* of reads may not be immediately reflected on the algorithm's data structures. These structures
   * are guarded by a lock and operations are applied in batches to avoid lock contention. The
   * penalty of applying the batches is spread across threads so that the amortized cost is slightly
   * higher than performing just the operation without enforcing the capacity constraint.
   *

// is more efficient than decrypting ETags sequentually.
func DecryptETags(ctx context.Context, k *kms.KMS, objects []ObjectInfo) error {
	const BatchSize = 250 // We process the objects in batches - 250 is a reasonable default.
	var (
		metadata = make([]map[string]string, 0, BatchSize)
		buckets  = make([]string, 0, BatchSize)
		names    = make([]string, 0, BatchSize)
	)
	for len(objects) > 0 {

	c.ProcessState = state

	var timer *time.Timer
	if c.ctxResult != nil {
		watch := <-c.ctxResult
		timer = watch.timer
		// If c.Process.Wait returned an error, prefer that.
		// Otherwise, report any error from the watchCtx goroutine,
		// such as a Context cancellation or a WaitDelay overrun.
		if err == nil && watch.err != nil {
			err = watch.err
		}
	}

	if goroutineErr := c.awaitGoroutines(timer); err == nil {

	}
	if s.wakeup != nil {
		// Non-blocking send.
		//
		// Others may also write to this channel and we don't
		// want to block on the receiver waking up. This also
		// effectively batches together wakeup notifications.
		select {
		case s.wakeup <- struct{}{}:
		default:
		}
	}
	if raceenabled {
		racerelease(unsafe.Pointer(&s.lock))
	}
	unlock(&s.lock)
}

    TFL_OperandHasAtleastRank<0, 2>,
    PredOpTrait<"input and result must have the same element type",
      TFL_TCresVTEtIsSameAsOp<0, 0>>,
    Pure]> {
  let summary = [{
    Returns a batched matrix tensor with new batched diagonal values.
  }];

  let description = [{
Given `input` and `diagonal`, this operation returns a tensor with the
same shape and values as `input`, except for the main diagonal of the

rules:
  # Nodes are watched to monitor for deletions.
  - apiGroups: [""]
    resources:
      - nodes
    verbs:
      - watch
      - list
      - get
  # Pods are watched to check for existence as part of IPAM controller.
  - apiGroups: [""]
    resources:
      - pods
    verbs:
      - get
      - list
      - watch

        during the python Dataset object creation instead doing it during
        Iterator creation time.
    *   Add `batch_dims` argument to `tf.gather`.
    *   The behavior of `tf.gather` is now correct when `axis=None` and
        `batch_dims<0`.
    *   Update docstring for gather to properly describe the non-empty
        `batch_dims` case.
    *   Removing of dtype in the constructor of initializers and partition_info

	Namespace string
	Match     string
	Matches   []string
	GwMatches []types.NamespacedName
	Dest      string
	Port      int
	PortMatch int
	Time      string
}

func (args vsArgs) Config(t *testing.T, variant string) string {
	if args.Time == "" {
		args.Time = TimeBase
	}

	if args.Matches == nil {
		args.Matches = []string{args.Match}
	}
	if variant == "httproute" {

    [(IsTailOfShape $rhs, $lhs),
      (IsTailOfShape $lhs, $rhs),
      (IsTailOfShape $input1, $input2),
      (IsTailOfShape $input2, $input1),
      (SameElementType $input1, $input2)]>;

  // Move binary op batched RHS before reshape:
  // binary(reshape(lhs), rhs) => reshape(binary(lhs, flatten(rhs)))
  // Pattern targetted here is as follows-
  // [input, lhr, rhs] == [<1x1024x128>, <1x1024x8x16>, <1x1x8x16xf32>]

   * of reads may not be immediately reflected on the algorithm's data structures. These structures
   * are guarded by a lock and operations are applied in batches to avoid lock contention. The
   * penalty of applying the batches is spread across threads so that the amortized cost is slightly
   * higher than performing just the operation without enforcing the capacity constraint.
   *