return status
}

// RunScorePlugins runs the set of configured scoring plugins.
// It returns a list that stores scores from each plugin and total score for each Node.
// It also returns *Status, which is set to non-success if any of the plugins returns
// a non-success status.

	// Reduce saturation near score=0 (so it is colored grey, rather than yellow).
	if math.Abs(score) < 0.2 {
		saturation *= math.Abs(score) / 0.2
	}

	// Apply 'shift' to move scores away from 0.0 (grey).
	if score > 0.0 {
		score = math.Pow(score, (1.0 - shift))
	}
	if score < 0.0 {
		score = -math.Pow(-score, (1.0 - shift))
	}

	var r, g, b float64 // red, green, blue

	PreemptVerb string
	// Verb for the prioritize call, empty if not supported. This verb is appended to the URLPrefix when issuing the prioritize call to extender.
	PrioritizeVerb string
	// The numeric multiplier for the node scores that the prioritize call generates.
	// The weight should be a positive integer
	Weight int64
	// Verb for the bind call, empty if not supported. This verb is appended to the URLPrefix when issuing the bind call to extender.

using the `tf.gather operation`.  For example:
  selected_indices = tf.image.non_max_suppression_v2(
      boxes, scores, max_output_size, iou_threshold, score_threshold)
  selected_boxes = tf.gather(boxes, selected_indices)
  }];

  let arguments = (ins
    TFL_FpTensor:$boxes,
    TFL_FpTensor:$scores,
    TFL_I32Tensor:$max_output_size,
    TFL_FpTensor:$iou_threshold,
    TFL_FpTensor:$score_threshold
  );

				return int64(math.MinInt64)
			}
			// The bigger the earliestStartTimeOnNode, the higher the score.
			return earliestStartTimeOnNode.UnixNano()
		}

		// Each scoreFunc scores the nodes according to specific rules and keeps the name of the node
		// with the highest score. If and only if the scoreFunc has more than one node with the highest

			},
			failedNodes: []*v1.Node{},
			objs: []runtime.Object{
				&v1.Service{Spec: v1.ServiceSpec{Selector: map[string]string{"foo": ""}}},
			},
			want: []framework.NodeScore{
				// Same scores as if we were using one spreading constraint.
				{Name: "node-a", Score: 44},
				{Name: "node-b", Score: 66},
				{Name: "node-c", Score: 77},
				{Name: "node-d", Score: 100},
			},
		},
		{

			if !status.IsSuccess() {
				t.Errorf("unexpected error: %v", status)
			}

			if diff := cmp.Diff(test.expectedList, gotList); diff != "" {
				t.Errorf("obtained scores (-want,+got):\n%s", diff)
			}
		})
	}
}

func Test_isSchedulableAfterNodeChange(t *testing.T) {
	podWithNodeAffinity := st.MakePod().NodeAffinityIn("foo", []string{"bar"})

				if availableAfterAllocation[numa] >= cpuGroupSize {
					remainderCombo = append(remainderCombo, numa)
				}
			}

			// Declare a set of local variables to help track the "balance
			// scores" calculated when using different subsets of
			// 'remainderCombo' to allocate remainder CPUs from.
			var bestLocalBalance float64 = math.MaxFloat64
			var bestLocalRemainder []int = nil

lawyers can talk about it for five minutes, without coming to a total disagreement as to all the premises. Innumerable children have been born into the cause; innumerable young people have married into it; innumerable old people have died out of it. Scores of persons have deliriously found themselves made parties in Jarndyce and Jarndyce, without knowing how or why; whole families have inherited legendary hatreds with the suit. The little plaintiff or defendant, who was promised a new rocking-horse...

    Res<TensorOf<[TF_Float16, TF_Float32]>, [{A 1-D float tensor of shape `[M]` representing the corresponding
scores for each selected box, where `M <= max_output_size`.  Scores only differ
from corresponding input scores when using Soft NMS (i.e. when
`soft_nms_sigma>0`)}]>:$selected_scores,
    Res<TF_Int32Tensor, [{A 0-D integer tensor representing the number of valid elements in