}

// Mul sets z = x * y and returns z.
func (z *Int) Mul(x, y *Int) *Int {
	x.doinit()
	y.doinit()
	z.doinit()
	C.mpz_mul(&z.i[0], &x.i[0], &y.i[0])
	return z
}

// Div sets z = x / y, rounding toward zero, and returns z.
func (z *Int) Div(x, y *Int) *Int {
	x.doinit()
	y.doinit()
	z.doinit()
	C.mpz_tdiv_q(&z.i[0], &x.i[0], &y.i[0])
	return z
}

// Mod sets z = x % y and returns z.

        for (RoundingMode mode : ALL_SAFE_ROUNDING_MODES) {
          // Skip some tests that fail due to GWT's non-compliant int implementation.
          // TODO(cpovirk): does this test fail for only some rounding modes or for all?
          if (p == -2147483648 && q == -1 && intsCanGoOutOfRange()) {
            continue;
          }
          int expected =

   * values in between the two calls, or if one is obtained from the other after round-tripping
   * through java serialization. However, floating point rounding errors mean that it may be false
   * for some instances where the statistics are mathematically equal, including instances
   * constructed from the same values in a different order... or (in the general case) even in the

        low = mid;
      } else {
        return mid; // allRequired[mid] = centerFloor, so we can't get closer than that
      }
    }

    // Now pick the closest of the two candidates. Note that there is no rounding here.
    if (from + to - allRequired[low] - allRequired[high] > 0) {
      return high;
    } else {
      return low;
    }
  }

  /** Swaps the values at {@code i} and {@code j} in {@code array}. */

fused operation, possibly across statements, and produce a result that differs
from the value obtained by executing and rounding the instructions individually.
An explicit <a href="#Numeric_types">floating-point type</a> <a href="#Conversions">conversion</a> rounds to
the precision of the target type, preventing fusion that would discard that rounding.
</p>

<p>
For instance, some architectures provide a "fused multiply and add" (FMA) instruction

			if len(operands) == 0 && len(items) == 0 {
				if p.arch.InFamily(sys.ARM, sys.ARM64, sys.AMD64, sys.I386, sys.Loong64, sys.RISCV64) && tok == '.' {
					// Suffixes: ARM conditionals, Loong64 vector instructions, RISCV rounding mode or x86 modifiers.
					tok = p.nextToken()
					str := p.lex.Text()
					if tok != scanner.Ident {
						p.errorf("instruction suffix expected identifier, found %s", str)
					}
					cond = cond + "." + str

	VPEXTRW $127, X20, (SP)(AX*2)     // 62e37d081524447f or 62e3fd081524447f
	// EVEX: embedded zeroing.
	VADDPD.Z X30, X1, K7, X0  // 6291f58f58c6
	VMAXPD.Z (AX), Z2, K1, Z1 // 62f1edc95f08
	// EVEX: embedded rounding.
	VADDPD.RU_SAE Z3, Z2, K1, Z1   // 62f1ed5958cb
	VADDPD.RD_SAE Z3, Z2, K1, Z1   // 62f1ed3958cb
	VADDPD.RZ_SAE Z3, Z2, K1, Z1   // 62f1ed7958cb
	VADDPD.RN_SAE Z3, Z2, K1, Z1   // 62f1ed1958cb

        model is called for the first time.
*   `tf.linalg`:
    *   Add `CompositeTensor` as a base class to `LinearOperator`.
*   `tf.lite`:
    *   Fix mean op reference quantization rounding issue.
    *   Added `framework_stable` BUILD target, which links in only the
        non-experimental TF Lite APIs.
    *   Remove deprecated Java `Interpreter` methods:

	VASUBVV		V1, V2, V3			// d7a1202e
	VASUBVV		V1, V2, V0, V3			// d7a1202c
	VASUBVX		X10, V2, V3			// d761252e
	VASUBVX		X10, V2, V0, V3			// d761252c

	// 31.12.3: Vector Single-Width Fractional Multiply with Rounding and Saturation
	VSMULVV		V1, V2, V3			// d781209e
	VSMULVV		V1, V2, V0, V3			// d781209c
	VSMULVX		X10, V2, V3			// d741259e
	VSMULVX		X10, V2, V0, V3			// d741259c

# These are supported funding model platforms

github: [jinzhu]
patreon: jinzhu