TP_OptimizerRegistrationParams params{
      TP_OPTIMIZER_REGISTRATION_PARAMS_STRUCT_SIZE};
  TP_Optimizer optimizer{TP_OPTIMIZER_STRUCT_SIZE};
  TP_OptimizerConfigs optimizer_configs{TP_OPTIMIZER_CONFIGS_STRUCT_SIZE};
  params.major_version = GO_MAJOR;
  params.minor_version = GO_MINOR;
  params.patch_version = GO_PATCH;
  params.optimizer = &optimizer;
  params.optimizer_configs = &optimizer_configs;

// optimized, meaning we will use additional parities when drives are offline
// to retain parity SLA.
//
// Default is "availability" optimized.
func (sCfg *Config) AvailabilityOptimized() bool {
	ConfigLock.RLock()
	defer ConfigLock.RUnlock()
	if !sCfg.initialized {
		return true
	}
	return sCfg.Optimize == "availability" || sCfg.Optimize == ""
}

    // The most common case is that from is a Reader (like InputStreamReader or StringReader) so
    // take advantage of that.
    if (from instanceof Reader) {
      // optimize for common output types which are optimized to deal with char[]
      if (to instanceof StringBuilder) {
        return copyReaderToBuilder((Reader) from, (StringBuilder) to);
      } else {

import com.google.j2objc.annotations.WeakOuter;
import java.util.Map;
import javax.annotation.CheckForNull;
import org.checkerframework.checker.nullness.qual.Nullable;

/** A {@code RegularImmutableTable} optimized for dense data. */
@GwtCompatible
@Immutable(containerOf = {"R", "C", "V"})
@ElementTypesAreNonnullByDefault
final class DenseImmutableTable<R, C, V> extends RegularImmutableTable<R, C, V> {

  params->optimizer->create_func = nullptr;
  params->optimizer->optimize_func = optimize_func;
  params->optimizer->destroy_func = nullptr;
}

TEST(Grappler, SuccessfulRegistration) {
  auto plugin_init = [](TP_OptimizerRegistrationParams* const params,
                        TF_Status* const status) -> void {
    TF_SetStatus(status, TF_OK, "");
    PopulateDefaultParam(params);

import com.google.j2objc.annotations.WeakOuter;
import java.util.Map;
import javax.annotation.CheckForNull;
import org.checkerframework.checker.nullness.qual.Nullable;

/** A {@code RegularImmutableTable} optimized for dense data. */
@GwtCompatible
@Immutable(containerOf = {"R", "C", "V"})
@ElementTypesAreNonnullByDefault
final class DenseImmutableTable<R, C, V> extends RegularImmutableTable<R, C, V> {

      }

      @Override
      public Set<EndpointPair<N>> edges() {
        if (allowsParallelEdges()) {
          return super.edges(); // Defer to AbstractGraph implementation.
        }

        // Optimized implementation assumes no parallel edges (1:1 edge to EndpointPair mapping).
        return new AbstractSet<EndpointPair<N>>() {
          @Override
          public Iterator<EndpointPair<N>> iterator() {

    assertThat(UnsignedLongs.remainder(0xfffffffffffffffeL, 2)).isEqualTo(0);
    assertThat(UnsignedLongs.remainder(0xfffffffffffffffeL, 5)).isEqualTo(4);
  }

  @GwtIncompatible // Too slow in GWT (~3min fully optimized)
  public void testDivideRemainderEuclideanProperty() {
    // Use a seed so that the test is deterministic:
    Random r = new Random(0L);
    for (int i = 0; i < 1000000; i++) {
      long dividend = r.nextLong();

#### Endpoints

Endpoints have an optimized code path, as they are by far the most frequently updated resource - in a steady cluster, this will often be the *only* change, caused by scale up/down.

    assertThat(UnsignedLongs.remainder(0xfffffffffffffffeL, 2)).isEqualTo(0);
    assertThat(UnsignedLongs.remainder(0xfffffffffffffffeL, 5)).isEqualTo(4);
  }

  @GwtIncompatible // Too slow in GWT (~3min fully optimized)
  public void testDivideRemainderEuclideanProperty() {
    // Use a seed so that the test is deterministic:
    Random r = new Random(0L);
    for (int i = 0; i < 1000000; i++) {
      long dividend = r.nextLong();