if err != nil {
		return 0, framework.AsStatus(err)
	}

	score := int64(countIntolerableTaintsPreferNoSchedule(node.Spec.Taints, s.tolerationsPreferNoSchedule))
	return score, nil
}

// NormalizeScore invoked after scoring all nodes.
func (pl *TaintToleration) NormalizeScore(ctx context.Context, _ *framework.CycleState, pod *v1.Pod, scores framework.NodeScoreList) *framework.Status {

	KubeProxyOOMScoreAdj  int = -999
	guaranteedOOMScoreAdj int = -997
	besteffortOOMScoreAdj int = 1000
)

// GetContainerOOMScoreAdjust returns the amount by which the OOM score of all processes in the
// container should be adjusted.
// The OOM score of a process is the percentage of memory it consumes
// multiplied by 10 (barring exceptional cases) + a configurable quantity which is between -1000

 * governing permissions and limitations under the License.
 */
package org.codelibs.fess.score;

import java.util.Arrays;
import java.util.Map;
import java.util.function.Function;

import org.apache.logging.log4j.LogManager;
import org.apache.logging.log4j.Logger;
import org.codelibs.core.lang.StringUtil;
import org.codelibs.fess.es.client.SearchEngineClient;

		score, tmask = adjustScore(panicPathAdj, score, tmask)
	}
	if csflags&CallSiteInInitFunc != 0 {
		score, tmask = adjustScore(initFuncAdj, score, tmask)
	}

	// Then adjustments to encourage inlining in selected cases.
	if csflags&CallSiteInLoop != 0 {
		score, tmask = adjustScore(inLoopAdj, score, tmask)
	}

	// Stop here if no callee props.
	if calleeProps == nil {
		cs.Score, cs.ScoreMask = score, tmask

		count += s.preferredNodeAffinity.Score(node)
	}

	return count, nil
}

// NormalizeScore invoked after scoring all nodes.
func (pl *NodeAffinity) NormalizeScore(ctx context.Context, state *framework.CycleState, pod *v1.Pod, scores framework.NodeScoreList) *framework.Status {
	return helper.DefaultNormalizeScore(framework.MaxNodeScore, false, scores)
}

// ScoreExtensions of the Score plugin.

// two different tags may represent the same language.
//
// By default, only legacy and deprecated tags are converted into their
// canonical equivalent. All other information is preserved. This approach makes
// the confidence scores more accurate and allows matchers to distinguish
// between variants that are otherwise lost.
//
// As a consequence, two tags that should be treated as identical according to

}

// storeOrder orders values with respect to stores. That is,
// if v transitively depends on store s, v is ordered after s,
// otherwise v is ordered before s.
// Specifically, values are ordered like
//
//	store1
//	NilCheck that depends on store1
//	other values that depends on store1
//	store2
//	NilCheck that depends on store2
//	other values that depends on store2
//	...
//

	// Shape specifies the points defining the score function shape, which is
	// used to score nodes based on the utilization of statically provisioned
	// PVs. The utilization is calculated by dividing the total requested
	// storage of the pod by the total capacity of feasible PVs on each node.
	// Each point contains utilization (ranges from 0 to 100) and its
	// associated score (ranges from 0 to 10). You can turn the priority by

	// or phrases begin with a single word on the previous line.

	// A score is the score (also called weight) for a given line.
	// add and cmp add and compare scores.
	type score struct {
		hi int64
		lo int64
	}
	add := func(s, t score) score { return score{s.hi + t.hi, s.lo + t.lo} }
	cmp := func(s, t score) int {
		switch {
		case s.hi < t.hi:
			return -1
		case s.hi > t.hi:
			return +1

            dim {
              size: -1
            }
            dim {
              size: 1
            }
          }
        }
      }
      outputs {
        key: "scores"
        value {
          name: "y3:0"
          dtype: DT_FLOAT
          tensor_shape {
            dim {
              size: -1
            }
            dim {
              size: 1