svg[loc[1]:]
	}

	if loc := graphID.FindStringIndex(svg); loc != nil {
		svg = svg[:loc[0]] +
			`<script type="text/ecmascript"><![CDATA[` + svgpan.JSSource + `]]></script>` +
			`<g id="viewport" transform="scale(0.5,0.5) translate(0,0)">` +
			svg[loc[0]:]
	}

	if loc := svgClose.FindStringIndex(svg); loc != nil {
		svg = svg[:loc[0]] +
			`</g>` +
			svg[loc[0]:]
	}

	return svg

		gen := atomic.Load(&th.gen) // atomic_load_acq_32
		bt = th.offset

		if tc, ok := th.getTimecounter(); !ok {
			return zeroBintime
		} else {
			delta := (tc - th.offset_count) & th.counter_mask
			bt.AddX(th.scale * uint64(delta))
		}
		if abs {
			bt.Add(&th.boottime)
		}

		atomic.Load(&binuptimeDummy) // atomic_thread_fence_acq()
		if curr == timekeepSharedPage.current && gen != 0 && gen == th.gen {
			break
		}

}

// Scale represents a scaling request for a resource.
message Scale {
  // Standard object metadata; More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#metadata.
  // +optional
  optional k8s.io.apimachinery.pkg.apis.meta.v1.ObjectMeta metadata = 1;

}

// Scale represents a scaling request for a resource.
message Scale {
  // Standard object metadata; More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#metadata.
  // +optional
  optional .k8s.io.apimachinery.pkg.apis.meta.v1.ObjectMeta metadata = 1;

	for _, successCase := range successCases {
		if errs := ValidateScale(&successCase); len(errs) != 0 {
			t.Errorf("expected success: %v", errs)
		}
	}

	errorCases := []struct {
		scale autoscaling.Scale
		msg   string
	}{{
		scale: autoscaling.Scale{
			ObjectMeta: metav1.ObjectMeta{
				Name:      "frontend",
				Namespace: metav1.NamespaceDefault,
			},
			Spec: autoscaling.ScaleSpec{
				Replicas: -1,
			},
		},

    class WhileModel(module.Module):
      """A model with a while op."""

      def __init__(self):
        w_shape = [3, 3] + [input_shape[-1], input_shape[-1]]
        self.w = np.random.uniform(low=-2, high=2, size=w_shape).astype('f4')

      @def_function.function
      def condition(self, x, w):
        return math_ops.reduce_sum(x, keepdims=False) < 100

        auto shape = GetAsVector(quant_tensor->shape());
        if (kUseUpdatedHybridSchemeDefault) {
          EXPECT_EQ(quant_tensor->quantization()->scale()->size(), shape[0]);
        } else {
          EXPECT_EQ(quant_tensor->quantization()->scale()->size(), 1);
        }
      } else {
        EXPECT_EQ(quant_tensor->type(), TensorType_FLOAT32);
      }
    }

        if (op->hasOneUse() &&
            op->user_begin()->hasTrait<OpTrait::IsTerminator>())
          return failure();
      }
      // If the quantize op is a requantize op, it is being used in other scale
      // adjustments and should be kept. Instead, moving dequantize op before
      // the requantize op to remove the unnecessary requantize op.
      if (auto qtype =

		argLen:         2,
		hasSideEffects: true,
		generic:        true,
	},
}

func (o Op) Asm() obj.As          { return opcodeTable[o].asm }
func (o Op) Scale() int16         { return int16(opcodeTable[o].scale) }
func (o Op) String() string       { return opcodeTable[o].name }
func (o Op) SymEffect() SymEffect { return opcodeTable[o].symEffect }

            "type": "string"
          },
          "replicas": {