code: def s_one_pre(topics):
      r"""Performs segmentation on a list of topics.
  
      Notes
      -----
      Segmentation is defined as
      :math:`s_{pre} = {(W', W^{*}) | W' = w_{i}; W^{*} = {w_j}; w_{i}, w_{j} \in W; i > j}`.
  
      Parameters
      ----------
      topics : list of np.array
          list of topics obtained from an algorithm such as LDA.
  
      Returns
      -------
      list of list of (int, int)
          :math:`(W', W^{*})` for all unique topic ids.
  
      Examples
      --------
      .. sourcecode:: pycon
  
          >>> import numpy as np
          >>> from gensim.topic_coherence import segmentation
          >>>
          >>> topics = [np.array([1, 2, 3]), np.array([4, 5, 6])]
          >>> segmentation.s_one_pre(topics)
          [[(2, 1), (3, 1), (3, 2)], [(5, 4), (6, 4), (6, 5)]]
  
      """
      s_one_pre_res = []
  
      for top_words in topics:
          s_one_pre_t = []
          for w_prime_index, w_prime in enumerate(top_words[1:]):
              for w_star in top_words[: w_prime_index + 1]:
                  s_one_pre_t.append((w_prime, w_star))
          s_one_pre_res.append(s_one_pre_t)
  
      return s_one_pre_res
  
label_desc: Use image node_id 1 for calling a global function with example usage: s_one_pre(topics) and returns: s_one_pre_res