public void testLog10TrivialOnPowerOfTen() {
    int x = 1000000;
    for (RoundingMode mode : ALL_ROUNDING_MODES) {
      assertEquals(6, IntMath.log10(x, mode));
    }
  }

  // Simple test to cover sqrt(0) for all types and all modes.
  @GwtIncompatible // sqrt
  public void testSqrtZeroAlwaysZero() {
    for (RoundingMode mode : ALL_ROUNDING_MODES) {
      assertEquals(0, IntMath.sqrt(0, mode));

          suite.addTest(new GeneratedMonitorTest(method, scenario, fair, null, expectedOutcome));
        }
      }
    }
  }

  /** A guard that encapsulates a simple, mutable boolean flag. */
  static class FlagGuard extends Monitor.Guard {

    private boolean satisfied;

    protected FlagGuard(Monitor monitor) {
      super(monitor);
    }

    @Override

		if metadata.Deleted || metadata.Size == 0 {
			parities[index] = totalShards / 2
		} else if metadata.TransitionStatus == lifecycle.TransitionComplete {
			// For tiered objects, read quorum is N/2+1 to ensure simple majority on xl.meta.
			// It is not equal to EcM because the data integrity is entrusted with the warm tier.
			// However, we never go below EcM, in case of a EcM=EcN setup.

   * Often called multiple times (that is, both when the inputs complete and when the output
   * completes).
   *
   * <p>This is similar to our proposed {@code afterCommit} method but not quite the same. See the
   * description of CL 265462958.
   */
  // TODO(user): Write more tests for memory retention.
  @ForOverride

 *       at this goal. Important note: it may be easy to achieve the theoretical minimum collision
 *       rate when using completely <i>random</i> sample input. The true test of a hash function is
 *       how it performs on representative real-world data, which tends to contain many hidden
 *       patterns and clumps. The goal of a good hash function is to stamp these patterns out as

   * @throws IllegalStateException if the dataset is empty
   */
  public double populationCovariance() {
    checkState(count() != 0);
    return sumOfProductsOfDeltas / count();
  }

  /**
   * Returns the sample covariance of the values. The count must be greater than one.
   *
   * <p>This is not guaranteed to return zero when the dataset consists of the same pair of values
   * multiple times, due to numerical errors.
   *

func getXLMetaBytes(totalParts int) []byte {
	xlSampleMeta := getSampleXLMeta(totalParts)
	xlMetaBytes, err := json.Marshal(xlSampleMeta)
	if err != nil {
		panic(err)
	}
	return xlMetaBytes
}

// Returns sample xlMetaV1Object{} for number of parts.
func getSampleXLMeta(totalParts int) xlMetaV1Object {
	xlMeta := newTestXLMetaV1()
	// Number of checksum info == total parts.
	xlMeta.Erasure.Checksums = make([]ChecksumInfo, totalParts)

                    return (Collection<String>[]) new Collection<?>[length];
                  }

                  @Override
                  public SampleElements<Entry<String, Collection<String>>> samples() {
                    return new SampleElements<>(
                        mapEntry("a", (Collection<String>) ImmutableSortedSet.of("alex")),

   * elements, and each pair of corresponding elements is equivalent according to {@code this}. Null
   * iterables are equivalent to one another.
   *
   * <p>Note that this method performs a similar function for equivalences as {@link
   * com.google.common.collect.Ordering#lexicographical} does for orderings.
   *
   * <p>The returned object is serializable if this object is serializable.
   *
   * @since 10.0

 *
 * <p>There are three primary interfaces provided to represent graphs. In order of increasing
 * complexity they are: {@link Graph}, {@link ValueGraph}, and {@link Network}. You should generally
 * prefer the simplest interface that satisfies your use case. See the <a
 * href="https://github.com/google/guava/wiki/GraphsExplained#choosing-the-right-graph-type">
 * "Choosing the right graph type"</a> section of the Guava User Guide for more details.