eager-friendly training step inside of a `tf.function`. With this
    *   All input types are converted to `Dataset`.
    *   The path assumes there is always a distribution strategy. when
        distribution strategy is not specified the path uses a no-op
        distribution strategy.
    *   The training step is wrapped in `tf.function` unless `run_eagerly=True`
        is set in compile. The single path execution code does not yet support

    }

    @Test
    void testDistributionManagementStatus() throws Exception {
        SimpleProblemCollector result = validate("distribution-management-status.xml");

        assertViolations(result, 0, 1, 0);

        assertTrue(result.getErrors().get(0).contains("distributionManagement.status"));
    }

    @Test

   * population, this is an estimator of the population standard deviation of the population which
   * is less biased than {@link #populationStandardDeviation()} (the unbiased estimator depends on
   * the distribution). The count must be greater than one.
   *
   * <p>This is not guaranteed to return zero when the dataset consists of the same value multiple

        List<SMBMessage> pendingOps = failedChannel.getPendingOperations();
        
        if (pendingOps.isEmpty()) {
            return;
        }
        
        log.info("Redistributing {} pending operations from failed channel", 
            pendingOps.size());
        
        // Redistribute to healthy channels
        for (SMBMessage op : pendingOps) {
            try {

          </association>
        </field>
        <field xdoc.separator="blank">
          <name>distributionManagement</name>
          <version>4.0.0+</version>
          <description>Distribution information for a project that enables deployment of the site
            and artifacts to remote web servers and repositories respectively.</description>
          <association>
            <type>DistributionManagement</type>

 * constant time operations. Expected in the hashtable sense (depends on the hash function doing a
 * good job of distributing the elements to the buckets to a distribution not far from uniform), and
 * amortized since some operations can trigger a hash table resize.
 *
 * <p>Unlike {@code java.util.HashSet}, iteration is only proportional to the actual {@code size()},

   * </ul>
   *
   * <p>Note that if your only goal is to turn a well-distributed {@code int} (such as a random
   * number or hash code) into a well-distributed nonnegative number, the most even distribution is
   * achieved not by this method or other absolute value methods, but by {@code x &
   * Integer.MAX_VALUE}.
   *
   * @since 33.5.0
   */
  public static int saturatedAbs(int x) {

   * population, this is an estimator of the population standard deviation of the population which
   * is less biased than {@link #populationStandardDeviation()} (the unbiased estimator depends on
   * the distribution). The count must be greater than one.
   *
   * <p>This is not guaranteed to return zero when the dataset consists of the same value multiple

			actualSha256 := actualH.Sum(nil)

			z := obj.(*erasureServerPools)
			er := z.serverPools[0].getHashedSet(object)

			disks := er.getDisks()
			distribution := hashOrder(pathJoin(bucket, object), nDisks)
			shuffledDisks := shuffleDisks(disks, distribution)

			// remove last data shard
			err = removeAll(pathJoin(shuffledDisks[11].String(), bucket, object))
			if err != nil {

## Dependencies

### Added
- github.com/distribution/reference: [v0.5.0](https://github.com/distribution/reference/tree/v0.5.0)

### Changed
- github.com/coredns/corefile-migration: [v1.0.20 → v1.0.21](https://github.com/coredns/corefile-migration/compare/v1.0.20...v1.0.21)
- github.com/docker/distribution: [v2.8.2+incompatible → v2.8.1+incompatible](https://github.com/docker/distribution/compare/v2.8.2...v2.8.1)