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;

  }

  // 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];

	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,
			},
		},

        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": {

		for i, iMax := 0, bounds.Max.X-bounds.Min.X; i < iMax; i++ {
			img.Pix[po+4*i+0] = d.img3.Y[yo+i]
			img.Pix[po+4*i+1] = d.img3.Cb[co+i/cScale]
			img.Pix[po+4*i+2] = d.img3.Cr[co+i/cScale]
			img.Pix[po+4*i+3] = 255
		}
	}
	return img, nil
}

// Decode reads a JPEG image from r and returns it as an [image.Image].
func Decode(r io.Reader) (image.Image, error) {
	var d decoder