%res2 = "tf.DivNoNan"(%arg0, %comp2) : (tensor<2xcomplex<f32>>, tensor<2xcomplex<f32>>) -> tensor<2xcomplex<f32>>
  %res3 = "tf.MulNoNan"(%arg0, %comp3) : (tensor<2xcomplex<f32>>, tensor<2xcomplex<f32>>) -> tensor<2xcomplex<f32>>
  func.return %res1, %res2, %res3 : tensor<2xcomplex<f32>>, tensor<2xcomplex<f32>>, tensor<2xcomplex<f32>>
}

    tf_device.return
  }) : () -> ()
  func.return
}

  // CHECK: "mhlo.fft"(%arg0) <{fft_length = dense<8> : tensor<1xi64>, fft_type = #mhlo<fft_type IFFT>}> : (tensor<8xcomplex<f32>>
  %0 = "tf.IFFT"(%arg0) : (tensor<8xcomplex<f32>>) -> tensor<8xcomplex<f32>>
  func.return %0 : tensor<8xcomplex<f32>>
}

// -----

// CHECK-LABEL: func @rfft_1D
func.func @rfft_1D(%arg0: tensor<8xf32>) -> tensor<5xcomplex<f32>> {

  func.return %0 : tensor<5x4xf32>
}

func.func @batchmatmulv2_adj_complex(%arg0: tensor<2x5xcomplex<f32>>, %arg1: tensor<4x2xcomplex<f32>>) -> tensor<5x4xcomplex<f32>> {
// CHECK-LABEL:   func @batchmatmulv2_adj_complex(
// CHECK-SAME:                                    [[LHS:%.*]]: tensor<2x5xcomplex<f32>>, [[RHS:%.*]]: tensor<4x2xcomplex<f32>>) -> tensor<5x4xcomplex<f32>> {
// CHECK:           [[LHSRE:%.*]] = mhlo.real [[LHS]]

}

// CHECK-LABEL:   func @complex(
// CHECK-SAME:                  %[[VAL_0:.*]]: tensor<3xf32>,
// CHECK-SAME:                  %[[VAL_1:.*]]: tensor<3xf32>) -> tensor<3xcomplex<f32>> {
// CHECK:           %[[VAL_2:.*]] = "tf.Complex"(%[[VAL_0]], %[[VAL_1]]) : (tensor<3xf32>, tensor<3xf32>) -> tensor<3xcomplex<f32>>
// CHECK:           return %[[VAL_2]] : tensor<3xcomplex<f32>>
// CHECK:         }

	g := 0.0
	if isInf(f) {
		g = 1.0
	}
	return copysign(g, f)
}

func complex128div(n complex128, m complex128) complex128 {
	var e, f float64 // complex(e, f) = n/m

	// Algorithm for robust complex division as described in
	// Robert L. Smith: Algorithm 116: Complex division. Commun. ACM 5(8): 435 (1962).
	if abs(real(m)) >= abs(imag(m)) {
		ratio := imag(m) / real(m)
		denom := real(m) + ratio*imag(m)

	floatZero   = constant.ToFloat(intZero)
	complexZero = constant.ToComplex(intZero)
)

// NewOne returns an OLITERAL representing 1 with the given type.
func NewOne(pos src.XPos, typ *types.Type) Node {
	var val constant.Value
	switch {
	case typ.IsInteger():
		val = intOne
	case typ.IsFloat():
		val = floatOne
	case typ.IsComplex():
		val = complexOne
	default:

func tocplx(v constant.Value) constant.Value {
	return constant.ToComplex(v)
}

func toflt(v constant.Value) constant.Value {
	if v.Kind() == constant.Complex {
		v = constant.Real(v)
	}

	return constant.ToFloat(v)
}

func toint(v constant.Value) constant.Value {
	if v.Kind() == constant.Complex {
		v = constant.Real(v)
	}

	if v := constant.ToInt(v); v.Kind() == constant.Int {
		return v
	}

  func.return %0 : tensor<4xcomplex<f32>>
}

func.func @complex128() -> tensor<4xcomplex<f64>> {
  // CHECK-LABEL: @complex128

// CHECK-SAME: (%[[Y:.*]]: tensor<*xcomplex<f32>>, %[[DY:.*]]: tensor<*xcomplex<f32>>)
func.func @tanhgrad_complex(%y : tensor<*xcomplex<f32>>, %dy: tensor<*xcomplex<f32>>) -> tensor<*xcomplex<f32>> {
  // CHECK-NOT: tf.TanhGrad
  %0 = "tf.TanhGrad"(%y, %dy) : (tensor<*xcomplex<f32>>, tensor<*xcomplex<f32>>) -> tensor<*xcomplex<f32>>

  func.return %0 : tensor<*xcomplex<f32>>
}

// CHECK-LABEL: func @ZerosLike_unranked