- [Action Required](#action-required)
    - [Other notable changes](#other-notable-changes)
- [v1.8.14](#v1814)
  - [Downloads for v1.8.14](#downloads-for-v1814)
    - [Client Binaries](#client-binaries-1)
    - [Server Binaries](#server-binaries-1)
    - [Node Binaries](#node-binaries-1)
  - [Changelog since v1.8.13](#changelog-since-v1813)
    - [Other notable changes](#other-notable-changes-1)
- [v1.8.13](#v1813)

        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));
      }

    } catch (NullPointerException expected) {
    }
  }

  public void testToTable() {
    Collector<Cell<String, String, Integer>, ?, Table<String, String, Integer>> collector =
        TableCollectors.toTable(
            Cell::getRowKey, Cell::getColumnKey, Cell::getValue, HashBasedTable::create);
    BiPredicate<Table<String, String, Integer>, Table<String, String, Integer>> equivalence =
        pairwiseOnResultOf(Table::cellSet);

		mountPointCache.Store(path, false)
		return false
	}

	switch windows.GetDriveType(&wvolume[0]) {
	case windows.DRIVE_FIXED, windows.DRIVE_REMOVABLE, windows.DRIVE_REMOTE, windows.DRIVE_RAMDISK:
		// Recognize "fixed", "removable", "remote" and "ramdisk" drives as proper drives
		// which can be treated as an actual mount-point, rest can be ignored.

  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();

 * @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);

    - [Other notable changes](#other-notable-changes)
- [v1.6.12](#v1612)
  - [Downloads for v1.6.12](#downloads-for-v1612)
    - [Client Binaries](#client-binaries-1)
    - [Server Binaries](#server-binaries-1)
    - [Node Binaries](#node-binaries-1)
  - [Changelog since v1.6.11](#changelog-since-v1611)
    - [Other notable changes](#other-notable-changes-1)
- [v1.6.11](#v1611)

    }

    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);
      }
    }

      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;

    private final Map<String, ExecutionPlanItem> lastMojoExecutionForAllPhases;

}

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