# CHECK:       quantization: {
# CHECK:         scale: [ 0.093635 ],
# CHECK:         zero_point: [ 22 ]
# CHECK:       }
# CHECK:     }, {
# CHECK:       shape: [ 1, 6, 31 ],
# CHECK:       type: INT8,
# CHECK:       buffer: 6,
# CHECK:       name: "output",
# CHECK:       quantization: {
# CHECK:         scale: [ 0.093635 ],
# CHECK:         zero_point: [ 22 ]
# CHECK:       }

	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 {

             "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
}

        input_tensor->quantization->scale.clear();
        input_tensor->quantization->zero_point.clear();
        input_tensor->quantization->min.push_back(-1.0);
        input_tensor->quantization->max.push_back(1.0);

        auto& output_tensor = sub_graph->tensors[op->outputs[0]];
        auto shape = output_tensor->shape;
        output_tensor->quantization->scale.clear();

// It is controlled by "k8s:prerelease-lifecycle-gen:introduced" tags in types.go.
func (in *Scale) APILifecycleIntroduced() (major, minor int) {
	return 1, 2

			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"))
		}
	}
}

	}
	return intOrPercent
}

// GetScaledValueFromIntOrPercent is meant to replace GetValueFromIntOrPercent.
// This method returns a scaled value from an IntOrString type. If the IntOrString
// is a percentage string value it's treated as a percentage and scaled appropriately
// in accordance to the total, if it's an int value it's treated as a simple value and

}

// 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)
}