code: def get_top_segmentations(table, n):
      
      stroke_count = list(range(len(table)))
      topf = TopFinder(n)
      for curr_segmentation in all_segmentations(stroke_count):
          curr_seg_score = score_segmentation(curr_segmentation, table)
          topf.push(curr_segmentation, curr_seg_score)
  
      for el, score in topf:
          yield [normalize_segmentation(el), score]
docs: Parameters
      ----------
      table : matrix of probabilities
          Each cell (i, j) of `table` gives the probability that i and j are in
          the same symbol.
      n : int
          Number of best segmentations which get returned