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__":

	MOVQ    $~(1<<63), AX
	ANDQ    AX, BX // p = |p|
	MOVQ    q+8(FP), CX
	ANDQ    AX, CX // q = |q|
	MOVQ    $PosInf, AX
	CMPQ    AX, BX
	JLE     isInfOrNaN
	CMPQ    AX, CX
	JLE     isInfOrNaN
	// hypot = max * sqrt(1 + (min/max)**2)
	MOVQ    BX, X0
	MOVQ    CX, X1
	ORQ     CX, BX
	JEQ     isZero
	MOVAPD  X0, X2
	MAXSD   X1, X0
	MINSD   X2, X1
	DIVSD   X0, X1
	MULSD   X1, X1
	ADDSD   $1.0, X1
	SQRTSD  X1, X1

  ZerosLike \
  Shape \
  ExpandDims \
  OnesLike

${generate} \
  --category=math \
  Mul \
  Conj \
  AddV2 \
  MatMul \
  Neg \
  Sum \
  Sub \
  Div \
  DivNoNan \
  Exp \
  Sqrt \
  SqrtGrad \
  Log1p

${generate} \
  --category=nn \
  SparseSoftmaxCrossEntropyWithLogits \
  ReluGrad \
  Relu \
  BiasAdd \
  BiasAddGrad

${generate} \
  --category=resource_variable \

  // 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>

//   ******@****.***

// Complex natural logarithm
//
// DESCRIPTION:
//
// Returns complex logarithm to the base e (2.718...) of
// the complex argument z.
//
// If
//       z = x + iy, r = sqrt( x**2 + y**2 ),
// then
//       w = log(r) + i arctan(y/x).
//
// The arctangent ranges from -PI to +PI.
//
// ACCURACY:
//
//                      Relative error:

// ComplexAbs is a helper type that defines an Abs method for
// complex types.
type ComplexAbs[T Complex] T

func (a ComplexAbs[T]) Abs() ComplexAbs[T] {
	r := float64(real(a))
	i := float64(imag(a))
	d := math.Sqrt(r * r + i * i)
	return ComplexAbs[T](complex(d, 0))
}

func OrderedAbsDifference[T OrderedNumeric](a, b T) T {
	return T(AbsDifference(OrderedAbs[T](a), OrderedAbs[T](b)))
}

// // complex types.
// type complexAbs[T Complex] T
//
// func (a complexAbs[T]) Abs() complexAbs[T] {
// 	r := float64(real(a))
// 	i := float64(imag(a))
// 	d := math.Sqrt(r*r + i*i)
// 	return complexAbs[T](complex(d, 0))
// }
//
// // OrderedAbsDifference returns the absolute value of the difference
// // between a and b, where a and b are of an ordered type.

// ComplexAbs is a helper type that defines an Abs method for
// complex types.
type ComplexAbs[T Complex] T

func (a ComplexAbs[T]) Abs() ComplexAbs[T] {
	r := float64(real(a))
	i := float64(imag(a))
	d := math.Sqrt(r * r + i * i)
	return ComplexAbs[T](complex(d, 0))
}

func OrderedAbsDifference[T OrderedNumeric](a, b T) T {
	return T(AbsDifference(OrderedAbs[T](a), OrderedAbs[T](b)))
}

// // complex types.
// type ComplexAbs[T Complex] T
// 
// func (a ComplexAbs[T]) Abs() ComplexAbs[T] {
// 	r := float64(real(a))
// 	i := float64(imag(a))
// 	d := math.Sqrt(r * r + i * i)
// 	return ComplexAbs[T](complex(d, 0))
// }
// 
// func OrderedAbsDifference[T OrderedNumeric](a, b T) T {
// 	return T(AbsDifference(OrderedAbs[T](a), OrderedAbs[T](b)))
// }
//