%0 = "tf.Const"() {value = dense<0.0> : tensor<f32>} : () -> tensor<f32>

f32], _support_output_type_float_in_quantized_op = true, detections_per_class = 100 : i64, device = "", h_scale = 5.000000e+00 : f32, max_classes_per_detection = 1 : i64, max_detections = 20 : i64, nms_iou_threshold = 6.000000e-01 : f32, nms_score_threshold = 3.000000e-01 : f32, num_classes = 90 : i64, use_regular_nms = false, w_scale = 5.000000e+00 : f32, x_scale = 1.000000e+01 : f32, y_scale = 1.000000e+01 : f32} : (tensor<1x64x64x32xf32>, tensor<1x64x64x32xf32>, tensor<1x64x64x32xf32>) -> (te...

			ObjectMeta: metav1.ObjectMeta{
				Name: "config",
			},
			Spec: admissionregistration.ValidatingAdmissionPolicyBindingSpec{
				PolicyName: "xyzlimit-scale.example.com",
				ParamRef: &admissionregistration.ParamRef{
					Name:                    "xyzlimit-scale-setting.example.com",
					ParameterNotFoundAction: ptr(admissionregistration.DenyAction),
				},
				MatchResources: &admissionregistration.MatchResources{

	if err := scaleUpStatefulSetControl(set, ssc, om, invariants); err != nil {
		t.Errorf("Failed to scale StatefulSet : %s", err)
	}
	var err error
	set, err = om.setsLister.StatefulSets(set.Namespace).Get(set.Name)
	if err != nil {
		t.Fatalf("Error getting updated StatefulSet: %v", err)
	}
	if set.Status.Replicas != 4 {
		t.Error("Failed to scale statefulset to 4 replicas")
	}
	if set.Status.ReadyReplicas != 4 {

var xxx_messageInfo_RollingUpdateStatefulSetStrategy proto.InternalMessageInfo

func (m *Scale) Reset()      { *m = Scale{} }
func (*Scale) ProtoMessage() {}
func (*Scale) Descriptor() ([]byte, []int) {
	return fileDescriptor_2747f709ac7c95e7, []int{12}
}
func (m *Scale) XXX_Unmarshal(b []byte) error {
	return m.Unmarshal(b)
}
func (m *Scale) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
	b = b[:cap(b)]

var xxx_messageInfo_RollingUpdateStatefulSetStrategy proto.InternalMessageInfo

func (m *Scale) Reset()      { *m = Scale{} }
func (*Scale) ProtoMessage() {}
func (*Scale) Descriptor() ([]byte, []int) {
	return fileDescriptor_c423c016abf485d4, []int{22}
}
func (m *Scale) XXX_Unmarshal(b []byte) error {
	return m.Unmarshal(b)
}
func (m *Scale) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
	b = b[:cap(b)]

}

// Creates a new `tfl.qconst` op for the bias. The bias values are 0s, because
// this bias a dummy bias (note that bias fusion is not considered for this
// transformation). The quantization scale for the bias is input scale *
// filter scale. `filter_const_op` is used to retrieve the filter scales and
// the size of the bias constant.
TFL::QConstOp CreateTflConstOpForDummyBias(

// Bias tensor's scale is input scale * filter scale.

</p>

<pre>
func (p *Point) Length() float64 {
	return math.Sqrt(p.x * p.x + p.y * p.y)
}

func (p *Point) Scale(factor float64) {
	p.x *= factor
	p.y *= factor
}
</pre>

<p>
bind the methods <code>Length</code> and <code>Scale</code>,
with receiver type <code>*Point</code>,
to the base type <code>Point</code>.
</p>

<p>

	})
}

type Point struct {
	x, y int
}

// This will be index 0.
func (p Point) AnotherMethod(scale int) int {
	return -1
}

// This will be index 1.
func (p Point) Dist(scale int) int {
	//println("Point.Dist", p.x, p.y, scale)
	return p.x*p.x*scale + p.y*p.y*scale
}

// This will be index 2.
func (p Point) GCMethod(k int) int {
	runtime.GC()
	return k + p.x
}