* final} constants.
 *
 * <p>The {@link Joiner} class provides the inverse operation to splitting, but note that a
 * round-trip between the two should be assumed to be lossy.
 *
 * <p>See the Guava User Guide article on <a
 * href="https://github.com/google/guava/wiki/StringsExplained#splitter">{@code Splitter}</a>.
 *
 * @author Julien Silland
 * @author Jesse Wilson

 * final} constants.
 *
 * <p>The {@link Joiner} class provides the inverse operation to splitting, but note that a
 * round-trip between the two should be assumed to be lossy.
 *
 * <p>See the Guava User Guide article on <a
 * href="https://github.com/google/guava/wiki/StringsExplained#splitter">{@code Splitter}</a>.
 *
 * @author Julien Silland
 * @author Jesse Wilson

 * converter.reverse().convert(converter.convert(a)).equals(a)} is always true). However, it is very
 * common (perhaps <i>more</i> common) for round-trip conversion to be <i>lossy</i>. Consider an
 * example round-trip using {@link com.google.common.primitives.Doubles#stringConverter}:
 *
 * <ol>
 *   <li>{@code stringConverter().convert("1.00")} returns the {@code Double} value {@code 1.0}

 * converter.reverse().convert(converter.convert(a)).equals(a)} is always true). However, it is very
 * common (perhaps <i>more</i> common) for round-trip conversion to be <i>lossy</i>. Consider an
 * example round-trip using {@link com.google.common.primitives.Doubles#stringConverter}:
 *
 * <ol>
 *   <li>{@code stringConverter().convert("1.00")} returns the {@code Double} value {@code 1.0}

	if !is64Bit {
		return
	}

	runTestCases("64-bit", []testCase{
		0: {"-", maxInt, "out of range"},
	})
}

type RunesTest struct {
	in    string
	out   []rune
	lossy bool
}

var RunesTests = []RunesTest{
	{"", []rune{}, false},
	{" ", []rune{32}, false},
	{"ABC", []rune{65, 66, 67}, false},
	{"abc", []rune{97, 98, 99}, false},

    this.max = max;
  }

  /**
   * Returns statistics over a dataset containing the given values.
   *
   * @param values a series of values, which will be converted to {@code double} values (this may
   *     cause loss of precision)
   */
  public static Stats of(Iterable<? extends Number> values) {
    StatsAccumulator accumulator = new StatsAccumulator();
    accumulator.addAll(values);
    return accumulator.snapshot();
  }

 */
public enum HandleType {
    /**
     * No durability - standard handle
     */
    NONE(0),

    /**
     * SMB 2.1 durable handle - survives network loss
     */
    DURABLE_V1(1),

    /**
     * SMB 3.0 durable handle V2 - with timeout configuration
     */
    DURABLE_V2(2),

    /**
     * SMB 3.0 persistent handle - survives server reboot
     */

    // The upper bound on the length of an array and the bounds on the int values mean that, in
    // this case only, we can compute the sum as a long without risking overflow or loss of
    // precision. So we do that, as it's slightly quicker than the Knuth algorithm.
    long sum = 0;
    for (int index = 0; index < values.length; ++index) {
      sum += values[index];
    }

   *     cause loss of precision)
   */
  public void addAll(Iterable<? extends Number> values) {
    for (Number value : values) {
      add(value.doubleValue());
    }
  }

  /**
   * Adds the given values to the dataset.
   *
   * @param values a series of values, which will be converted to {@code double} values (this may
   *     cause loss of precision)
   */

    assertThat(transformation.slope()).isWithin(ALLOWED_ERROR).of(slope);
    // Note that we cannot test the actual mapping of points, as the results will be unreliable due
    // to loss of precision with this value of the slope.
  }

  public void testMappingWithSlope_maximalSlope() {
    double x1 = 1.2;
    double y1 = 3.4;
    double slope = Double.MAX_VALUE;