SataQiu <******@****.***> 1713430345 +0800

SataQiu <******@****.***> 1702613527 +0800

	MULA    R1, R2, R4, R4
	// w = R2:R1 in nSec
	MOVW    R3, R1	      // R4:R3 -> R2:R1
	MOVW    R4, R2

	// multiply nanoseconds by reciprocal of 10**9 (scaled by 2**61)
	// to get seconds (96 bit scaled result)
	MOVW	$0x89705f41, R3		// 2**61 * 10**-9
	MULLU	R1,R3,(R6,R5)		// R7:R6:R5 = R2:R1 * R3
	MOVW	$0,R7
	MULALU	R2,R3,(R7,R6)

	// unscale by discarding low 32 bits, shifting the rest by 29

	// if behavior is specified, we should fill all the 'nil' values with the default ones
	if obj.Spec.Behavior != nil {
		obj.Spec.Behavior.ScaleUp = GenerateHPAScaleUpRules(obj.Spec.Behavior.ScaleUp)
		obj.Spec.Behavior.ScaleDown = GenerateHPAScaleDownRules(obj.Spec.Behavior.ScaleDown)
	}
}

// GenerateHPAScaleUpRules returns a fully-initialized HPAScalingRules value

	// if behavior is specified, we should fill all the 'nil' values with the default ones
	if obj.Spec.Behavior != nil {
		obj.Spec.Behavior.ScaleUp = GenerateHPAScaleUpRules(obj.Spec.Behavior.ScaleUp)
		obj.Spec.Behavior.ScaleDown = GenerateHPAScaleDownRules(obj.Spec.Behavior.ScaleDown)
	}
}

// GenerateHPAScaleUpRules returns a fully-initialized HPAScalingRules value

			sum += scaledImageScore(state, totalNumNodes)
		}
	}
	return sum
}

// scaledImageScore returns an adaptively scaled score for the given state of an image.
// The size of the image is used as the base score, scaled by a factor which considers how much nodes the image has "spread" to.
// This heuristic aims to mitigate the undesirable "node heating problem", i.e., pods get assigned to the same or

    func.func @main(%arg0 : tensor<1xf32>) -> tensor<1xf32> {
      %scales = "tf.Const"() { value = dense<1.0> : tensor<f32> } : () -> tensor<f32>
      %zps = "tf.Const"() { value = dense<3> : tensor<i32> } : () -> tensor<i32>

      %0 = "tf.UniformQuantize"(%arg0, %scales, %zps) {
        quantization_axis = -1 : i64, quantization_min_val = -128 : i64, quantization_max_val = 127 : i64

func.func @illegal_tf_uniform_quantize(%arg0 : tensor<1xf32>) -> tensor<1xf32> {
  %scales = "tf.Const"() { value = dense<1.0> : tensor<f32> } : () -> tensor<f32>
  %zps = "tf.Const"() { value = dense<3> : tensor<i32> } : () -> tensor<i32>

  // expected-error@+1 {{'tf.UniformQuantize' op is illegal as it is a UQ op or contains uq/qint types}}
  %0 = "tf.UniformQuantize"(%arg0, %scales, %zps) {

class FixedResultScale<QuantizedType qt> : NativeOpTrait<!strconcat(
  "quant::FixedResult", qt.name, "Scale<", qt.asTraitArgsStr, ">::Impl")>;

// Specify this trait if the bias-th input of the op is a bias input, which
// needs a scale based on the scales of op1 and op2.
class AccumulatorUniformScale<int bias, int op1, int op2> : NativeOpTrait<
  !strconcat("quant::AccumulatorUniformScale<",

    @Override
    double singleQuantile(int index, int scale, double[] dataset) {
      return Quantiles.scale(scale).index(index).computeInPlace(dataset);
    }

    @Override
    Map<Integer, Double> multipleQuantiles(
        Collection<Integer> indexes, int scale, double[] dataset) {
      return Quantiles.scale(scale).indexes(indexes).computeInPlace(dataset);
    }
  },
  ;

  /**