"enable post training quantization. Only used in tests">,
      Option<"legacy_float_scale_", "legacy-float-scale", "bool", "false",
             "calculate quantization scales in float instead of double">,
      Option<"disable_per_channel_", "disable-per-channel", "bool", "false",
             "Whether disable per-channel quantized weights.">,

	// reference to scaled resource; horizontal pod autoscaler will learn the current resource consumption
	// and will set the desired number of pods by using its Scale subresource.
	ScaleTargetRef CrossVersionObjectReference `json:"scaleTargetRef" protobuf:"bytes,1,opt,name=scaleTargetRef"`
	// minReplicas is the lower limit for the number of replicas to which the autoscaler

	if config.MaxPerCore != nil && *config.MaxPerCore > 0 {
		floor := 0
		if config.Min != nil {
			floor = int(*config.Min)
		}
		scaled := int(*config.MaxPerCore) * detectNumCPU()
		if scaled > floor {
			logger.V(3).Info("GetConntrackMax: using scaled conntrack-max-per-core")
			return scaled, nil
		}
		logger.V(3).Info("GetConntrackMax: using conntrack-min")
		return floor, nil
	}
	return 0, nil
}

			t.Errorf(onPolicy("Failed to scale StatefulSet back up: %v", err))
		}
		set, err = om.setsLister.StatefulSets(set.Namespace).Get(set.Name)
		if err != nil {
			t.Errorf(onPolicy("Could not get scaled down StatefulSet: %v", err))
		}
		if set.Status.Replicas != 3 {
			t.Errorf(onPolicy("Failed to scale set back up once PVC was deleted"))
		}
	}
}

}

// Scale multiplies all sample values in a profile by a constant and keeps
// only samples that have at least one non-zero value.
func (p *Profile) Scale(ratio float64) {
	if ratio == 1 {
		return
	}
	ratios := make([]float64, len(p.SampleType))
	for i := range p.SampleType {
		ratios[i] = ratio
	}
	p.ScaleN(ratios)
}

	//fmt.Printf("testing nsamples=%v mean=%v stddev=%v seed=%v\n", nsamples, mean, stddev, seed);

	samples := generateNormalSamples(nsamples, mean, stddev, seed)
	errorScale := max(1.0, stddev) // Error scales with stddev
	expected := &statsResults{mean, stddev, 0.10 * errorScale, 0.08 * errorScale}

	// Make sure that the entire set matches the expected distribution.
	checkSampleDistribution(t, samples, expected)

        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);
      }
    }

						return Quantity{i: int64Amount{value: result, scale: Scale(scale)}, Format: format, s: str}, nil
					}
				default:
					if scale%3 == 0 && !strings.HasSuffix(shifted, "000") && shifted[0] != '0' {
						return Quantity{i: int64Amount{value: result, scale: Scale(scale)}, Format: format, s: str}, nil
					}
				}
				return Quantity{i: int64Amount{value: result, scale: Scale(scale)}, Format: format}, nil
			}
		}
	}

}

TFE_MonitoringBuckets* TFE_MonitoringNewExponentialBuckets(double scale,
                                                           double growth_factor,
                                                           int bucket_count) {
  return new TFE_MonitoringBuckets([scale, growth_factor, bucket_count]() {
    return tensorflow::monitoring::Buckets::Exponential(scale, growth_factor,

			return false
		}
	case policy.WhenScaled == delete && policy.WhenDeleted == delete:
		podScaledDown := !podInOrdinalRange(pod, set)
		// If a pod is scaled down, there should be no set ref and a pod ref;
		// if the pod is not scaled down it's the other way around.
		if podScaledDown == hasOwnerRef(claim, set) {
			return false
		}
		if podScaledDown != hasOwnerRef(claim, pod) {
			return false
		}