double rmin = rmins[axis];
    double rmax = rmaxs[axis];
    if (std::fabs(rmax - rmin) < std::numeric_limits<double>::epsilon()) {
      scales.push_back(1.0);
      zeroPoints.push_back(qmin);
      continue;
    }

    double scale;
    int64_t nudgedZeroPoint;
    getNudgedScaleAndZeroPoint(qmin, qmax, rmin, rmax, scale, nudgedZeroPoint);
    scales.push_back(scale);

          // supports only symmetric quantization.
          rmax = std::max(std::abs(rmin), std::abs(rmax));
          rmin = -rmax;
        }
        TensorRangeSanityCheck(op, rmin, rmax);
        mins.push_back(rmin);
        maxs.push_back(rmax);
      }
      quant_type = quantfork::fakeQuantAttrsToType(
          op.getLoc(), num_bits, *op.getAxis(), mins, maxs, narrow_range,
          expressed, is_signed);

                                       std::optional<double> rmax, int32_t qmin,
                                       int32_t qmax) {
  auto quantize = [scale, zero_point](float f) {
    return zero_point + static_cast<int32_t>(std::round(f / scale));
  };

  if (rmin.has_value() && rmax.has_value()) {
    return {std::max(qmin, quantize(rmin.value())),
            std::min(qmax, quantize(rmax.value()))};
  } else if (rmin.has_value()) {

      maxs[channel_index] = std::max(maxs[channel_index], ele_value);
    }
    // Expand range to include 0.
    for (int i = 0; i < dim_size; ++i) {
      maxs[i] = std::max(maxs[i], 0.0);
      mins[i] = std::min(mins[i], 0.0);
    }
    if (symmetric) {
      for (int i = 0; i < dim_size; ++i) {
        maxs[i] = std::max(std::abs(mins[i]), std::abs(maxs[i]));
        mins[i] = -maxs[i];

	// arm64:"FMINS"
	// riscv64:"FMINS"
	// ppc64/power9:"XSMINJDP"
	// ppc64/power10:"XSMINJDP"
	return min(a, b)
}

func Float32Max(a, b float32) float32 {
	// amd64:"MINSS"
	// arm64:"FMAXS"
	// riscv64:"FMAXS"
	// ppc64/power9:"XSMAXJDP"
	// ppc64/power10:"XSMAXJDP"
	return max(a, b)
}

// ------------------------ //
//  Constant Optimizations  //
// ------------------------ //

/// originating op.
quant::UniformQuantizedType fakeQuantAttrsToType(Location loc, unsigned numBits,
                                                 double rmin, double rmax,
                                                 bool narrowRange,
                                                 Type expressedType,
                                                 bool isSigned = false);

	"FRCSR",
	"FSCSR",
	"FRRM",
	"FSRM",
	"FRFLAGS",
	"FSFLAGS",
	"FSRMI",
	"FSFLAGSI",
	"FLW",
	"FSW",
	"FADDS",
	"FSUBS",
	"FMULS",
	"FDIVS",
	"FMINS",
	"FMAXS",
	"FSQRTS",
	"FMADDS",
	"FMSUBS",
	"FNMADDS",
	"FNMSUBS",
	"FCVTWS",
	"FCVTLS",
	"FCVTSW",
	"FCVTSL",
	"FCVTWUS",
	"FCVTLUS",
	"FCVTSWU",
	"FCVTSLU",
	"FSGNJS",

      SmallVector<double, 4> maxs(1, std::numeric_limits<double>::min());
      // Computes the effective min/max values of the attribute values.
      quant::ExtractMinMaxFromAttr(attr, /*dim_size=*/1, /*slice_size=*/1,
                                   /*symmetric=*/true, mins, maxs);
      double scale = maxs[0] / -llvm::minIntN(tensor_property.number_of_bits);

  }
  auto mins = tensor.quantization->min;
  auto maxs = tensor.quantization->max;
  if (mins.size() != maxs.size() || mins.empty()) return nullptr;

  llvm::SmallVector<llvm::APFloat, 4> min_maxs;
  min_maxs.reserve(mins.size() * 2);
  for (int i = 0, end = mins.size(); i < end; ++i) {
    llvm::APFloat min(mins[i]);
    llvm::APFloat max(maxs[i]);
    min_maxs.push_back(min);
    min_maxs.push_back(max);
  }

	FADDS	F1, F0, F2				// 53011000
	FSUBS	F1, F0, F2				// 53011008
	FMULS	F1, F0, F2				// 53011010
	FDIVS	F1, F0, F2				// 53011018
	FMINS	F1, F0, F2				// 53011028
	FMAXS	F1, F0, F2				// 53111028
	FSQRTS	F0, F1					// d3000058

	// 11.7: Single-Precision Floating-Point Conversion and Move Instructions
	FCVTWS	F0, X5					// d31200c0
	FCVTWS.RNE	F0, X5				// d30200c0