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()

    %i8_max_f64 = "tf.Cast"(%i8_max) : (tensor<f32>) -> tensor<f64>

    %range_nume = "tf.Sub"(%r_max_f64, %r_min_f64) : (tensor<1xf64>, tensor<1xf64>) -> tensor<1xf64>
    %range_deno = "tf.Sub"(%i8_max_f64, %i8_min_f64) : (tensor<f64>, tensor<f64>) -> tensor<f64>

    %scale_f64 = "tf.Div"(%range_nume, %range_deno) : (tensor<1xf64>, tensor<f64>) -> tensor<1xf64>

package main

func main() {
	var b [8]byte
	one := uint8(1)
	f16(&one, b[:2])
	if b[1] != 1 {
		println("2-byte value lost")
	}
	f32(&one, b[:4])
	if b[3] != 1 {
		println("4-byte value lost")
	}
	f64(&one, b[:8])
	if b[7] != 1 {
		println("8-byte value lost")
	}
}

//go:noinline
func f16(p *uint8, b []byte) {
	_ = b[1]            // bounds check
	x := *p             // load a byte

		panic("divide")
	}
}

func f64(x, y, q, r int64) {
	if t := x / y; t != q {
		fmt.Printf("%d/%d = %d, want %d\n", x, y, t, q)
		panic("divide")
	}
	if t := x % y; t != r {
		fmt.Printf("%d%%%d = %d, want %d\n", x, y, t, r)
		panic("divide")
	}
}

func main() {
	f8(-1<<7, -1, -1<<7, 0)
	f16(-1<<15, -1, -1<<15, 0)
	f32(-1<<31, -1, -1<<31, 0)
	f64(-1<<63, -1, -1<<63, 0)

	f53 = f52 * 53
	f54 = f53 * 54
	f55 = f54 * 55
	f56 = f55 * 56
	f57 = f56 * 57
	f58 = f57 * 58
	f59 = f58 * 59
	f60 = f59 * 60
	f61 = f60 * 61
	f62 = f61 * 62
	f63 = f62 * 63
	f64 = f63 * 64
	f65 = f64 * 65
	f66 = f65 * 66
	f67 = f66 * 67
	f68 = f67 * 68
	f69 = f68 * 69
	f70 = f69 * 70
	f71 = f70 * 71
	f72 = f71 * 72
	f73 = f72 * 73
	f74 = f73 * 74
	f75 = f74 * 75
	f76 = f75 * 76

// -----

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}}

func (v TF) String() string   { return Sprintf("F: %f", float64(v)) }
func (v TF32) String() string { return Sprintf("F32: %f", float32(v)) }
func (v TF64) String() string { return Sprintf("F64: %f", float64(v)) }
func (v TB) String() string   { return Sprintf("B: %t", bool(v)) }
func (v TS) String() string   { return Sprintf("S: %q", string(v)) }

func check(t *testing.T, got, want string) {
	if got != want {

}

// CHECK-LABEL: not_fold_cast_fft_to_rfft
func.func @not_fold_cast_fft_to_rfft(%arg0: tensor<10x20x30xcomplex<f64>>) -> tensor<10x20x30xcomplex<f32>> {
  %0 = "tf.Cast"(%arg0) : (tensor<10x20x30xcomplex<f64>>) -> tensor<10x20x30xcomplex<f32>>
  %1 = "tf.FFT"(%0) : (tensor<10x20x30xcomplex<f32>>) -> tensor<10x20x30xcomplex<f32>>
  func.return %1: tensor<10x20x30xcomplex<f32>>

			return 0, false
		}
		e, err1 := strconv.Atoi(exp)
		if err1 != nil {
			println("bad p", exp)
			return 0, false
		}
		return float32(float64(n) * pow2(e)), true
	}
	f64, err1 := strconv.ParseFloat(s, 32)
	f1 := float32(f64)
	if err1 != nil {
		return 0, false
	}
	return f1, true
}

func TestFp(t *testing.T) {
	f, err := os.Open("testdata/testfp.txt")
	if err != nil {

	u32, v32, w32 uint32  = 0, 0, 1
	u64, v64, w64 uint64  = 0, 0, 1
	up, vp, wp    uintptr = 0, 0, 1

	f, g, h                         float64 = 0, 0, 1
	f32, g32, h32                   float32 = 0, 0, 1
	f64, g64, h64, inf, negInf, nan float64 = 0, 0, 1, math.Inf(1), math.Inf(-1), math.NaN()

	c, d, e          complex128 = 0 + 0i, 0 + 0i, 1 + 1i
	c64, d64, e64    complex64  = 0 + 0i, 0 + 0i, 1 + 1i