* suggestions.
 *
 */
public class SortedCopyBenchmark {
  @Param({"1", "10", "1000", "1000000"})
  int size; // logarithmic triangular

  @Param boolean mutable;

  @Param InputOrder inputOrder;

  enum InputOrder {
    SORTED {
      @Override
      void arrange(List<Integer> list) {
        Collections.sort(list);
      }
    },
    ALMOST_SORTED {

 * suggestions.
 *
 */
public class SortedCopyBenchmark {
  @Param({"1", "10", "1000", "1000000"})
  int size; // logarithmic triangular

  @Param boolean mutable;

  @Param InputOrder inputOrder;

  enum InputOrder {
    SORTED {
      @Override
      void arrange(List<Integer> list) {
        Collections.sort(list);
      }
    },
    ALMOST_SORTED {

  // Another renaming of local device
  const string second_name = "def";
  server_def.set_job_name(second_name);
  server_def.mutable_cluster()->mutable_job(0)->set_name(second_name);
  (*server_def.mutable_cluster()->mutable_job(0)->mutable_tasks())[0] =
      absl::StrCat(second_name, ":",
                   tensorflow::testing::PickUnusedPortOrDie());

  serialized = server_def.SerializeAsString();

        assertEquals(mutable, copy);
      }

      ImmutableRangeSet<Integer> built = builder.build();
      assertEquals(mutable, built);
      assertEquals(ImmutableRangeSet.copyOf(mutable), built);
      assertEquals(mutable.complement(), built.complement());

      for (int i = 0; i <= 11; i++) {
        assertEquals(mutable.contains(i), built.contains(i));
      }

  tensorflow::ClusterDef* cluster_def = server_def.mutable_cluster();
  tensorflow::JobDef* job_def = cluster_def->add_job();
  job_def->set_name(job_name);
  for (int i = 0; i < num_tasks; i++) {
    int port = tensorflow::testing::PickUnusedPortOrDie();
    job_def->mutable_tasks()->insert(
        {i, tensorflow::strings::StrCat("localhost:", port)});
  }
  auto* config = server_def.mutable_default_session_config();

        assertEquals(mutable, copy);
      }

      ImmutableRangeSet<Integer> built = builder.build();
      assertEquals(mutable, built);
      assertEquals(ImmutableRangeSet.copyOf(mutable), built);
      assertEquals(mutable.complement(), built.complement());

      for (int i = 0; i <= 11; i++) {
        assertEquals(mutable.contains(i), built.contains(i));
      }

 * @since 4.0.0
 */
@Experimental
@Consumer
public interface PropertyContributor extends SpiService {
    /**
     * Invoked just before session is created with a mutable map that carries collected user properties so far.
     *
     * @param userProperties The mutable user properties, never {@code null}.
     */
    void contribute(Map<String, String> userProperties);

}

void TF_EnableXLACompilation(TF_SessionOptions* options, unsigned char enable) {
  tensorflow::ConfigProto& config = options->options.config;
  auto* optimizer_options =
      config.mutable_graph_options()->mutable_optimizer_options();
  if (enable) {
    optimizer_options->set_global_jit_level(tensorflow::OptimizerOptions::ON_1);

    // These XLA flags are needed to trigger XLA properly from C (more generally

    /*
      At the moment, this class is totally immutable, and this is in line with thoughts about the
      pre-calculated execution plan that stays the same during the execution.

      If deciding to add mutable state to this class, it should be at least considered to
      separate this into a separate mutable structure.

    */

    private final List<ExecutionPlanItem> planItem;

    }

    void add(Object o) {
      checkNotNull(o);
      if (element == null) {
        this.element = o;
      } else if (extras.isEmpty()) {
        // Replace immutable empty list with mutable list.
        extras = new ArrayList<>(MAX_EXTRAS);
        extras.add(o);
      } else if (extras.size() < MAX_EXTRAS) {
        extras.add(o);
      } else {
        throw multiples(true);