* Stopwatch} requires object allocation and additional method calls, which can reduce the accuracy
 * of the elapsed times reported. {@code Stopwatch} is still suitable for logging and metrics where
 * reasonably accurate values are sufficient. If the uncommon case that you need to maximize
 * accuracy, use {@code System.nanoTime()} directly instead.
 *
 * <p>Basic usage:
 *
 * {@snippet :

 * dictionaries. Stemmer override dictionaries allow administrators to define
 * custom stemming rules that override the default stemming behavior for
 * specific terms, improving search accuracy for domain-specific vocabularies.
 */
public class StemmerOverrideService {

    /** Dictionary manager for accessing dictionary files. */
    @Resource
    protected DictionaryManager dictionaryManager;

}

// To collect RX stats during "mc support perf net"
// RXSample holds the RX bytes for the duration between
// the last peer to connect and the first peer to disconnect.
// This is to improve the RX throughput accuracy.
type netPerfRX struct {
	RX                uint64    // RX bytes
	lastToConnect     time.Time // time at which last peer to connect to us
	firstToDisconnect time.Time // time at which the first peer disconnects from us

        assertTrue(StrictMath.pow(2.0, log2 + 1) > d);
      }
    }
  }

  @GwtIncompatible // DoubleMath.log2(double, RoundingMode)
  public void testRoundLog2Half() {
    // We don't expect perfect rounding accuracy.
    for (int exp : asList(-1022, -50, -1, 0, 1, 2, 3, 4, 100, 1022, 1023)) {
      for (RoundingMode mode : asList(HALF_EVEN, HALF_UP, HALF_DOWN)) {
        double x = Math.scalb(Math.sqrt(2) + 0.001, exp);

        assertTrue(StrictMath.pow(2.0, log2 + 1) > d);
      }
    }
  }

  @GwtIncompatible // DoubleMath.log2(double, RoundingMode)
  public void testRoundLog2Half() {
    // We don't expect perfect rounding accuracy.
    for (int exp : asList(-1022, -50, -1, 0, 1, 2, 3, 4, 100, 1022, 1023)) {
      for (RoundingMode mode : asList(HALF_EVEN, HALF_UP, HALF_DOWN)) {
        double x = Math.scalb(Math.sqrt(2) + 0.001, exp);

     * removed in one segment while checking another, in which case the table was never actually
     * empty at any point. (The sum ensures accuracy up through at least 1<<31 per-segment
     * modifications before recheck.)  Method containsValue() uses similar constructions for
     * stability checks.
     */
    long sum = 0L;

     * removed in one segment while checking another, in which case the table was never actually
     * empty at any point. (The sum ensures accuracy up through at least 1<<31 per-segment
     * modifications before recheck.) Method containsValue() uses similar constructions for
     * stability checks.
     */
    long sum = 0L;

    *   `model.add_loss(symbolic_tensor)` should work in ambient eager.
    *   Update metric name to always reflect what the user has given in compile.
        Affects following cases
    *   When name is given as 'accuracy'/'crossentropy'
    *   When an aliased function name is used eg. 'mse'
    *   Removing the `weighted` prefix from weighted metric names.
    *   Allow non-Tensors through v2 losses.

  }

  @ListFeature.Require(absent = SUPPORTS_ADD_WITH_INDEX)
  @CollectionSize.Require(absent = ZERO)
  /*
   * absent = ZERO isn't required, since unmodList.add() must
   * throw regardless, but it keeps the method name accurate.
   */
  public void testAddAtIndex_unsupportedPresent() {
    assertThrows(UnsupportedOperationException.class, () -> getList().add(0, e0()));
    expectUnchanged();
  }

  }

  @CollectionFeature.Require(absent = SUPPORTS_ADD)
  @CollectionSize.Require(absent = ZERO)
  /*
   * absent = ZERO isn't required, since unmodList.add() must
   * throw regardless, but it keeps the method name accurate.
   */
  public void testAdd_unsupportedPresent() {
    assertThrows(UnsupportedOperationException.class, () -> getList().add(e0()));
  }

  @CollectionFeature.Require(value = {SUPPORTS_ADD, ALLOWS_NULL_VALUES})