this.totalCount = elemCount + totalCount(left) + totalCount(right);
    }

    private void recomputeHeight() {
      this.height = 1 + max(height(left), height(right));
    }

    private void recompute() {
      recomputeMultiset();
      recomputeHeight();
    }

    private AvlNode<E> rebalance() {
      switch (balanceFactor()) {
        case -2:

    reallyTestNoMatchThenMatch(matcher.precomputed().negate(), s);
  }

  private void doTestNoMatchThenMatch(CharMatcher matcher, String s) {
    reallyTestNoMatchThenMatch(matcher, s);
    reallyTestMatchThenNoMatch(matcher.negate(), s);
    reallyTestNoMatchThenMatch(matcher.precomputed(), s);
    reallyTestMatchThenNoMatch(matcher.negate().precomputed(), s);

   * - median with compute taking a double-collection and with computeInPlace;
   * - quartiles with index and with indexes taking int-varargs, and with compute taking a
   *   double-collection and with computeInPlace;
   * - scale with index and with indexes taking int-varargs, and with all overloads of compute
   *   taking a double-collection and with computeInPlace;

   * - median with compute taking a double-collection and with computeInPlace;
   * - quartiles with index and with indexes taking int-varargs, and with compute taking a
   *   double-collection and with computeInPlace;
   * - scale with index and with indexes taking int-varargs, and with all overloads of compute
   *   taking a double-collection and with computeInPlace;

 *
 * <h3>Examples</h3>
 *
 * <p>To compute the median:
 *
 * {@snippet :
 * double myMedian = median().compute(myDataset);
 * }
 *
 * where {@link #median()} has been statically imported.
 *
 * <p>To compute the 99th percentile:
 *
 * {@snippet :
 * double myPercentile99 = percentiles().index(99).compute(myDataset);
 * }
 *

        return (logX2Floor < 2 * logFloor + 1) ? logFloor : logFloor + 1;
    }
    throw new AssertionError();
  }

  /*
   * The maximum number of bits in a square root for which we'll precompute an explicit half power
   * of two. This can be any value, but higher values incur more class load time and linearly
   * increasing memory consumption.
   */

            print(
                'ERROR: TensorFlow only supports small CUDA compute'
                ' capabilities of sm_30 and higher. Please re-specify the list'
                ' of compute capabilities excluding version %s.' % ver)
            all_valid = False
          if ver < 35:
            print('WARNING: XLA does not support CUDA compute capabilities '

Kubernetes manages stateless Spark and Hive containers elastically on the compute nodes. Spark has native scheduler integration with Kubernetes. Hive, for legacy reasons, uses YARN scheduler on top of Kubernetes.

            }

            // Compute Resource Group IDs with Resource Domain ID to get SIDs
            this.resourceGroupSids = new SID[resourceGroups.length];
            for (int i = 0; i < resourceGroups.length; i++) {
                this.resourceGroupSids[i] = new SID(resourceDomainId, resourceGroups[i].getId());
            }

            // Compute User IDs with Domain ID to get User SIDs

    (For example, a function in a library to compute square roots has
    a purpose that is entirely well-defined independent of the
    application.  Therefore, Subsection 2d requires that any
    application-supplied function or table used by this function must
    be optional: if the application does not supply it, the square
    root function must still compute square roots.)