namespace tensorflow {
namespace {

// TODO(andydavis) Support returning relative error (as opposed to max error)
// between theoretical and numerical jacobians:
//   fabs(jac_t - jac_n) / max(fabs(jac_t), fabs(jac_n))

// TODO(andydavis) Vectorize and/or multi-thread Jacobian computations if
// performance becomes an issue.

// BaseUnitsForType provides a list of typed unit values for each basis in the

}

// Check that it's using integer registers
func abs(x, y float64) {
	// amd64:"BTRQ\t[$]63"
	// arm64:"FABSD\t"
	// s390x:"LPDFR\t",-"MOVD\t"     (no integer load/store)
	// ppc64x:"FABS\t"
	// riscv64:"FABSD\t"
	// wasm:"F64Abs"
	// arm/6:"ABSD\t"
	// mips64/hardfloat:"ABSD\t"
	// mips/hardfloat:"ABSD\t"
	sink64[0] = math.Abs(x)

	// amd64:"BTRQ\t[$]63","PXOR"    (TODO: this should be BTSQ)

		{name: "FROUND", argLength: 1, reg: fp11, asm: "FRIN"},                                          // round(arg0), float64
		{name: "FABS", argLength: 1, reg: fp11, asm: "FABS"},                                            // abs(arg0), float64
		{name: "FNABS", argLength: 1, reg: fp11, asm: "FNABS"},                                          // -abs(arg0), float64

(Sqrt ...) => (FSQRT ...)
(Sqrt32 ...) => (FSQRTS ...)
(Floor ...) => (FFLOOR ...)
(Ceil ...) => (FCEIL ...)
(Trunc ...) => (FTRUNC ...)
(Round ...) => (FROUND ...)
(Copysign x y) => (FCPSGN y x)
(Abs ...) => (FABS ...)
(FMA ...) => (FMADD ...)

// Lowering extension
// Note: we always extend to 64 bits even though some ops don't need that many result bits.
(SignExt8to(16|32|64) ...) => (MOVBreg ...)

    new_scales.reserve(scales.size());
    auto scales_iter = scales.begin();
    for (const auto& f : factor_values) {
      new_scales.push_back(*scales_iter *
                           std::fabs(FloatAttr::getValueAsDouble(f)));
      ++scales_iter;
    }
    // We are assuming symmetric quantization.
    auto new_ele_type = quant::UniformQuantizedPerAxisType::get(

  // the storage type is less than or equal to 8 bits.
  void TensorRangeSanityCheck(quantfork::StatisticsOp op, double& min,
                              double& max) const {
    double range = std::fabs(max - min);
    if (num_bits <= 8 && range >= 10.0) {
      op.emitWarning()
          << "Tensor range is too wide to be quantized. Use tf.clip_by_value "
             "or tf.relu6 to narrow the tensor range. Range: "

	FNMSUBS F1, F2, F3, F4          // ec8110fc
	FNMSUBSCC F1, F2, F3, F4        // ec8110fd
	FSEL F1, F2, F3, F4             // fc8110ee
	FSELCC F1, F2, F3, F4           // fc8110ef
	FABS F1, F2                     // fc400a10
	FNABS F1, F2                    // fc400910
	FABSCC F1, F2                   // fc400a11
	FNABSCC F1, F2                  // fc400911
	FNEG F1, F2                     // fc400850

	case 32: /* fadd fr1,[fr2],fr3 */
		r := p.Reg
		if r == obj.REG_NONE {
			r = p.To.Reg
		}
		o1 = OP_FRRR(c.oprrr(p.As), p.From.Reg, r, p.To.Reg)

	case 33: /* fabs fr1, fr3 */
		o1 = OP_FRRR(c.oprrr(p.As), obj.REG_NONE, p.From.Reg, p.To.Reg)

	case 34: /* mov $con,fr ==> or/add $i,t; mov t,fr */
		a := AADDU
		if o.a1 == C_ANDCON {
			a = AOR
		}

	case 32: // fadd fr1,[fr2],fr3
		r := int(p.Reg)
		if r == 0 {
			r = int(p.To.Reg)
		}
		o1 = OP_RRR(c.oprrr(p.As), uint32(p.From.Reg), uint32(r), uint32(p.To.Reg))

	case 33: // fabs fr1, fr3
		o1 = OP_RRR(c.oprrr(p.As), uint32(0), uint32(p.From.Reg), uint32(p.To.Reg))

	case 34: // mov $con,fr
		v := c.regoff(&p.From)
		a := AADDU
		if o.from1 == C_ANDCON {
			a = AOR
		}

}
func rewriteValuePPC64_OpPPC64FNEG(v *Value) bool {
	v_0 := v.Args[0]
	// match: (FNEG (FABS x))
	// result: (FNABS x)
	for {
		if v_0.Op != OpPPC64FABS {
			break
		}
		x := v_0.Args[0]
		v.reset(OpPPC64FNABS)
		v.AddArg(x)
		return true
	}
	// match: (FNEG (FNABS x))
	// result: (FABS x)
	for {
		if v_0.Op != OpPPC64FNABS {
			break
		}
		x := v_0.Args[0]