/*
   * Concurrency control uses the classic two-condition algorithm
   * found in any textbook.
   */

  /** Monitor guarding all access */
  final Monitor monitor;

  /** Guard for waiting takes */
  private final Monitor.Guard notEmpty;

  /** Guard for waiting puts */
  private final Monitor.Guard notFull;

  // Internal helper methods

  /** Circularly increment i. */
  final int inc(int i) {

  Run({{func_feed, Int32Tensor(2)}}, func_op, 2 /*+=*/ + 5 + 1);

  // Verify input, output, and subset of edges in fdef.
  // The subset of edges we verify is a chain between feed1 and output to
  // make sure that the correct output is picked.
  tensorflow::FunctionDef fdef;
  ASSERT_TRUE(GetFunctionDef(func_, &fdef));
  VerifyFDefInputs(fdef, M({{"feed1"}, {"feed2"}}));
  VerifyFDefOutputs(fdef, M({{"test_loop_exit"}}));

    String expected = "TestClass{field1=1, value1, field2=value2}";

    assertThat(helper.toString()).isEqualTo(expected);
    // Call toString again
    assertThat(helper.toString()).isEqualTo(expected);

    // Make sure the cached value is reset when we modify the helper at all
    String expected2 = "TestClass{field1=1, value1, field2=value2, 2}";
    helper.addValue(2);
    assertThat(helper.toString()).isEqualTo(expected2);
  }

    }

    @Override
    public Multiset<K> create(Object... elements) {
      /*
       * This is nasty and complicated, but it's the only way to make sure keys get mapped to enough
       * distinct values.
       */
      Entry<?, ?>[] entries = new Entry<?, ?>[elements.length];
      Map<K, Iterator<V>> valueIterators = new HashMap<>();

    for (double tolerance : TOLERANCE_CANDIDATES) {
      assertTrue(DoubleMath.fuzzyEquals(Double.NaN, Double.NaN, tolerance));
    }
  }

  public void testFuzzyEqualsZeroTolerance() {
    // make sure we test -0 tolerance
    for (double zero : Doubles.asList(0.0, -0.0)) {
      for (double a : ALL_DOUBLE_CANDIDATES) {
        for (double b : ALL_DOUBLE_CANDIDATES) {
          assertEquals(

    // array of indexes to be calculated is modified between the calls to indexes and compute: since
    // the contract is that it is snapshotted, this shouldn't make any difference to the result.
    int[] indexes = {0, 10, 5, 1, 8, 10};
    ScaleAndIndexes intermediate = Quantiles.scale(10).indexes(indexes);
    indexes[0] = 3;
    assertThat(intermediate.compute(SIXTEEN_SQUARES_DOUBLES))

    // array of indexes to be calculated is modified between the calls to indexes and compute: since
    // the contract is that it is snapshotted, this shouldn't make any difference to the result.
    int[] indexes = {0, 10, 5, 1, 8, 10};
    ScaleAndIndexes intermediate = Quantiles.scale(10).indexes(indexes);
    indexes[0] = 3;
    assertThat(intermediate.compute(SIXTEEN_SQUARES_DOUBLES))

            if (response != null) {
                response.clearReceived();
            }

            // try TreeConnectAndX with the request
            // this does not make any sense if we are disconnecting right now
            T chainedResponse = null;
            if (!(request instanceof SmbComTreeDisconnect) && !(request instanceof Smb2TreeDisconnectRequest)) {

		SuccessorModTime:           o.SuccessorModTime,
		ReplicationStatusInternal:  o.ReplicationStatusInternal,
		VersionPurgeStatusInternal: o.VersionPurgeStatusInternal,
	}
	cinfo.UserDefined = make(map[string]string, len(o.UserDefined))
	maps.Copy(cinfo.UserDefined, o.UserDefined)
	return cinfo
}

func (o ObjectInfo) tierStats() tierStats {
	ts := tierStats{
		TotalSize:   uint64(o.Size),

 *
 * <ul>
 *   <li>Test cases related to whether the graph is directed or undirected.
 *   <li>Test cases related to the specific implementation of the {@link Graph} interface.
 * </ul>
 *
 * TODO(user): Make this class generic (using <N, E>) for all node and edge types.
 * TODO(user): Differentiate between directed and undirected edge strings.
 */
@NullUnmarked
public abstract class AbstractGraphTest {

  Graph<Integer> graph;