const maxInsertion = 12

	var (
		wasBalanced    = true // whether the last partitioning was reasonably balanced
		wasPartitioned = true // whether the slice was already partitioned
	)

	for {
		length := b - a

		if length <= maxInsertion {
			insertionSortCmpFunc(data, a, b, cmp)
			return
		}

		// Fall back to heapsort if too many bad choices were made.
		if limit == 0 {

	const maxInsertion = 12

	var (
		wasBalanced    = true // whether the last partitioning was reasonably balanced
		wasPartitioned = true // whether the slice was already partitioned
	)

	for {
		length := b - a

		if length <= maxInsertion {
			insertionSortOrdered(data, a, b)
			return
		}

		// Fall back to heapsort if too many bad choices were made.
		if limit == 0 {

				}
			}
			if len(splitPoints) == 1 {
				continue // no splits, leave equivalence class alone.
			}

			// Move another equivalence class down in place of e.
			partition[i] = partition[len(partition)-1]
			partition = partition[:len(partition)-1]
			i--

			// Add new equivalence classes for the parts of e we found.
			splitPoints = append(splitPoints, len(e))
			for j := 0; j < len(splitPoints)-1; j++ {

    assertFalse(partitions.hasNext());
  }

  public void testPaddedPartition_singleton2() {
    Iterator<Integer> source = Iterators.singletonIterator(1);
    Iterator<List<Integer>> partitions = Iterators.paddedPartition(source, 2);
    assertTrue(partitions.hasNext());
    assertTrue(partitions.hasNext());
    assertEquals(Arrays.<@Nullable Integer>asList(1, null), partitions.next());

		},
		{
			// missing element 2
			vars: []*ir.Name{v1, v2, v3},
			partition: map[*ir.Name][]int{
				v1: []int{0, 1},
				v2: []int{0, 1},
				v3: []int{0, 1},
			},
			experr: true,
		},
		{
			// partitions disagree for v1 vs v2
			vars: []*ir.Name{v1, v2, v3},
			partition: map[*ir.Name][]int{
				v1: []int{0, 1, 2},
				v2: []int{1, 0, 2},
				v3: []int{0, 1, 2},
			},
			experr: true,
		},
	}

   * <p><b>Note:</b> if {@code iterable} is a {@link List}, use {@link Lists#partition(List, int)}
   * instead.
   *
   * @param iterable the iterable to return a partitioned view of
   * @param size the desired size of each partition (the last may be smaller)
   * @return an iterable of unmodifiable lists containing the elements of {@code iterable} divided
   *     into partitions
   * @throws IllegalArgumentException if {@code size} is nonpositive

   * <p><b>Note:</b> if {@code iterable} is a {@link List}, use {@link Lists#partition(List, int)}
   * instead.
   *
   * @param iterable the iterable to return a partitioned view of
   * @param size the desired size of each partition (the last may be smaller)
   * @return an iterable of unmodifiable lists containing the elements of {@code iterable} divided
   *     into partitions
   * @throws IllegalArgumentException if {@code size} is nonpositive

// along with this program.  If not, see <http://www.gnu.org/licenses/>.

package cmd

import (
	"errors"

	"github.com/zeebo/xxh3"
)

// getFileInfoVersions partitions this object's versions such that,
//   - fivs.Versions has all the non-free versions
//   - fivs.FreeVersions has all the free versions
//

  // [g1, g2, g4, g5, g3]
  auto data = op.input(0);
  auto partitions = op.input(1);
  int32_t num_partitions;
  TF_RETURN_IF_ERROR(
      GetNodeAttr(op.node()->attrs(), "num_partitions", &num_partitions));

  // Note: the shape of the partitions is a prefix of the data shape.
  // shape(partitions) = [5]
  auto partitions_shape = Shape(scope, partitions);
  // We now create a partitions-shaped tensor with integers from

The new [Cookie.Partitioned] field identifies cookies with the Partitioned attribute....