func i64tor(x int64Val) ratVal   { return ratVal{newRat().SetInt64(int64(x))} }
func i64tof(x int64Val) floatVal { return floatVal{newFloat().SetInt64(int64(x))} }
func itor(x intVal) ratVal       { return ratVal{newRat().SetInt(x.val)} }
func itof(x intVal) floatVal     { return floatVal{newFloat().SetInt(x.val)} }
func rtof(x ratVal) floatVal     { return floatVal{newFloat().SetRat(x.val)} }

		if dst != nil && srcPatch != nil {
			retVal, errMerge := util.MergeAnyWithAny(dst, srcPatch)
			if errMerge != nil {
				return false, fmt.Errorf("function MergeAnyWithAny failed for ApplyClusterMerge: %v", errMerge)
			}

			// Merge the above result with the whole cluster
			merge.Merge(dst, retVal)
		}
	}
	return true, nil
}

  kAsync,
};

enum class DeviceCompileState {
  kUncompiled,
  kCompiling,
  kCompiled,
};

// Creates a single-node graph using the specified `node_def` as the only op
// apart from the arg and retval nodes corresponding to `args` and
// `result_types` respectively.
absl::StatusOr<std::unique_ptr<Graph>> CreateSingleOpGraph(
    const NodeDef& node_def, absl::Span<const XlaArgument> args,

	var retval []certificates.CertificateSigningRequestCondition
	for i, c := range csr.Status.Conditions {
		if c.Type == conditionType {
			retval = append(retval, csr.Status.Conditions[i])
		}
	}
	return retval
}

// getValidationOptions returns the validation options to be

Status TapeOperation::Execute(absl::Span<AbstractTensorHandle*> retvals,
                              int* num_retvals) {
  TF_RETURN_IF_ERROR(parent_op_->Execute(retvals, num_retvals));
  for (int i = 0; i < *num_retvals; i++) {
    // TODO(srbs): Manage refcount of ForwardOperation's inputs/outputs.
    forward_op_.outputs.push_back(retvals[i]);
  }

               absl::Span<AbstractTensorHandle*> retvals, int* num_retvals,
               ForwardOperation* forward_op_, Tape* tape,
               const GradientRegistry& registry) {
  TF_RETURN_IF_ERROR(op_->Execute(retvals, num_retvals));
  for (int i = 0; i < *num_retvals; i++) {
    // TODO(srbs): Manage refcount of ForwardOperation's inputs/outputs.
    forward_op_->outputs.push_back(retvals[i]);
  }

        List<Map<String, Object>> docs = JsonPath.from(response).getList("data");
        int prevVal = 0;
        for (Map<String, Object> doc : docs) {
            int sortValue = Integer.parseInt(doc.get(sortField).toString());
            assertTrue(sortValue >= prevVal);
            prevVal = sortValue;
        }
    }

    @Test
    public void searchTestWithWildcard() throws Exception {

      auto metadata_arg = metadata_proto.add_args();
      metadata_arg->set_kind(
          tensorflow::tpu::TPUCompileMetadataProto::Arg::PARAMETER);
      metadata_arg->set_dtype(dtype);
    }
    // Set up one dummy retval.
    metadata_proto.add_retvals();
    bool use_tuple_args = true;
    std::vector<tensorflow::tpu::ShardingAndIndex> arg_core_mapping;
    std::vector<std::vector<xla::Shape>> per_core_arg_shapes;

  @SuppressWarnings("unchecked")
  @Benchmark
  boolean getIfPresent(int repetitions) {
    boolean retVal = false;
    for (int i = 0; i < repetitions; ++i) {
      retVal &= Enums.getIfPresent(enumType, sampleData[i & 255]).isPresent();
    }
    return retVal;
  }

  private enum SmallEnum {
    X0,
    X1,
    X2
  }

  private enum MediumEnum {
    X0,
    X1,

  @SuppressWarnings("unchecked")
  @Benchmark
  boolean getIfPresent(int repetitions) {
    boolean retVal = false;
    for (int i = 0; i < repetitions; ++i) {
      retVal &= Enums.getIfPresent(enumType, sampleData[i & 255]).isPresent();
    }
    return retVal;
  }

  private enum SmallEnum {
    X0,
    X1,
    X2
  }

  private enum MediumEnum {
    X0,
    X1,