UnsignedInteger unsignedDiv = aUnsigned.dividedBy(bUnsigned);
          assertThat(unsignedDiv.intValue()).isEqualTo(expected);
        }
      }
    }
  }

  public void testDivideByZeroThrows() {
    for (int a : TEST_INTS) {
      assertThrows(
          ArithmeticException.class,
          () -> {
            UnsignedInteger unused = UnsignedInteger.fromIntBits(a).dividedBy(UnsignedInteger.ZERO);
          });

  }

  /**
   * Returns the result of dividing this by {@code val}.
   *
   * @throws ArithmeticException if {@code val} is zero
   * @since 14.0
   */
  public UnsignedInteger dividedBy(UnsignedInteger val) {
    return fromIntBits(UnsignedInts.divide(value, checkNotNull(val).value));
  }

  /**
   * Returns this mod {@code val}.
   *
   * @throws ArithmeticException if {@code val} is zero

            byte[] testBuffer = new byte[8];
            ByteBuffer bb = ByteBuffer.wrap(testBuffer).order(ByteOrder.LITTLE_ENDIAN);
            bb.putShort((short) 0); // pathConsumed (will be divided by 2)
            bb.putShort((short) 0); // numReferrals
            bb.putShort((short) 0); // tflags
            bb.putShort((short) 0); // tflags high bytes (skipped in decode)

Below is a list of data/parity drives and corresponding _approximate_ storage space usage on a 16 drive MinIO deployment. The field _storage
usage ratio_ is simply the drive space used by the file after erasure-encoding, divided by actual file size.

| Total Drives (N) | Data Drives (D) | Parity Drives (P) | Storage Usage Ratio |
|------------------|-----------------|-------------------|---------------------|

	return ep, nil
}

// GetAllSets - parses all ellipses input arguments, expands them into
// corresponding list of endpoints chunked evenly in accordance with a
// specific set size.
// For example: {1...64} is divided into 4 sets each of size 16.
// This applies to even distributed setup syntax as well.
func GetAllSets(setDriveCount uint64, args ...string) ([][]string, error) {
	var setArgs [][]string
	if !ellipses.HasEllipses(args...) {

   * @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
   */
  public static <T extends @Nullable Object> Iterable<List<T>> partition(
      Iterable<T> iterable, int size) {

   * @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
   */
  public static <T extends @Nullable Object> Iterable<List<T>> partition(
      Iterable<T> iterable, int size) {

   * @param iterator the iterator to return a partitioned view of
   * @param size the desired size of each partition (the last may be smaller)
   * @return an iterator of immutable lists containing the elements of {@code iterator} divided into
   *     partitions
   * @throws IllegalArgumentException if {@code size} is nonpositive
   */
  public static <T extends @Nullable Object> UnmodifiableIterator<List<T>> partition(

 *
 * <ul>
 *   <li>automatic loading of entries into the cache
 *   <li>least-recently-used eviction when a maximum size is exceeded (note that the cache is
 *       divided into segments, each of which does LRU internally)
 *   <li>time-based expiration of entries, measured since last access or last write
 *   <li>keys automatically wrapped in {@code WeakReference}

        passed to the optimizer but the reported loss will be a scalar value.
    *   `SUM`: Scalar sum of weighted losses. 4. `SUM_OVER_BATCH_SIZE`: Scalar
        `SUM` divided by number of elements in losses. This reduction type is
        not supported when used with `tf.distribute.Strategy` outside of
        built-in training loops like `tf.keras` `compile`/`fit`.