#define ASSERT_DOUBLE_EQUAL(actual, expected, granularity) { if ((fabs((double)actual - expected) > fabs((double)granularity))) { CU_assertImplementation(FALSE, __LINE__, ("ASSERT_DOUBLE_EQUAL(" #actual ","  #expected "," #granularity ")"), __FILE__, "", FALSE); return; }}
/** Deprecated (version 1). @deprecated Use CU_ASSERT_DOUBLE_NOT_EQUAL_FATAL. */

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

	case INT:
		if inst.Opcode>>24 == 0xCC {
			args = nil
			op = "int3"
		}

	case LCALL, LJMP:
		if len(args) == 2 {
			args[0], args[1] = args[1], args[0]
		}

	case FCHS, FABS, FTST, FLDPI, FLDL2E, FLDLG2, F2XM1, FXAM, FLD1, FLDL2T, FSQRT, FRNDINT, FCOS, FSIN:
		if len(args) == 0 {
			args = append(args, "st0")
		}

	"DECQ",
	"DECW",
	"DIVB",
	"DIVL",
	"DIVPD",
	"DIVPS",
	"DIVQ",
	"DIVSD",
	"DIVSS",
	"DIVW",
	"DPPD",
	"DPPS",
	"EMMS",
	"ENTER",
	"EXTRACTPS",
	"F2XM1",
	"FABS",
	"FADDD",
	"FADDDP",
	"FADDF",
	"FADDL",
	"FADDW",
	"FBLD",
	"FBSTP",
	"FCHS",
	"FCLEX",
	"FCMOVB",
	"FCMOVBE",
	"FCMOVCC",
	"FCMOVCS",
	"FCMOVE",
	"FCMOVEQ",

	FSUB	F3, F12                // b31b00c3
	FMULS	F4, F11                // b31700b4
	FMUL	F5, F10                // b31c00a5
	FDIVS	F6, F9                 // b30d0096
	FDIV	F7, F8                 // b31d0087
	FABS	F1, F2                 // b3100021
	FSQRTS	F3, F4                 // b3140043
	FSQRT	F5, F15                // b31500f5
	FIEBR	$0, F0, F1             // b3570010
	FIDBR	$7, F2, F3             // b35f7032

				} else if rno >= uint16(S0) && rno <= uint16(S31) {
					op = fmt.Sprintf("%sS", op)
				} else if rno >= uint16(D0) && rno <= uint16(D31) {
					op = fmt.Sprintf("%sD", op)
				}
			}
		}

	case FABS, FNEG, FSQRT, FRINTN, FRINTP, FRINTM, FRINTZ, FRINTA, FRINTX, FRINTI:
		if r, ok := inst.Args[1].(Reg); ok {
			rno := uint16(r)
			if rno >= uint16(S0) && rno <= uint16(S31) {
				op = fmt.Sprintf("%sS", op)

		{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

    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: "