Praveen raj Mani <******@****.***> 1725663990 +0530

			for {
				select {
				case <-ctx.Done():
					return
				case watchResp, ok := <-watchCh:
					if !ok {
						time.Sleep(1 * time.Second)
						// Upon an error on watch channel
						// re-init the watch channel.
						goto outerLoop
					}
					if err := watchResp.Err(); err != nil {
						iamLogIf(ctx, err)
						// log and retry.
						time.Sleep(1 * time.Second)

		return db.CreateInBatches(value, db.CreateBatchSize)
	}

	tx = db.getInstance()
	tx.Statement.Dest = value
	return tx.callbacks.Create().Execute(tx)
}

// CreateInBatches inserts value in batches of batchSize
func (db *DB) CreateInBatches(value interface{}, batchSize int) (tx *DB) {
	reflectValue := reflect.Indirect(reflect.ValueOf(value))

	switch reflectValue.Kind() {
	case reflect.Slice, reflect.Array:

    /** True if either side has cleanly shut down this stream. We shall send no more bytes. */
    var finished: Boolean = false,
  ) : Sink {
    /**
     * Buffer of outgoing data. This batches writes of small writes into this sink as larges frames
     * written to the outgoing connection. Batching saves the (small) framing overhead.
     */
    private val sendBuffer = Buffer()

   * 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 {

    be only be used to accelerate a transition to OkHttp's request/response API.
    These deprecated modules will be dropped in an upcoming OkHttp 3.x release.

 *  **Canceling batches of calls is now the application's responsibility.**
    The API to cancel calls by tag has been removed and replaced with a more
    general mechanism. The dispatcher now exposes all in-flight calls via its