}
	if len(unMarshalXLMeta.Erasure.Distribution) != len(jsoniterXLMeta.Erasure.Distribution) {
		t.Errorf("Expected the size of Erasure Distribution to be %d, but got %d.", len(unMarshalXLMeta.Erasure.Distribution), len(jsoniterXLMeta.Erasure.Distribution))
	} else {
		for i := 0; i < len(unMarshalXLMeta.Erasure.Distribution); i++ {
			if unMarshalXLMeta.Erasure.Distribution[i] != jsoniterXLMeta.Erasure.Distribution[i] {

			// consider the offline disks as consistent.
			continue
		}
		if !meta.Deleted {
			if len(meta.Erasure.Distribution) != len(onlineDisks) {
				// Erasure distribution seems to have lesser
				// number of items than number of online disks.
				inconsistent++
				continue
			}
			if meta.Erasure.Distribution[i] != meta.Erasure.Index {

/**
 * Utilities for benchmarks.
 *
 * <p>In many cases, we wish to vary the order of magnitude of the input as much as we want to vary
 * the input itself, so most methods which generate values use an exponential distribution varying
 * the order of magnitude of the generated values uniformly at random.
 *
 * @author Louis Wasserman
 */
@NullUnmarked
final class MathBenchmarking {
  static final int ARRAY_SIZE = 0x10000;

	return shuffleWithDist[int](parts, distribution)
}

// shuffleDisks - shuffle input disks slice depending on the
// erasure distribution. Return shuffled slice of disks with
// their expected distribution.
func shuffleDisks(disks []StorageAPI, distribution []int) []StorageAPI {
	return shuffleWithDist[StorageAPI](disks, distribution)
}

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