}
	return inj.ScoreRes, nil
}

func injectNormalizeRes(inj injectedResult, scores framework.NodeScoreList) *framework.Status {
	if framework.Code(inj.NormalizeStatus) != framework.Success {
		return framework.NewStatus(framework.Code(inj.NormalizeStatus), "injecting failure.")
	}
	for i := range scores {
		scores[i].Score = inj.NormalizeRes
	}
	return nil
}

		},
		{
			scores:         []int64{0, 0, 0, 0},
			expectedScores: []int64{0, 0, 0, 0},
		},
		{
			reverse:        true,
			scores:         []int64{0, 0, 0, 0},
			expectedScores: []int64{100, 100, 100, 100},
		},
	}

	for i, test := range tests {
		t.Run(fmt.Sprintf("case_%d", i), func(t *testing.T) {
			scores := framework.NodeScoreList{}
			for _, score := range test.scores {

		args         config.NodeResourcesBalancedAllocationArgs
		runPreScore  bool
	}{
		{
			// Node1 scores (remaining resources) on 0-MaxNodeScore scale
			// CPU Fraction: 0 / 4000 = 0%
			// Memory Fraction: 0 / 10000 = 0%
			// Node1 Score: (1-0) * MaxNodeScore = MaxNodeScore
			// Node2 scores (remaining resources) on 0-MaxNodeScore scale
			// CPU Fraction: 0 / 4000 = 0 %
			// Memory Fraction: 0 / 10000 = 0%

// scores from [0, max(scores)] to [0, maxPriority]. If reverse is set to true, it
// reverses the scores by subtracting it from maxPriority.
// Note: The input scores are always assumed to be non-negative integers.
func DefaultNormalizeScore(maxPriority int64, reverse bool, scores framework.NodeScoreList) *framework.Status {
	var maxCount int64
	for i := range scores {
		if scores[i].Score > maxCount {

	}
}

func (m scoreMap) append(other scoreMap) {
	for topology, oScores := range other {
		scores := m[topology]
		if scores == nil {
			m[topology] = oScores
			continue
		}
		for k, v := range oScores {
			scores[k] += v
		}
	}
}

func (pl *InterPodAffinity) processExistingPod(
	state *preScoreState,
	existingPod *framework.PodInfo,

		wantErrs       field.ErrorList
		wantStatusCode framework.Code
	}{
		{
			// Node1 scores (remaining resources) on 0-MaxNodeScore scale
			// CPU Score: ((4000 - 0) * MaxNodeScore) / 4000 = MaxNodeScore
			// Memory Score: ((10000 - 0) * MaxNodeScore) / 10000 = MaxNodeScore
			// Node1 Score: (100 + 100) / 2 = 100
			// Node2 scores (remaining resources) on 0-MaxNodeScore scale

		wantErrs       field.ErrorList
		wantStatusCode framework.Code
	}{
		{
			// Node1 scores (used resources) on 0-MaxNodeScore scale
			// CPU Score: (0 * MaxNodeScore)  / 4000 = 0
			// Memory Score: (0 * MaxNodeScore) / 10000 = 0
			// Node1 Score: (0 + 0) / 2 = 0
			// Node2 scores (used resources) on 0-MaxNodeScore scale
			// CPU Score: (0 * MaxNodeScore) / 4000 = 0

			name:           "nothing scheduled, nothing requested",
		},
		{
			// Node1 scores (used resources) on 0-MaxNodeScore scale
			// Node1 Score:
			// rawScoringFunction(used + requested / available)
			// resourceScoringFunction((0+2),8)
			//  = 2/8 * maxUtilization = 25 = rawScoringFunction(25)
			// Node1 Score: 2
			// Node2 scores (used resources) on 0-MaxNodeScore scale
			// rawScoringFunction(used + requested / available)

		if s.IgnoredNodes.Has(score.Name) {
			scores[i].Score = invalidScore
			continue
		}
		if score.Score < minScore {
			minScore = score.Score
		}
		if score.Score > maxScore {
			maxScore = score.Score
		}
	}

	for i := range scores {
		if scores[i].Score == invalidScore {
			scores[i].Score = 0
			continue
		}
		if maxScore == 0 {
			scores[i].Score = framework.MaxNodeScore
			continue

	// Prioritize based on extender-implemented priority functions. The returned scores & weight
	// are used to compute the weighted score for an extender. The weighted scores are added to
	// the scores computed by Kubernetes scheduler. The total scores are used to do the host selection.
	Prioritize(pod *v1.Pod, nodes []*NodeInfo) (hostPriorities *extenderv1.HostPriorityList, weight int64, err error)