Map.of("_default", List.of("QUERY1")), //
                Set.of("QUERY1"), //
                buildQuery("QUERY1^10"));

        assertQueryContext(

  static final double LARGE_INTEGER_VALUES_MEAN =
      BigInteger.valueOf(Integer.MAX_VALUE)
          .multiply(BigInteger.valueOf(3L))
          .divide(BigInteger.valueOf(4L))
          .doubleValue();
  static final double LARGE_INTEGER_VALUES_POPULATION_VARIANCE =
      BigInteger.valueOf(Integer.MAX_VALUE)
          .multiply(BigInteger.valueOf(Integer.MAX_VALUE))
          .divide(BigInteger.valueOf(16L))
          .doubleValue();

  static final double LARGE_INTEGER_VALUES_MEAN =
      BigInteger.valueOf(Integer.MAX_VALUE)
          .multiply(BigInteger.valueOf(3L))
          .divide(BigInteger.valueOf(4L))
          .doubleValue();
  static final double LARGE_INTEGER_VALUES_POPULATION_VARIANCE =
      BigInteger.valueOf(Integer.MAX_VALUE)
          .multiply(BigInteger.valueOf(Integer.MAX_VALUE))
          .divide(BigInteger.valueOf(16L))
          .doubleValue();

	VFRDIVVF	F10, V2, V0, V3			// d7512584

	// 31.13.5: Vector Widening Floating-Point Multiply
	VFWMULVV	V1, V2, V3			// d79120e2
	VFWMULVV	V1, V2, V0, V3			// d79120e0
	VFWMULVF	F10, V2, V3			// d75125e2
	VFWMULVF	F10, V2, V0, V3			// d75125e0

	// 31.13.6: Vector Single-Width Floating-Point Fused Multiply-Add Instructions
	VFMACCVV	V2, V1, V3			// d79120b2
	VFMACCVV	V2, V1, V0, V3			// d79120b0

              // It's definitely safe to multiply into numerator and denominator.
              numerator *= n;
              denominator *= i;
              numeratorBits += nBits;
            } else {
              // It might not be safe to multiply into numerator and denominator,
              // so multiply (numerator / denominator) into result.

    /**
     * Test that closures work
     */
    @Test
    public void test_evaluate_closures() {
        final Map<String, Object> params = new HashMap<>();
        final String script = "def multiply = { x, y -> x * y }; return multiply(6, 7)";
        assertEquals(42, groovyEngine.evaluate(script, params));
    }

    /**
     * Test that list operations work
     */
    @Test

        .that(Longs.tryParse(BigInteger.valueOf(MAX_VALUE).multiply(BigInteger.TEN).toString()))
        .isNull();
    assertWithMessage("Min long - 1")
        .that(Longs.tryParse(BigInteger.valueOf(MIN_VALUE).subtract(BigInteger.ONE).toString()))
        .isNull();
    assertWithMessage("Min long * 10")
        .that(Longs.tryParse(BigInteger.valueOf(MIN_VALUE).multiply(BigInteger.TEN).toString()))
        .isNull();

    checkBigIntegerConversion("127.255.255.255", BigInteger.valueOf(Integer.MAX_VALUE));
    checkBigIntegerConversion(
        "255.255.255.254", BigInteger.valueOf(Integer.MAX_VALUE).multiply(BigInteger.valueOf(2)));
    checkBigIntegerConversion(
        "255.255.255.255", BigInteger.ONE.shiftLeft(32).subtract(BigInteger.ONE));
  }

  public void testFromIpv6BigIntegerValid() {

* **Deep Learning**: this is a sub-field of Machine Learning, so, the same applies. It's just that there is not a single spreadsheet of numbers to multiply, but a huge set of them, and in many cases, you use a special processor to build and / or use those models.

### Concurrency + Parallelism: Web + Machine Learning { #concurrency-parallelism-web-machine-learning }

        try {
          BigInteger quotient = BigIntegerMath.divide(p, q, UNNECESSARY);
          BigInteger undone = quotient.multiply(q);
          if (!p.equals(undone)) {
            failFormat("expected %s.multiply(%s) = %s; got %s", quotient, q, p, undone);
          }
          assertTrue(dividesEvenly);
        } catch (ArithmeticException e) {
          assertFalse(dividesEvenly);