func hashOrder(key string, cardinality int) []int {
	if cardinality <= 0 {
		// Returns an empty int slice for cardinality < 0.
		return nil
	}

	nums := make([]int, cardinality)
	keyCrc := crc32.Checksum([]byte(key), crc32.IEEETable)

	start := int(keyCrc % uint32(cardinality))
	for i := 1; i <= cardinality; i++ {
		nums[i-1] = 1 + ((start + i) % cardinality)
	}
	return nums
}

	if cardinality <= 0 {
		return -1
	}
	keyCrc := crc32.Checksum([]byte(key), crc32.IEEETable)
	return int(keyCrc % uint32(cardinality))
}

func hashKey(algo string, key string, cardinality int, id [16]byte) int {
	switch algo {
	case formatErasureVersionV2DistributionAlgoV1:
		return crcHashMod(key, cardinality)
	case formatErasureVersionV3DistributionAlgoV2, formatErasureVersionV3DistributionAlgoV3:

	for _, etag := range etags {
		if etag == "" {
			continue
		}
		etagOccurrenceMap[etag]++
	}

	maxima = 0 // Counter for remembering max occurrence of elements.
	latest := ""

	// Find the common cardinality from previously collected
	// occurrences of elements.
	for etag, count := range etagOccurrenceMap {
		if count < maxima {
			continue
		}

		// We are at or above maxima
		if count > maxima {

   */
  protected final void expectContents(E... elements) {
    expectContents(asList(elements));
  }

  /**
   * Asserts that the collection under test contains exactly the given elements, respecting
   * cardinality but not order. Subclasses may override this method to provide stronger assertions,
   * e.g., to check ordering in lists, but realize that <strong>unless a test extends {@link

*   `tf.data.experimental.dense_to_ragged_batch` to output variable ragged rank.
*   `tf.data.experimental.cardinality` is now a method on `tf.data.Dataset`.
*   `tf.data.Dataset` now supports `len(Dataset)` when the cardinality is
    finite.

### `tf.distribute`:

*   Expose experimental