code: def get_points_nd(self, clicks_lists):
      total_clicks = []
      logging.info(
          "check_list",
          clicks_lists,
      )
      num_pos_clicks = [
          sum(x.is_positive for x in clicks_list)
          for clicks_list in clicks_lists
      ]
      num_neg_clicks = [
          len(clicks_list) - num_pos
          for clicks_list, num_pos in zip(clicks_lists, num_pos_clicks)
      ]
  
      num_max_points = max(num_pos_clicks + num_neg_clicks)
  
      if self.net_clicks_limit is not None:
          num_max_points = min(self.net_clicks_limit, num_max_points)
      num_max_points = max(1, num_max_points)
  
      for clicks_list in clicks_lists:
          clicks_list = clicks_list[: self.net_clicks_limit]
          pos_clicks = [
              click.coords_and_indx
              for click in clicks_list
              if click.is_positive
          ]
          pos_clicks = pos_clicks + (
              num_max_points - len(pos_clicks)
          ) * [(-1, -1, -1, -1)]
  
          neg_clicks = [
              click.coords_and_indx
              for click in clicks_list
              if not click.is_positive
          ]
          neg_clicks = neg_clicks + (
              num_max_points - len(neg_clicks)
          ) * [(-1, -1, -1, -1)]
          total_clicks.append(pos_clicks + neg_clicks)
  
      return paddle.to_tensor(total_clicks)
  
imports: import os
  import sys
  import logging
  import numpy
  from copy import deepcopy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.inference import create_predictor, Config
  from inference.ops import DistMaps3D, ScaleLayer, BatchImageNormalize3D, SigmoidForPred
label_desc: Use image node_id 13 for calling the BasePredictor obj's underlying member method code with example usage: obj.get_points_nd(clicks_lists) and returns: paddle