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);
          });

          UnsignedLong unsignedDiv = aUnsigned.dividedBy(bUnsigned);
          assertThat(unsignedDiv.longValue()).isEqualTo(expected);
        }
      }
    }
  }

  public void testDivideByZeroThrows() {
    for (long a : TEST_LONGS) {
      assertThrows(
          ArithmeticException.class,
          () -> UnsignedLong.fromLongBits(a).dividedBy(UnsignedLong.ZERO));
    }
  }

   * @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) {

  /*
   * Since Quantiles provides a fluent-style API, each test covers a chain of methods resulting in
   * the computation of one or more quantiles (or in an error) rather than individual methods. The
   * tests are divided into three sections:
   * 1. Tests on a hardcoded dataset for chains starting with median(), quartiles(), and scale(10);
   * 2. Tests on hardcoded datasets include non-finite values for chains starting with scale(10);

  /*
   * Since Quantiles provides a fluent-style API, each test covers a chain of methods resulting in
   * the computation of one or more quantiles (or in an error) rather than individual methods. The
   * tests are divided into three sections:
   * 1. Tests on a hardcoded dataset for chains starting with median(), quartiles(), and scale(10);
   * 2. Tests on hardcoded datasets include non-finite values for chains starting with scale(10);

 *
 * <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 {@linkplain WeakReference weak} references

 *
 * <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 {@linkplain WeakReference weak} references

different kinds, the result is of the operand's kind that appears later in this
list: integer, rune, floating-point, complex.
For example, an untyped integer constant divided by an
untyped complex constant yields an untyped complex constant.
</p>

<pre>
const a = 2 + 3.0          // a == 5.0   (untyped floating-point constant)

        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`.