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

            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 '

            }

            // 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

                                    ? 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)) {

		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:

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

   *
   * @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);
  }

  /**

      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();