code: def s_one_set(topics):
      r"""Perform s_one_set segmentation on a list of topics.
      Segmentation is defined as
      :math:`s_{set} = {(W', W^{*}) | W' = {w_i}; w_{i} \in W; W^{*} = W}`
  
      Parameters
      ----------
      topics : list of `numpy.ndarray`
          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([9, 10, 7])]
          >>> segmentation.s_one_set(topics)
          [[(9, array([ 9, 10,  7])), (10, array([ 9, 10,  7])), (7, array([ 9, 10,  7]))]]
  
      """
      s_one_set_res = []
  
      for top_words in topics:
          s_one_set_t = []
          for w_prime in top_words:
              s_one_set_t.append((w_prime, top_words))
          s_one_set_res.append(s_one_set_t)
  
      return s_one_set_res
  
label_desc: Use image node_id 3 for calling a global function with example usage: s_one_set(topics) and returns: s_one_set_res