%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...

	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 {

  }

  // Check output shape for non-batch dimension, following documentation below.
  // https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/batch-mat-mul
  int64_t x_row_dim = x_shape[x_shape.size() - 2];
  int64_t x_col_dim = x_shape[x_shape.size() - 1];
  int64_t y_row_dim = y_shape[y_shape.size() - 2];
  int64_t y_col_dim = y_shape[y_shape.size() - 1];
  int64_t out_row_dim = output_shape[output_shape.size() - 2];

</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
}

"description": "Resources is a list of resources this rule applies to.\n\nFor example: 'pods' means pods. 'pods/log' means the log subresource of pods. '*' means all resources, but not subresources. 'pods/*' means all subresources of pods. '*/scale' means all scale subresources. '*/*' means all resources and their subresources.\n\nIf wildcard is present, the validation rule will ensure resources do not overlap with each other.\n\nDepending on the enclosing object, subresources might not be allowed. Required.",...

			ab.asmidx(ctxt, int(a.Scale), REG_NONE, base)
			return
		}

		if disp8, ok := toDisp8(v, p, ab); ok {
			ab.Put1(byte(1<<6 | reg[base]<<0 | r<<3))
			ab.asmidx(ctxt, int(a.Scale), REG_NONE, base)
			ab.Put1(disp8)
			return
		}

		ab.Put1(byte(2<<6 | reg[base]<<0 | r<<3))
		ab.asmidx(ctxt, int(a.Scale), REG_NONE, base)
		goto putrelv
	}

          },
          "group": {
            "description": "group is the preferred group of the resource.  Empty implies the group of the containing resource list. For subresources, this may have a different value, for example: Scale\".",
            "type": "string"
          },
          "kind": {
            "default": "",
            "description": "kind is the kind for the resource (e.g. 'Foo' is the kind for a resource 'foo')",

          },
          "group": {
            "description": "group is the preferred group of the resource.  Empty implies the group of the containing resource list. For subresources, this may have a different value, for example: Scale\".",
            "type": "string"
          },
          "kind": {
            "default": "",
            "description": "kind is the kind for the resource (e.g. 'Foo' is the kind for a resource 'foo')",