func.return %0 : tensor<4xcomplex<f64>>
}

func.func @f16() -> tensor<4xf16> {
  // CHECK-LABEL: @f16
  // CHECK: value = dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00]> : tensor<4xf16>

	_ = complex(f32, f32)
	_ = complex(f32, 1)
	_ = complex(f32, 1.0)
	_ = complex(f32, 'a')
	_ = complex(f64, f64)
	_ = complex(f64, 1)
	_ = complex(f64, 1.0)
	_ = complex(f64, 'a')
	_ = complex(f32 /* ERROR "mismatched types" */ , f64)
	_ = complex(f64 /* ERROR "mismatched types" */ , f32)
	_ = complex(1, 1)
	_ = complex(1, 1.1)
	_ = complex(1, 'a')
	complex /* ERROR "not used" */ (1, 2)

  func.return %0 : tensor<4xcomplex<f64>>
}

func.func @f16() -> tensor<4xf16> {
  // CHECK-LABEL: @f16
  // CHECK: value = dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00]> : tensor<4xf16>

// CHECK-SAME: (%[[ARG0:.+]]: tensor<f64>)
func.func @min_with_f64_max_val(%arg0 : tensor<f64>) -> (tensor<f64>, tensor<f64>) {
  %f64_max = arith.constant dense<1.7976931348623157E+308> : tensor<f64>
  %0 = "tfl.minimum"(%arg0, %f64_max) : (tensor<f64>, tensor<f64>) -> tensor<f64>
  %1 = "tfl.minimum"(%f64_max, %arg0) : (tensor<f64>, tensor<f64>) -> tensor<f64>
  func.return %0, %1 : tensor<f64>, tensor<f64>
  // CHECK: return %[[ARG0]], %[[ARG0]]

	// ppc64x/power9:"FMOVS\t[$]f32\\.42440000\\(SB\\)"
	// ppc64x/power10:"XXSPLTIDP\t[$]1111752704,"
	return 49.0
}

func Float64Constant() float64 {
	// ppc64x/power8:"FMOVD\t[$]f64\\.4048800000000000\\(SB\\)"
	// ppc64x/power9:"FMOVD\t[$]f64\\.4048800000000000\\(SB\\)"
	// ppc64x/power10:"XXSPLTIDP\t[$]1111752704,"
	return 49.0
}

func Float32DenormalConstant() float32 {
	// ppc64x:"FMOVS\t[$]f32\\.00400000\\(SB\\)"

		fmt.Printf("  %s", mode)
	}
	fmt.Println()

	for _, f64 := range operands {
		fmt.Printf("%4g", f64)
		for mode := big.ToNearestEven; mode <= big.ToPositiveInf; mode++ {
			// sample operands above require 2 bits to represent mantissa
			// set binary precision to 2 to round them to integer values
			f := new(big.Float).SetPrec(2).SetMode(mode).SetFloat64(f64)
			fmt.Printf("  %*g", len(mode.String()), f)
		}
		fmt.Println()

// -----

func.func @nonstatic_shape_mhlo() -> tensor<?xi32> attributes {tf.entry_function = {control_outputs = "", inputs = "i,j", outputs = "identity_RetVal"}} {
  %0 = mhlo.constant dense<1.000000e+00> : tensor<f64>
  %1 = mhlo.convert %0 : (tensor<f64>) -> tensor<i64>
  %2 = mhlo.reshape %1 : (tensor<i64>) -> tensor<1xi64>
  // expected-error @+1 {{Node `mhlo.dynamic_iota` must have compile-time constant}}

}

// CHECK-LABEL: QuantizeAssignVariableWithDequantAndNotEqualType
func.func @QuantizeAssignVariableWithDequantAndNotEqualType(%arg0 : tensor<1x2x1x3x!quant.uniform<i8:f64, 1.0>>) -> tensor<1x2x1x3x!quant.uniform<i8:f64, 1.0>> {
  %1 = "tfl.var_handle"() : () -> tensor<!tf_type.resource>
  %2 = "tfl.read_variable"(%1) : (tensor<!tf_type.resource>) -> tensor<1x2x1x3xf32>

  // CHECK-LABEL: mirror_pad
  func.func @mirror_pad(%arg0: tensor<2x3xcomplex<f64>>) -> tensor<4x7xcomplex<f64>> {
    %0 = mhlo.constant dense<[[1, 1], [2, 2]]> : tensor<2x2xi32>
    // CHECK-NOT: tf.MirrorPad
    %1 = "tf.MirrorPad"(%arg0, %0) {mode = "SYMMETRIC"} : (tensor<2x3xcomplex<f64>>, tensor<2x2xi32>) -> tensor<4x7xcomplex<f64>>
    func.return %1 : tensor<4x7xcomplex<f64>>
  }

  // CHECK-LABEL: bucketize

func fnma(x, y, z float64) float64 {
	// riscv64:"FNMADDD",-"FNMSUBD"
	return math.FMA(x, -y, -z)
}

func fromFloat64(f64 float64) uint64 {
	// amd64:"MOVQ\tX.*, [^X].*"
	// arm64:"FMOVD\tF.*, R.*"
	// ppc64x:"MFVSRD"
	// mips64/hardfloat:"MOVV\tF.*, R.*"
	return math.Float64bits(f64+1) + 1
}

func fromFloat32(f32 float32) uint32 {
	// amd64:"MOVL\tX.*, [^X].*"
	// arm64:"FMOVS\tF.*, R.*"