assertTrue(DcerpcError.DCERPC_FAULT_CANT_PERFORM >= 0 && DcerpcError.DCERPC_FAULT_CANT_PERFORM <= 0x0000FFFF,
                "DCERPC_FAULT_CANT_PERFORM should be in standard range");
        assertTrue(DcerpcError.DCERPC_FAULT_NDR >= 0 && DcerpcError.DCERPC_FAULT_NDR <= 0x0000FFFF,
                "DCERPC_FAULT_NDR should be in standard range");

     *         no versions matched the requested range.
     */
    @Nonnull
    List<Version> getVersions();

    /**
     * Gets the lowest version matching the requested range.
     *
     * @return An Optional containing the lowest matching version, or empty Optional if no versions
     *         matched the requested range
     */
    @Nonnull
    default Optional<Version> getLowestVersion() {
        return getVersions().isEmpty()

  }

  @Override
  public abstract @Nullable Range<C> rangeContaining(C value);

  @Override
  public boolean isEmpty() {
    return asRanges().isEmpty();
  }

  @Override
  public void add(Range<C> range) {
    throw new UnsupportedOperationException();
  }

  @Override
  public void remove(Range<C> range) {
    throw new UnsupportedOperationException();
  }

  @Override

constraints.NotBlank.message = {item} não pode estar em branco.
constraints.NotEmpty.message = {item} não pode estar vazio.
constraints.ParametersScriptAssert.message = A expressão de script "{script}" não é verdadeira.
constraints.Range.message = {item} deve estar entre {min} e {max}.
constraints.SafeHtml.message = {item} contém conteúdo HTML perigoso.
constraints.ScriptAssert.message = A expressão de script "{script}" não é verdadeira.

to the network can execute arbitrary code with the privileges of the user
running the `tf.train.Server`.

## Untrusted inputs during training and prediction

TensorFlow supports a wide range of input data formats. For example it can
process images, audio, videos, and text. There are several modules specialized
in taking those formats, modifying them, and/or converting them to intermediate

	o = msgp.Require(b, z.Msgsize())
	o = msgp.AppendMapHeader(o, uint32(len(z)))
	for zb0007, zb0008 := range z {
		o = msgp.AppendString(o, zb0007)
		// map header, size 2
		// string "Bins"
		o = append(o, 0x82, 0xa4, 0x42, 0x69, 0x6e, 0x73)
		o = msgp.AppendArrayHeader(o, uint32(24))
		for zb0009 := range zb0008.Bins {
			o, err = zb0008.Bins[zb0009].MarshalMsg(o)
			if err != nil {

	for range b.N {
		for range SplitSeq(benchInputHard, nil) {
		}
	}
}

func BenchmarkSplitSeqSingleByteSeparator(b *testing.B) {
	sep := []byte("/")
	for range b.N {
		for range SplitSeq(benchInputHard, sep) {
		}
	}
}

func BenchmarkSplitSeqMultiByteSeparator(b *testing.B) {
	sep := []byte("hello")
	for range b.N {
		for range SplitSeq(benchInputHard, sep) {
		}
	}
}

	if len(foreignKeys) == 1 {
		for idx, r := range foreignValues {
			queryValues[idx] = r[0]
		}

		return clause.Column{Table: table, Name: foreignKeys[0]}, queryValues
	}

	columns := make([]clause.Column, len(foreignKeys))
	for idx, key := range foreignKeys {
		columns[idx] = clause.Column{Table: table, Name: key}
	}

	for idx, r := range foreignValues {
		queryValues[idx] = r
	}

	m.Set(usageDeleteMarkersCount, float64(clusterDeleteMarkersCount))
	m.Set(usageBucketsCount, float64(clusterBuckets))
	for k, v := range clusterObjectSizesHistogram {
		m.Set(usageSizeDistribution, float64(v), "range", k)
	}
	for k, v := range clusterVersionsHistogram {
		m.Set(usageVersionCountDistribution, float64(v), "range", k)
	}

	return nil
}

const (
	usageBucketQuotaTotalBytes = "quota_total_bytes"

			// Save Belongs To associations
			for _, rel := range db.Statement.Schema.Relationships.BelongsTo {
				if v, ok := selectColumns[rel.Name]; (ok && !v) || (!ok && restricted) {
					continue
				}

				setupReferences := func(obj reflect.Value, elem reflect.Value) {
					for _, ref := range rel.References {
						if !ref.OwnPrimaryKey {