serverPools[0].GetDisks(0)()
	distribution := []int{16, 14, 12, 10, 8, 6, 4, 2, 1, 3, 5, 7, 9, 11, 13, 15}

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

    *   `tf.data.Dataset.zip` now supports Python-style zipping, i.e. `Dataset.zip(a, b, c)`.
    * `tf.data.Dataset.shuffle` now supports `tf.data.UNKNOWN_CARDINALITY` When doing a "full shuffle" using  `dataset = dataset.shuffle(dataset.cardinality())`. But remember, a "full shuffle" will load the full dataset into memory so that it can be shuffled, so make sure to only use this with small datasets or datasets of small objects (like filenames).

*   `tf.math`

		}
		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

			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

		// Run for a number of times, shuffling the input to ensure that output is consistent.
		for i := range 10 {
			t.Run(fmt.Sprintf("test-%d-%s-run-%d", testID, tt.name, i), func(t *testing.T) {
				if i > 0 {
					rng.Shuffle(len(tt.m), func(i, j int) {
						tt.m[i], tt.m[j] = tt.m[j], tt.m[i]
					})
				}
				gotSelected, gotOk := tt.m.resolve(&tt.r)
				if gotOk != tt.wantOk {

		askDisks = len(disks) // use all available drives
	}

	// However many we ask, versions must exist on ~50%
	listingQuorum := (askDisks + 1) / 2

	if askDisks > 0 && len(disks) > askDisks {
		rand.Shuffle(len(disks), func(i, j int) {
			disks[i], disks[j] = disks[j], disks[i]
		})
		fallbackDisks = disks[askDisks:]
		disks = disks[:askDisks]
	}

	// How to resolve results.

	. "gorm.io/gorm/utils/tests"
)

func TestMigrate(t *testing.T) {
	allModels := []interface{}{&User{}, &Account{}, &Pet{}, &Company{}, &Toy{}, &Language{}, &Tools{}, &Man{}}
	rand.Seed(time.Now().UnixNano())
	rand.Shuffle(len(allModels), func(i, j int) { allModels[i], allModels[j] = allModels[j], allModels[i] })
	DB.Migrator().DropTable("user_speaks", "user_friends", "ccc")

	if err := DB.Migrator().DropTable(allModels...); err != nil {

	}
	return rand.New(rand.NewSource(seed)).Intn(maxN-minN) + minN
}

// Randomizes the order of bytes in the byte array
// using Knuth Fisher-Yates shuffle algorithm.
func randomizeBytes(s []byte, seed int64) []byte {
	// special value -1 means no explicit seeding.
	if seed != -1 {
		rand.Seed(seed)
	}
	n := len(s)
	var j int