}

// This is only used for operations that may cause overflow.
var op2str2 = [...]string{
	token.ADD: "addition",
	token.SUB: "subtraction",
	token.XOR: "bitwise XOR",
	token.MUL: "multiplication",
	token.SHL: "shift",
}

// If typ is a type parameter, underIs returns the result of typ.underIs(f).
// Otherwise, underIs returns the result of f(under(typ)).
func underIs(typ Type, f func(Type) bool) bool {

	// looks strange but the purpose is to arrive at an integer division
	// (e.g. if retainExtraPercent = 12.5, then we get a divisor of 8)
	// that also avoids the overflow from a multiplication.
	gcPercentGoal += gcPercentGoal / (1.0 / (retainExtraPercent / 100.0))
	// Align it to a physical page boundary to make the following calculations
	// a bit more exact.

}

// This is only used for operations that may cause overflow.
var op2str2 = [...]string{
	syntax.Add: "addition",
	syntax.Sub: "subtraction",
	syntax.Xor: "bitwise XOR",
	syntax.Mul: "multiplication",
	syntax.Shl: "shift",
}

// If typ is a type parameter, underIs returns the result of typ.underIs(f).
// Otherwise, underIs returns the result of f(under(typ)).
func underIs(typ Type, f func(Type) bool) bool {

	var r int64
	if rate <= 0 {
		r = 0 // disable profiling
	} else if rate == 1 {
		r = 1 // profile everything
	} else {
		// convert ns to cycles, use float64 to prevent overflow during multiplication
		r = int64(float64(rate) * float64(ticksPerSecond()) / (1000 * 1000 * 1000))
		if r == 0 {
			r = 1
		}
	}

	atomic.Store64(&blockprofilerate, uint64(r))
}

	//
	// The constants are obscured in this way for efficiency. The denominator
	// of the fraction is always a power-of-two for a quick division, so that
	// the numerator is a single constant integer multiplication.
	triggerRatioDen = 64

	// The minimum trigger constant was chosen empirically: given a sufficiently
	// fast/scalable allocator with 48 Ps that could drive the trigger ratio

func (p *PriorityQueue) calculateBackoffDuration(podInfo *framework.QueuedPodInfo) time.Duration {
	duration := p.podInitialBackoffDuration
	for i := 1; i < podInfo.Attempts; i++ {
		// Use subtraction instead of addition or multiplication to avoid overflow.
		if duration > p.podMaxBackoffDuration-duration {
			return p.podMaxBackoffDuration
		}
		duration += duration
	}
	return duration
}

      input_signature=[
          tensor_spec.TensorSpec(shape=[1, 4], dtype=dtypes.float32)
      ]
  )
  def matmul(self, matmul_input: core.Tensor) -> Mapping[str, core.Tensor]:
    """Performs a matrix multiplication.

    Args:
      matmul_input: Input tensor to matmul with the filter.

    Returns:
      A map of: output key -> output result.
    """
    out = math_ops.matmul(matmul_input, self.matmul_filters)

	dwarf.Uleb128put(d, fsd, 1)                         // code_alignment_factor
	dwarf.Sleb128put(d, fsd, dataAlignmentFactor)       // all CFI offset calculations include multiplication with this factor
	dwarf.Uleb128put(d, fsd, int64(thearch.Dwarfreglr)) // return_address_register

	fsu.AddUint8(dwarf.DW_CFA_def_cfa)                  // Set the current frame address..

     For example, for the equation denoting batch matrix multiplication,
     `bij,bjk->bik`, the axis label `b` corresponds to a batch dimension.

 (d) Contraction: In case of binary einsum, axes corresponding to labels
     appearing in two different inputs (and not in the output) are contracted
     against each other.
     Considering the batch matrix multiplication equation again

	ASUBW & obj.AMask:  rIIIEncoding,
	ASRAW & obj.AMask:  rIIIEncoding,

	// 5.3: Load and Store Instructions (RV64I)
	ALD & obj.AMask: iIEncoding,
	ASD & obj.AMask: sIEncoding,

	// 7.1: Multiplication Operations
	AMUL & obj.AMask:    rIIIEncoding,
	AMULH & obj.AMask:   rIIIEncoding,
	AMULHU & obj.AMask:  rIIIEncoding,
	AMULHSU & obj.AMask: rIIIEncoding,
	AMULW & obj.AMask:   rIIIEncoding,