,[0-9]{1,3}%,[0-9]{1,3}%,[0,1]{1}.?[0-9]*\)/i;if(e.match(f)){var g=e.replace(/\(/g,"").replace(/\)/g,""),h=g.split(","),i=!0;return h.forEach(function(a,c){var e=b(a);Number.isInteger(e)?0===c?(d=0<=e&&e<=360,d||(i=!1)):(d=0<=e&&e<=100,d||(i=!1)):isNaN(e)?(e=parseInt(a),d=0<=e&&e<=100,d||(i=!1)):(d=0<=e&&e<=1,d||(i=!1))}),i}return!1},errorMessage:"",errorMessageKey:"badHsla"})}(a)});...

//
// Special cases are:
//
//	Erfinv(1) = +Inf
//	Erfinv(-1) = -Inf
//	Erfinv(x) = NaN if x < -1 or x > 1
//	Erfinv(NaN) = NaN
func Erfinv(x float64) float64 {
	// special cases
	if IsNaN(x) || x <= -1 || x >= 1 {
		if x == -1 || x == 1 {
			return Inf(int(x))
		}
		return NaN()
	}

	sign := false
	if x < 0 {
		x = -x
		sign = true
	}

	var ans float64

	for i := 0; i < len(fs); i++ {
		testfs := Clone(fs)
		testfs[i] = math.NaN()

		fmin := Min(testfs)
		if !math.IsNaN(fmin) {
			t.Errorf("got min %v, want NaN", fmin)
		}

		fmax := Max(testfs)
		if !math.IsNaN(fmax) {
			t.Errorf("got max %v, want NaN", fmax)
		}
	}
}

func TestMinMaxPanics(t *testing.T) {
	intCmp := func(a, b int) int { return a - b }

  tensorflow::histogram::Histogram histo;
  for (int64_t i = 0; i < TF_TensorElementCount(safe_values_ptr.get()); ++i) {
    const double double_val = static_cast<double>(values_array[i]);
    if (Eigen::numext::isnan(double_val)) {
      std::ostringstream err;
      err << "Nan in summary histogram for: " << k->op_node_name;
      TF_SetStatus(status.get(), TF_INVALID_ARGUMENT, err.str().c_str());

// Note: On systems where Sqrt is a single instruction, the compiler
// may turn a direct call into a direct use of that instruction instead.

func sqrt(x float64) float64 {
	// special cases
	switch {
	case x == 0 || IsNaN(x) || IsInf(x, 1):
		return x
	case x < 0:
		return NaN()
	}
	ix := Float64bits(x)
	// normalize x
	exp := int((ix >> shift) & mask)
	if exp == 0 { // subnormal x
		for ix&(1<<shift) == 0 {

		Lp6         = 1.531383769920937332e-01     // 3FC39A09D078C69F
		Lp7         = 1.479819860511658591e-01     // 3FC2F112DF3E5244
	)

	// special cases
	switch {
	case x < -1 || IsNaN(x): // includes -Inf
		return NaN()
	case x == -1:
		return Inf(-1)
	case IsInf(x, 1):
		return Inf(1)
	}

	absx := Abs(x)

	var f float64
	var iu uint64
	k := 1

//	Gamma(-Inf) = NaN
//	Gamma(NaN) = NaN
func Gamma(x float64) float64 {
	const Euler = 0.57721566490153286060651209008240243104215933593992 // A001620
	// special cases
	switch {
	case isNegInt(x) || IsInf(x, -1) || IsNaN(x):
		return NaN()
	case IsInf(x, 1):
		return Inf(1)
	case x == 0:
		if Signbit(x) {
			return Inf(-1)
		}
		return Inf(1)
	}
	q := Abs(x)
	p := Floor(q)
	if q > 33 {
		if x >= 0 {

  auto y = Div(scope, x, Sub(scope, x, x));
  float max_error;
  TF_ASSERT_OK((ComputeGradientError<float, float, float>(
      scope, {x}, {shape}, {y}, {shape}, &max_error)));
  EXPECT_TRUE(std::isnan(max_error));
}

TEST(GradientCheckerTest, MatMulGrad) {
  Scope scope = Scope::NewRootScope();

  TensorShape x_shape({4, 3});
  TensorShape y_shape({3, 2});
  TensorShape z_shape({4, 2});

		Q4 = 4.00821782732936239552e-06  // 0x3ED0CFCA86E65239
		Q5 = -2.01099218183624371326e-07 // 0xBE8AFDB76E09C32D
	)

	// special cases
	switch {
	case IsInf(x, 1) || IsNaN(x):
		return x
	case IsInf(x, -1):
		return -1
	}

	absx := x
	sign := false
	if x < 0 {
		absx = -absx
		sign = true
	}

	// filter out huge argument

 TensorArrayV3 8
 TensorArrayWriteV3 9
 Unique 2
 VariableV2 164
 _Retval 5
cluster 0 size 440
 Abs 40
 AddN 1
 Any 41
 Cast 40
 ConcatV2 2
 Const 95
 ExpandDims 2
 IsInf 1
 IsNan 40
 L2Loss 40
 LogicalOr 1
 Max 41
 Minimum 1
 Mul 82
 Pack 3
 Reciprocal 2
 Reshape 2
 ReverseSequence 1
 Sqrt 1
 Sum 1
 Transpose 3
cluster 1 size 86
 BroadcastGradientArgs 1
 Cast 5