#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

	"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

		{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: "