code: def proposal(
      cls_prob,
      bbox_pred,
      im_info,
      scales,
      ratios,
      feature_stride,
      threshold,
      rpn_pre_nms_top_n,
      rpn_post_nms_top_n,
      rpn_min_size,
      iou_loss,
  ):
      """Proposal operator.
  
      Parameters
      ----------
      cls_prob : relay.Expr
          4-D tensor with shape [batch, 2 * num_anchors, height, width].
  
      bbox_pred : relay.Expr
          4-D tensor with shape [batch, 4 * num_anchors, height, width].
  
      im_info : relay.Expr
          2-D tensor with shape [batch, 3]. The last dimension should be in format of
          [im_height, im_width, im_scale]
  
      scales : list/tuple of float
          Scales of anchor windows.
  
      ratios : list/tuple of float
          Ratios of anchor windows.
  
      feature_stride : int
          The size of the receptive field each unit in the convolution layer of the rpn, for example
          the product of all stride's prior to this layer.
  
      threshold : float
          Non-maximum suppression threshold.
  
      rpn_pre_nms_top_n : int
          Number of top scoring boxes to apply NMS. -1 to use all boxes.
  
      rpn_post_nms_top_n : int
          Number of top scoring boxes to keep after applying NMS to RPN proposals.
  
      rpn_min_size : int
          Minimum height or width in proposal.
  
      iou_loss : bool
          Usage of IoU loss.
  
      Returns
      -------
      output : relay.Expr
          2-D tensor with shape [batch * rpn_post_nms_top_n, 5]. The last dimension is in format of
          [batch_index, w_start, h_start, w_end, h_end].
      """
      return _make.proposal(
          cls_prob,
          bbox_pred,
          im_info,
          scales,
          ratios,
          feature_stride,
          threshold,
          rpn_pre_nms_top_n,
          rpn_post_nms_top_n,
          rpn_min_size,
          iou_loss,
      )
  
imports: from .None import _make
label_desc: Use image node_id 3 for calling a global function with example usage: proposal(cls_prob, bbox_pred, im_info, scales, ratios, feature_stride, threshold, rpn_pre_nms_top_n, rpn_post_nms_top_n, rpn_min_size, iou_loss) and returns: _make