// MODEL-DEBUG: %[[f_sqrt1:.*]] = "tfl.sqrt"(%[[f_conv]]
// MODEL-DEBUG: %[[q_sqrt1:.*]] = "tfl.sqrt"(%[[dq0]]
// MODEL-DEBUG: %[[q1:.*]] = "tfl.quantize"(%[[q_sqrt1]])
// MODEL-DEBUG: %[[dq1:.*]] = "tfl.dequantize"(%[[q1]])
// MODEL-DEBUG: %[[f_sqrt2:.*]] = "tfl.sqrt"(%[[f_sqrt1]])
// MODEL-DEBUG-NOT: debug_
// MODEL-DEBUG-SAME: (tensor<?x1x1x3xf32>)
// MODEL-DEBUG: %[[q_sqrt2:.*]] = "tfl.sqrt"(%[[dq1]]
// MODEL-DEBUG-NOT: debug_

// a slash between ImportPath and # in the anchored forms.
// For example, here are some baseURL values and URLs they can generate:
//
//	"/pkg/" → "/pkg/math/#Sqrt"
//	"/pkg"  → "/pkg/math#Sqrt"
//	"/"     → "/math/#Sqrt"
//	""      → "/math#Sqrt"
func (l *DocLink) DefaultURL(baseURL string) string {
	if l.ImportPath != "" {
		slash := ""
		if strings.HasSuffix(baseURL, "/") {
			slash = "/"
		} else {

    if (!fp_type) {
      return failure();
    }

    // result = (x - mean) * scale / sqrt(variance + epsilon) + offset
    // Let multiplier = scale / sqrt(variance + epsilon), to compute
    // (x - mean) * scale / sqrt(variance + epsilon) + offset,
    // is then to compute (x * multiplier) + (offset - mean * multiplier).

    auto epsilon = materializeEpsilon(

           const char* raw_device_name = nullptr);

// Computes square root of x element-wise.
Status Sqrt(AbstractContext* ctx, AbstractTensorHandle* const x,
            AbstractTensorHandle** y, const char* name = nullptr,
            const char* raw_device_name = nullptr);

// Computes the gradient for the sqrt of `x` wrt its input.
Status SqrtGrad(AbstractContext* ctx, AbstractTensorHandle* const y,

      <w>externref</w>
      <w>funcref</w>
      <w>jetbrains</w>
      <w>kotlinx</w>
      <w>ktor</w>
      <w>optref</w>
      <w>popcnt</w>
      <w>rotl</w>
      <w>rotr</w>
      <w>simd</w>
      <w>sqrt</w>
      <w>testsuite</w>
      <w>uninstantiable</w>
      <w>unintercepted</w>
      <w>unlinkable</w>
      <w>vtable</w>
      <w>wabt</w>
      <w>xopt</w>
    </words>
  </dictionary>

// operations. Specifically, performs the following calculation:
//
//   (x - mean) * scale / sqrt(variance + epsilon) + offset
//
// Let multiplier = scale / sqrt(variance + epsilon),
// to compute
//   (x - mean) * scale / sqrt(variance + epsilon) + offset,
// is then to compute
//   (x * multiplier) + (offset - mean * multiplier).
//

		// Check 1K total values, down from 64M.
		inc = 1 << 16
	}
	for i := 1; i < 1<<randomBitCount; i += inc {
		l, fl := math.Log2(float64(i)), runtime.Fastlog2(float64(i))
		d := l - fl
		e += d * d
	}
	e = math.Sqrt(e)

	if e > 1.0 {
		t.Fatalf("imprecision on fastlog2 implementation, want <=1.0, got %f", e)
	}

	return Vec2{v[0], v[0]}
}

func (v Vec2) B() Vec2 {
	return Vec2{1.0 / v.D(), 0}
}

func (v Vec2) C() Vec2 {
	return Vec2{v[0], v[0]}
}

func (v Vec2) D() float64 {
	return math.Sqrt(v[0])

//
// __ieee754_log(x)
// Return the logarithm of x
//
// Method :
//   1. Argument Reduction: find k and f such that
//			x = 2**k * (1+f),
//	   where  sqrt(2)/2 < 1+f < sqrt(2) .
//
//   2. Approximation of log(1+f).
//	Let s = f/(2+f) ; based on log(1+f) = log(1+s) - log(1-s)
//		 = 2s + 2/3 s**3 + 2/5 s**5 + .....,
//	     	 = 2s + s*R

}

func asin(x float64) float64 {
	if x == 0 {
		return x // special case
	}
	sign := false
	if x < 0 {
		x = -x
		sign = true
	}
	if x > 1 {
		return NaN() // special case
	}

	temp := Sqrt(1 - x*x)
	if x > 0.7 {
		temp = Pi/2 - satan(temp/x)
	} else {
		temp = satan(x / temp)
	}

	if sign {
		temp = -temp
	}
	return temp
}

// Acos returns the arccosine, in radians, of x.
//