static constexpr char kClassifyMethodName[] = "tensorflow/serving/classify";

/// Classification classes output.
static constexpr char kClassifyOutputClasses[] = "classes";

/// Classification scores output.
static constexpr char kClassifyOutputScores[] = "scores";

////////////////////////////////////////////////////////////////////////////////
/// Predict API constants.

/// Predict inputs.
static constexpr char kPredictInputs[] = "inputs";

		wg.Wait()
		for i := range nodesScores {
			if score, ok := allNodeExtendersScores[nodes[i].Node().Name]; ok {
				nodesScores[i].Scores = append(nodesScores[i].Scores, score.Scores...)
				nodesScores[i].TotalScore += score.TotalScore
			}
		}
	}

	if loggerVTen.Enabled() {
		for i := range nodesScores {

                resultBuf.append(b.getClass().getSimpleName()).append(" : ").append(count).append('\n');
            } catch (final Exception e) {
                logger.warn("Failed to update scores.", e);
                resultBuf.append(e.getMessage()).append('\n');
            }
        });
        return resultBuf.toString();
    }

    protected void addScoreBooster(final ScoreBooster scoreBooster) {

See the License for the specific language governing permissions and
limitations under the License.
*/

package oom

import (
	"errors"
)

var unsupportedErr = errors.New("setting OOM scores is unsupported in this build")

func NewOOMAdjuster() *OOMAdjuster {
	return &OOMAdjuster{
		ApplyOOMScoreAdj:          unsupportedApplyOOMScoreAdj,
		ApplyOOMScoreAdjContainer: unsupportedApplyOOMScoreAdjContainer,
	}

	}
	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
}

}

// sumImageScores returns the sum of image scores of all the containers that are already on the node.
// Each image receives a raw score of its size, scaled by scaledImageScore. The raw scores are later used to calculate
// the final score.
func sumImageScores(nodeInfo *framework.NodeInfo, pod *v1.Pod, totalNumNodes int) int64 {
	var sum int64

// NodePluginScores is a struct with node name and scores for that node.
type NodePluginScores struct {
	// Name is node name.
	Name string
	// Scores is scores from plugins and extenders.
	Scores []PluginScore
	// TotalScore is the total score in Scores.
	TotalScore int64
}

// PluginScore is a struct with plugin/extender name and score.
type PluginScore struct {
	// Name is the name of plugin or extender.

}

}  // namespace

void ConvertNMSPaddedFunc::RewriteFunc() {
  func_->setAttr(kTFImplements,
                 StringAttr::get(func_.getContext(), kTfNMSPadded));
  Value boxes = func_.getArgument(0);
  Value scores = func_.getArgument(1);
  Value max_output_size = func_.getArgument(2);
  Value iou_threshold = func_.getArgument(3);
  Value score_threshold = func_.getArgument(4);
  auto output_type0 = func_.getFunctionType().getResult(0);

					framework.NewStatus(framework.Error, "this score function shouldn't be executed because this plugin returned Skip in the PreScore"),
				), "PreScore", "Score"),
			},
			extenders: nil,
			want: []framework.NodePluginScores{
				{Name: "node1", Scores: []framework.PluginScore{}},
				{Name: "node2", Scores: []framework.PluginScore{}},
			},
		},
		{
			name: "the score from Image Locality plugin with image in all nodes",

def LegalizeNonMaxSuppressionV4 : Pat<
  (TF_NonMaxSuppressionV4Op $boxes, $scores, $max_output_size, $iou_threshold,
    $score_threshold, $pad_to_max_output_size),
  (TFL_NonMaxSuppressionV4Op $boxes, $scores, $max_output_size, $iou_threshold,
    $score_threshold)>;

def LegalizeNonMaxSuppressionV5 : Pat<
  (TF_NonMaxSuppressionV5Op $boxes, $scores, $max_output_size, $iou_threshold,