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

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

	  if (padToMaxOutputSize === void 0) {
	    padToMaxOutputSize = false;
	  }

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

	  var result = ENGINE.runKernel(NonMaxSuppressionV4, inputs, attrs);
	  return {
	    selectedIndices: result[0],
	    validOutputs: result[1]
	  };
	}
Asynchronously performs non maximum suppression of bounding boxes based on
iou (intersection over union), with an option to pad results.

@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 padToMaxOutputSize Defalts to false. If true, size of output
    `selectedIndices` is padded to maxOutputSize.
@return A map with the following properties:
    - selectedIndices: A 1D tensor with the selected box indices.
    - validOutputs: A scalar denoting how many elements in `selectedIndices`
      are valid. Valid elements occur first, then padding.

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