* Holder object for values that cannot be null or empty (will be printed unconditionally). This
     * helps to shortcut most calls to isEmpty(), which is important because the check for emptiness
     * is relatively expensive. Use a subtype so this also doesn't need any extra storage.
     */
    private static final class UnconditionalValueHolder extends ValueHolder {}
  }

  private MoreObjects() {}

      //
      // We use two volatile reads. We could reduce this to one by
      // putting our fields into a holder class, but (at least on x86)
      // the extra memory consumption and indirection are more
      // expensive than the extra volatile reads.
      long nanos = expirationNanos;
      long now = System.nanoTime();
      if (nanos == 0 || now - nanos >= 0) {
        synchronized (lock) {

        }
      }
    } else {
      /*
       * We'll call the user callback or collect the values only when all inputs complete,
       * regardless of whether some failed. This lets us avoid calling expensive methods like
       * Future.get() when we don't need to (specifically, for whenAllComplete().call*()), and it
       * lets all futures share the same listener.
       *

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    int removes = frequency(asList(stimuli), remove);
    if ((!features.contains(IteratorFeature.SUPPORTS_REMOVE) && removes > 1)
        || (stimuli.length >= 5 && removes > 2)) {
      // removes are the most expensive thing to test, since they often throw exceptions with stack
      // traces, so we test them a bit less aggressively
      return;
    }

    int removes = frequency(asList(stimuli), remove);
    if ((!features.contains(IteratorFeature.SUPPORTS_REMOVE) && removes > 1)
        || (stimuli.length >= 5 && removes > 2)) {
      // removes are the most expensive thing to test, since they often throw exceptions with stack
      // traces, so we test them a bit less aggressively
      return;
    }

    }

    // Assert that the dataset contains the same elements after the in-place computation (although
    // they may be reordered). We only do this for one index rather than for all indexes, as it is
    // quite expensive (quadratic in the size of PSEUDORANDOM_DATASET).
    double[] dataset = Doubles.toArray(PSEUDORANDOM_DATASET);
    @SuppressWarnings("unused")
    double actual = percentiles().index(33).computeInPlace(dataset);

    }

    // Assert that the dataset contains the same elements after the in-place computation (although
    // they may be reordered). We only do this for one index rather than for all indexes, as it is
    // quite expensive (quadratic in the size of PSEUDORANDOM_DATASET).
    double[] dataset = Doubles.toArray(PSEUDORANDOM_DATASET);
    @SuppressWarnings("unused")
    double actual = percentiles().index(33).computeInPlace(dataset);

  }

  /**
   * Returns an immutable <i>view</i> of this array's values as a {@code List}; note that {@code
   * long} values are boxed into {@link Long} instances on demand, which can be very expensive. The
   * returned list should be used once and discarded. For any usages beyond that, pass the returned
   * list to {@link com.google.common.collect.ImmutableList#copyOf(Collection) ImmutableList.copyOf}

  }

  /**
   * Returns an immutable <i>view</i> of this array's values as a {@code List}; note that {@code
   * int} values are boxed into {@link Integer} instances on demand, which can be very expensive.
   * The returned list should be used once and discarded. For any usages beyond that, pass the
   * returned list to {@link com.google.common.collect.ImmutableList#copyOf(Collection)