uv3 := new(Element).Multiply(u, t0.Multiply(v2, v))
	uv7 := new(Element).Multiply(uv3, t0.Square(v2))
	rr := new(Element).Multiply(uv3, t0.Pow22523(uv7))

	check := new(Element).Multiply(v, t0.Square(rr)) // check = v * r^2

	uNeg := new(Element).Negate(u)
	correctSignSqrt := check.Equal(u)
	flippedSignSqrt := check.Equal(uNeg)
	flippedSignSqrtI := check.Equal(t0.Multiply(uNeg, sqrtM1))

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

        assertQueryContext(

  // CHECK-DAG: %[[VARIANCE_EPS_RSQRT:.+]] = mhlo.rsqrt %[[VARIANCE_EPS]] : tensor<256xf32>
  // CHECK-DAG: %[[MULTIPLIER:.+]] = mhlo.multiply %[[VARIANCE_EPS_RSQRT]], %[[SCALE]] : tensor<256xf32>
  // CHECK-DAG: %[[MUL_MEAN:.+]] = mhlo.multiply %[[MULTIPLIER]], %[[MEAN]] : tensor<256xf32>
  // CHECK-DAG: %[[RHS:.+]] = mhlo.subtract %[[OFFSET]], %[[MUL_MEAN]] : tensor<256xf32>

  %0 = "mhlo.broadcast_in_dim"(%cst0) <{broadcast_dimensions = dense<3> : tensor<1xi64>}> : (tensor<4xi32>) -> tensor<1x1x2x4xi32>
  // CHECK: %[[MUL:.*]] = mhlo.multiply %[[BROADCAST]], %[[ARG]] : tensor<1x1x2x4xi32>
  %1 = mhlo.multiply %0, %arg0 : tensor<1x1x2x4xi32>
  // CHECK:      return %[[MUL]] : tensor<1x1x2x4xi32>
  func.return %1 : tensor<1x1x2x4xi32>

      case 2:
        return nums.get(start).multiply(nums.get(start + 1));
      case 3:
        return nums.get(start).multiply(nums.get(start + 1)).multiply(nums.get(start + 2));
      default:
        // Otherwise, split the list in half and recursively do this.
        int m = (end + start) >>> 1;
        return listProduct(nums, start, m).multiply(listProduct(nums, m, end));
    }
  }

  /**

      case 2:
        return nums.get(start).multiply(nums.get(start + 1));
      case 3:
        return nums.get(start).multiply(nums.get(start + 1)).multiply(nums.get(start + 2));
      default:
        // Otherwise, split the list in half and recursively do this.
        int m = (end + start) >>> 1;
        return listProduct(nums, start, m).multiply(listProduct(nums, m, end));
    }
  }

  /**

example.com/pgo/devirtualize/mult%2epkg.MultFn
48 91
example.com/pgo/devirtualize.ExerciseIface
example.com/pgo/devirtualize.Add.Add
49 55
example.com/pgo/devirtualize.ExerciseIface
example.com/pgo/devirtualize/mult%2epkg.Mult.Multiply
49 40
example.com/pgo/devirtualize.ExerciseFuncClosure
example.com/pgo/devirtualize.Sub.Add
18 14
example.com/pgo/devirtualize.ExerciseFuncField
example.com/pgo/devirtualize.SubFn
23 12

  // CHECK-DAG: %[[CST_BCAST:.+]] = "mhlo.broadcast_in_dim"(%[[CST]]) <{broadcast_dimensions = dense<3> : tensor<1xi64>}> : (tensor<2xf32>) -> tensor<1x1x3x2xf32>
  // CHECK-DAG: %[[NEW_FILTER:.+]] =  mhlo.multiply %[[CST_BCAST]], %[[FILTER]] : tensor<1x1x3x2xf32>

	op_VME    uint32 = 0xE7A6 // 	VRR-c	VECTOR MULTIPLY EVEN
	op_VMH    uint32 = 0xE7A3 // 	VRR-c	VECTOR MULTIPLY HIGH
	op_VMLE   uint32 = 0xE7A4 // 	VRR-c	VECTOR MULTIPLY EVEN LOGICAL
	op_VMLH   uint32 = 0xE7A1 // 	VRR-c	VECTOR MULTIPLY HIGH LOGICAL
	op_VMLO   uint32 = 0xE7A5 // 	VRR-c	VECTOR MULTIPLY ODD LOGICAL
	op_VML    uint32 = 0xE7A2 // 	VRR-c	VECTOR MULTIPLY LOW
	op_VMO    uint32 = 0xE7A7 // 	VRR-c	VECTOR MULTIPLY ODD

func NewScalar() *Scalar {
	return &Scalar{}
}

// MultiplyAdd sets s = x * y + z mod l, and returns s. It is equivalent to
// using Multiply and then Add.
func (s *Scalar) MultiplyAdd(x, y, z *Scalar) *Scalar {
	// Make a copy of z in case it aliases s.
	zCopy := new(Scalar).Set(z)
	return s.Multiply(x, y).Add(s, zCopy)
}

// Add sets s = x + y mod l, and returns s.
func (s *Scalar) Add(x, y *Scalar) *Scalar {
	// s = 1 * x + y mod l