): List<SmallSubprojectBucket> {
        // splitIntoBuckets() method expects us to split large element into N elements,
        // but we want to have a single bucket with N batches.
        // As a workaround, we repeat the bucket N times, and deduplicate the result at the end
        val resultIncludingDuplicates = splitIntoBuckets(
            LinkedList(subProjectTestClassTimes),

}

// FindInBatches finds all records in batches of batchSize
func (db *DB) FindInBatches(dest interface{}, batchSize int, fc func(tx *DB, batch int) error) *DB {
	var (
		tx = db.Order(clause.OrderByColumn{
			Column: clause.Column{Table: clause.CurrentTable, Name: clause.PrimaryKey},
		}).Session(&Session{})
		queryDB      = tx
		rowsAffected int64
		batch        int
	)

	// user specified offset or limit

    /** 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.
   *

    matrices or batches of matrices (CPU only).
*   Added gradients for eigenvalues and eigenvectors computed using
    `self_adjoint_eig` or `self_adjoint_eigvals`.
*   Eliminated `batch_*` methods for most linear algebra and FFT ops and
    promoted the non-batch version of the ops to handle batches of matrices.

   * 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 len(SSES3SinglePartObjects) == 0 {
			for i := range batch {
				size, err := batch[i].GetActualSize()
				if err != nil {
					return err
				}
				batch[i].Size = size

				if _, ok := crypto.IsEncrypted(batch[i].UserDefined); ok {
					ETag, err := etag.Parse(batch[i].ETag)
					if err != nil {
						return err
					}
					batch[i].ETag = ETag.Format().String()
				}
			}

    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

   * 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.
   *

    assertTrue(matcher.matches(s.charAt(0)));
    assertTrue(matcher.apply(s.charAt(0)));
    assertEquals(0, matcher.indexIn(s));
    assertEquals(0, matcher.indexIn(s, 0));
    assertEquals(-1, matcher.indexIn(s, 1));
    assertEquals(0, matcher.lastIndexIn(s));
    assertTrue(matcher.matchesAnyOf(s));
    assertTrue(matcher.matchesAllOf(s));
    assertFalse(matcher.matchesNoneOf(s));