code: def regular_non_max_suppression(
      boxes,
      scores,
      max_detections_per_class,
      max_detections,
      num_classes,
      iou_threshold,
      score_threshold,
  ):
      """Regular non-maximum suppression operator for object detection, corresponding to TFLite's
      regular NMS. NMS is performed for each class separately.
  
      Parameters
      ----------
      boxes : relay.Expr
          3-D tensor with shape (batch_size, num_boxes, 4). The four values in boxes
          encode (ymin, xmin, ymax, xmax) coordinates of a box
  
      scores: relay.Expr
          3-D tensor with shape (batch_size, num_boxes, num_classes_with_background)
  
      max_detections_per_class : int
          The maxinum number of output selected boxes per class
  
      max_detections : int
          The maxinum number of output selected boxes
  
      num_classes : int
          The number of classes without background
  
      iou_threshold : float
          IoU test threshold
  
      score_threshold : float
          Score threshold to filter out low score boxes early
  
      Returns
      -------
      out : relay.Tuple
          The output is a relay.Tuple of four tensors. The first is `detection_boxes` of size
          `(batch_size, max_detections , 4)`, the second is `detection_classes` of size
          `(batch_size, max_detections)`, the third is `detection_scores` of size
          `(batch_size, max_detections)`, and the fourth is `num_detections` of size `(batch_size,)`
          representing the total number of selected boxes per batch.
      """
      return expr.TupleWrapper(
          _make.regular_non_max_suppression(
              boxes,
              scores,
              max_detections_per_class,
              max_detections,
              num_classes,
              iou_threshold,
              score_threshold,
          ),
          4,
      )
  
imports: from tvm.relay import expr
  from .None import _make
label_desc: Use image node_id 4 for calling a global function with example usage: regular_non_max_suppression(boxes, scores, max_detections_per_class, max_detections, num_classes, iou_threshold, score_threshold) and returns: expr