* 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.
   *

	if err != nil {
		if errors.Is(err, errConfigNotFound) || isErrObjectNotFound(err) {
			return errNoSuchJob
		}
		return err
	}
	if len(data) == 0 {
		// Seems to be empty create a new batchRepl object.
		return nil
	}
	if len(data) <= 4 {
		return fmt.Errorf("%s: no data", ri.JobType)
	}
	// Read header
	switch binary.LittleEndian.Uint16(data[0:2]) {
	case format:
	default:

   * 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.
   *

        * [`tf.data.Dataset.ragged_batch`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset/#ragged_batch), which batches elements of `tf.data.Dataset`s into `tf.RaggedTensor`s.
        * [`tf.data.Dataset.sparse_batch`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset/#sparse_batch), which batches elements of `tf.data.Dataset`s into `tf.sparse.SparseTensor`s.

## Bug Fixes and Other Changes

*   `tf.image`

// 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 {

   * 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.
   *

- Kubelet: plugins for dynamic resource allocation may use the `v1alpha3` API instead of v1alpha2 if they want to do prepare/unprepare operations in batches. ([#119012](https://github.com/kubernetes/kubernetes/pull/119012), [@pohly](https://github.com/pohly))