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:

          dups.set(i);
        }
      }
      if (dups.isEmpty()) {
        return null;
      }
      @SuppressWarnings({"rawtypes", "unchecked"})
      Entry<K, V>[] newEntries = new Entry[size - dups.cardinality()];
      for (int inI = 0, outI = 0; inI < size; inI++) {
        if (!dups.get(inI)) {
          newEntries[outI++] = entries[inI];
        }
      }
      return newEntries;
    }
  }

  /**

          dups.set(i);
        }
      }
      if (dups.isEmpty()) {
        return localAlternatingKeysAndValues;
      }
      Object[] newAlternatingKeysAndValues = new Object[(size - dups.cardinality()) * 2];
      for (int inI = 0, outI = 0; inI < size * 2; ) {
        if (dups.get(inI >>> 1)) {
          inI += 2;
        } else {
          newAlternatingKeysAndValues[outI++] =

   */
  @GwtIncompatible // SmallCharMatcher
  CharMatcher precomputedInternal() {
    BitSet table = new BitSet();
    setBits(table);
    int totalCharacters = table.cardinality();
    if (totalCharacters * 2 <= DISTINCT_CHARS) {
      return precomputedPositive(totalCharacters, table, toString());
    } else {

*   `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