// 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(

// 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).
//

	case IsInf(x, 0):
		if IsInf(x, -1) {
			return Pow(1/x, -y) // Pow(-0, -y)
		}
		switch {
		case y < 0:
			return 0
		case y > 0:
			return Inf(1)
		}
	case y == 0.5:
		return Sqrt(x)
	case y == -0.5:
		return 1 / Sqrt(x)
	}

	yi, yf := Modf(Abs(y))
	if yf != 0 && x < 0 {
		return NaN()
	}
	if yi >= 1<<63 {
		// yi is a large even int that will lead to overflow (or underflow to 0)

	const width = 130
	const height = 50

	im := image.NewGray(image.Rectangle{Max: image.Point{X: width, Y: height}})
	for x := 0; x < width; x++ {
		for y := 0; y < height; y++ {
			dist := math.Sqrt(math.Pow(float64(x-width/2), 2)/3+math.Pow(float64(y-height/2), 2)) / (height / 1.5) * 255
			var gray uint8
			if dist > 255 {
				gray = 255
			} else {
				gray = uint8(dist)
			}

@app.route('/hello')
def hello():
    version = os.environ.get('SERVICE_VERSION')

    # do some cpu intensive computation
    x = 0.0001
    for i in range(0, 1000000):
        x = x + math.sqrt(x)

    return 'Hello version: %s, instance: %s\n' % (version, os.environ.get('HOSTNAME'))


@app.route('/health')
def health():
    return 'Helloworld is healthy', 200


if __name__ == "__main__":

  // CHECK-DAG: %[[EPS_BCAST:.+]] = mhlo.constant dense<1.001000e-05> : tensor<256xf32>
  // CHECK-DAG: %[[VARIANCE_EPS:.+]] = mhlo.add %[[VARIANCE]], %[[EPS_BCAST]] : tensor<256xf32>
  // CHECK-DAG: %[[STDDEV:.+]] = mhlo.sqrt %[[VARIANCE_EPS]] : tensor<256xf32>
  // CHECK-DAG: %[[STDDEV_BCAST:.+]] = "mhlo.broadcast_in_dim"(%[[STDDEV]]) <{broadcast_dimensions = dense<1> : tensor<1xi64>}> : (tensor<256xf32>) -> tensor<4x256xf32>

	}

	// Expect a uniform distribution of byte values, which lie in [0, 255].
	var (
		mean       = 255.0 / 2
		stddev     = 256.0 / math.Sqrt(12.0)
		errorScale = stddev / math.Sqrt(float64(n))
	)

	expected := &statsResults{mean, stddev, 0.10 * errorScale, 0.08 * errorScale}

	// Cast bytes as floats to use the common distribution-validity checks.

 private:
  AbstractTensorHandlePtr exp_;
};

class SqrtGradientFunction : public GradientFunction {
 public:
  explicit SqrtGradientFunction(AbstractTensorHandle* sqrt) : sqrt_(sqrt) {
    sqrt->Ref();
  }
  Status Compute(AbstractContext* ctx,
                 absl::Span<AbstractTensorHandle* const> grad_outputs,
                 absl::Span<AbstractTensorHandle*> grad_inputs) override {

   * R*R)} of the population standard deviation of {@code y}, where {@code R} is the Pearson's
   * correlation coefficient (as given by {@link #pearsonsCorrelationCoefficient()}).
   *
   * <p>The corresponding root-mean-square error in {@code x} as a function of {@code y} is a
   * fraction {@code sqrt(1/(R*R) - 1)} of the population standard deviation of {@code x}. This fit