* - 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 '

		return opVal, opErr
	}

	// Need to evaluate the ConditionRHS
	switch {
	case e.ConditionRHS.Compare != nil:
		cmpRight, cmpRErr := e.ConditionRHS.Compare.Operand.evalNode(r, tableAlias)
		if cmpRErr != nil {
			return nil, cmpRErr
		}

		b, err := opVal.compareOp(strings.ToUpper(e.ConditionRHS.Compare.Operator), cmpRight)
		return FromBool(b), err

	case e.ConditionRHS.Between != nil:

                                    ? s
                                    : "";
                    map.compute(name, (n, ob) -> ob == null || comparator.compare(ob, b) < 0 ? b : ob);
                }
                for (var bean : locator.get().locate(toGuiceKey(valueType))) {
                    if (isPlexusBean(bean)) {

            }

            // 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

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.

   * computes) correspond to minimum intervals between subsequent requests, for the specified number
   * of requested permits.
   *
   * Here is an example of storedPermitsToWaitTime: If storedPermits == 10.0, and we want 3 permits,
   * we take them from storedPermits, reducing them to 7.0, and compute the throttling for these as

      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();