new RoundToDoubleTester(BigDecimal.ZERO).setExpectation(0.0, values()).test();
  }

  public void testRoundToDouble_oneThird() {
    new RoundToDoubleTester(
            BigDecimal.ONE.divide(BigDecimal.valueOf(3), new MathContext(50, HALF_EVEN)))
        .roundUnnecessaryShouldThrow()
        .setExpectation(0.33333333333333337, UP, CEILING)

  }

  /**
   * Verifies that the specified expected result is always produced by collecting the specified
   * inputs, regardless of how the elements are divided.
   */
  @SafeVarargs
  @CanIgnoreReturnValue
  @SuppressWarnings("nullness") // TODO(cpovirk): Remove after we fix whatever the bug is.

        }
        boolean dividesEvenly = (p % q) == 0;
        try {
          assertEquals(p + "/" + q, p, IntMath.divide(p, q, UNNECESSARY) * q);
          assertTrue(p + "/" + q + " not expected to divide evenly", dividesEvenly);
        } catch (ArithmeticException e) {
          assertFalse(p + "/" + q + " expected to divide evenly", dividesEvenly);
        }
      }
    }
  }

  public void testZeroDivIsAlwaysZero() {

    }

    private double singleQuantileFromSorted(int index, int scale, double[] dataset) {
      long numerator = (long) index * (dataset.length - 1);
      int positionFloor = (int) LongMath.divide(numerator, scale, RoundingMode.DOWN);
      int remainder = (int) (numerator - positionFloor * scale);
      if (remainder == 0) {
        return dataset[positionFloor];
      } else {

  }

  /**
   * Binary search [sections] for [codePoint], looking at its top 14 bits.
   *
   * This binary searches over 4-byte entries, and so it needs to adjust binary search indices
   * in (by dividing by 4) and out (by multiplying by 4).
   */
  private fun findSectionsIndex(codePoint: Int): Int {
    val target = (codePoint and 0x1fff80) shr 7
    val offset =
      binarySearch(
        position = 0,

      int j = i & ARRAY_MASK;
      tmp += LongMath.sqrt(positive[j], mode);
    }
    return tmp;
  }

  @Benchmark
  int divide(int reps) {
    int tmp = 0;
    for (int i = 0; i < reps; i++) {
      int j = i & ARRAY_MASK;
      tmp += LongMath.divide(longs[j], nonzero[j], mode);
    }
    return tmp;
  }

      int j = i & ARRAY_MASK;
      tmp += IntMath.sqrt(positive[j], mode);
    }
    return tmp;
  }

  @Benchmark
  int divide(int reps) {
    int tmp = 0;
    for (int i = 0; i < reps; i++) {
      int j = i & ARRAY_MASK;
      tmp += IntMath.divide(ints[j], nonzero[j], mode);
    }
    return tmp;
  }

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 |
|------------------|-----------------|-------------------|---------------------|

    std::vector<TFE_TensorHandle*> device_inputs;
    device_inputs.reserve(inputs.size());
    for (int input_index = 0; input_index < inputs.size(); ++input_index) {
      // Parallel tensors are divided between operations by device.
      device_inputs.push_back(inputs[input_index]->tensor(device_index));
    }
    device_thread->StartExecute(
        context, operation_name, std::move(device_inputs), attributes,

- **Operator** - The component provides user friendly options to operate the Istio service mesh.

## Repositories

The Istio project is divided across a few GitHub repositories:

- [istio/api](https://github.com/istio/api). This repository defines
component-level APIs and common configuration formats for the Istio platform.