}

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

// ScoreExtensions of the Score plugin.
func (pl *TaintToleration) ScoreExtensions() framework.ScoreExtensions {
	return pl
}

// 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
// and 1000. Containers with higher OOM scores are killed if the system runs out of memory.
// See https://lwn.net/Articles/391222/ for more information.
func GetContainerOOMScoreAdjust(pod *v1.Pod, container *v1.Container, memoryCapacity int64) int {

        if (bulkRequestBuilder != null) {
            final BulkResponse response = bulkRequestBuilder.execute().actionGet(requestTimeout);
            if (response.hasFailures()) {
                logger.warn("Failed to update scores: {}", response.buildFailureMessage());
            }
            bulkRequestBuilder = null;
        }
    }

    public int getPriority() {
        return this.priority;
    }

	returnFeedsInlinableFuncToIndCallAdj
	returnFeedsConcreteToInterfaceCallAdj

	sentinelScoreAdj // sentinel; not a real adjustment
)

// This table records the specific values we use to adjust call
// site scores in a given scenario.
// NOTE: these numbers are chosen very arbitrarily; ideally
// we will go through some sort of turning process to decide
// what value for each one produces the best performance.

}

// 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.
func (pl *NodeAffinity) ScoreExtensions() framework.ScoreExtensions {
	return pl
}

// 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

	// Each point contains utilization (ranges from 0 to 100) and its
	// associated score (ranges from 0 to 10). You can turn the priority by
	// specifying different scores for different utilization numbers.
	// The default shape points are:
	// 1) 0 for 0 utilization
	// 2) 10 for 100 utilization
	// All points must be sorted in increasing order by utilization.

	return x
}
func (h ValHeap) Less(i, j int) bool {
	x := h.a[i]
	y := h.a[j]
	sx := h.score[x.ID]
	sy := h.score[y.ID]
	if c := sx - sy; c != 0 {
		return c < 0 // lower scores come earlier.
	}
	// Note: only scores are required for correct scheduling.
	// Everything else is just heuristics.

	ix := h.inBlockUses[x.ID]
	iy := h.inBlockUses[y.ID]
	if ix != iy {

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

	// it makes the text easier to skim if not too many sentences
	// 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