function nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
	  if (iouThreshold === void 0) {
	    iouThreshold = 0.5;
	  }

	  if (scoreThreshold === void 0) {
	    scoreThreshold = Number.NEGATIVE_INFINITY;
	  }

	  if (softNmsSigma === void 0) {
	    softNmsSigma = 0.0;
	  }

	  var $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');
	  var $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');
	  var params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
	  maxOutputSize = params.maxOutputSize;
	  iouThreshold = params.iouThreshold;
	  scoreThreshold = params.scoreThreshold;
	  softNmsSigma = params.softNmsSigma;
	  var inputs = {
	    boxes: $boxes,
	    scores: $scores
	  };
	  var attrs = {
	    maxOutputSize: maxOutputSize,
	    iouThreshold: iouThreshold,
	    scoreThreshold: scoreThreshold,
	    softNmsSigma: softNmsSigma
	  }; // tslint:disable-next-line: no-unnecessary-type-assertion

	  var result = ENGINE.runKernel(NonMaxSuppressionV5, inputs, attrs);
	  return {
	    selectedIndices: result[0],
	    selectedScores: result[1]
	  };
	}
Performs non maximum suppression of bounding boxes based on
iou (intersection over union).

This op also supports a Soft-NMS mode (c.f.
Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score
of other overlapping boxes, therefore favoring different regions of the image
with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`
parameter to be larger than 0.

@param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is
    `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of
    the bounding box.
@param scores a 1d tensor providing the box scores of shape `[numBoxes]`.
@param maxOutputSize The maximum number of boxes to be selected.
@param iouThreshold A float representing the threshold for deciding whether
    boxes overlap too much with respect to IOU. Must be between [0, 1].
    Defaults to 0.5 (50% box overlap).
@param scoreThreshold A threshold for deciding when to remove boxes based
    on score. Defaults to -inf, which means any score is accepted.
@param softNmsSigma A float representing the sigma parameter for Soft NMS.
    When sigma is 0, it falls back to nonMaxSuppression.
@return A map with the following properties:
    - selectedIndices: A 1D tensor with the selected box indices.
    - selectedScores: A 1D tensor with the corresponding scores for each
      selected box.

@doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}