* - 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);
 * }
 *

            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 '

   *
   * @since 21.0
   */
  @CanIgnoreReturnValue
  public long updateAndGet(K key, LongUnaryOperator updaterFunction) {
    checkNotNull(updaterFunction);
    Long result =
        map.compute(
            key, (k, value) -> updaterFunction.applyAsLong(value == null ? 0L : value.longValue()));
    return requireNonNull(result);
  }

  /**

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

      int[] same = new int[hashBits];
      int[] diff = new int[hashBits];
      // go through trials to compute probability
      for (int j = 0; j < trials; j++) {
        int key1 = rand.nextInt();
        // flip input bit for key2
        int key2 = key1 ^ (1 << i);
        // compute hash values
        int hash1 = function.hashInt(key1).asInt();
        int hash2 = function.hashInt(key2).asInt();

      control_output_names_vec.push_back(string(control_output_names[i]));
    }
  }

  // Compute body nodes.
  std::vector<const Node*> body_nodes;
  status->status = tensorflow::ComputeBodyNodes(
      fn_body, fn_name, num_opers, opers, input_nodes, &body_nodes);
  if (TF_GetCode(status) != TF_OK) return nullptr;

  // Compute body nodes.
  std::vector<const Node*> control_output_nodes;

      eachQueue@ for (queue in readyQueues) {
        val candidate = queue.futureTasks[0]
        val candidateDelay = maxOf(0L, candidate.nextExecuteNanoTime - now)

        when {
          // Compute the delay of the soonest-executable task.
          candidateDelay > 0L -> {
            minDelayNanos = minOf(candidateDelay, minDelayNanos)
            continue@eachQueue
          }