code: def aggregate_segment_sims(segment_sims, with_std, with_support):
      """Compute various statistics from the segment similarities generated via set pairwise comparisons
      of top-N word lists for a single topic.
  
      Parameters
      ----------
      segment_sims : iterable of float
          Similarity values to aggregate.
      with_std : bool
          Set to True to include standard deviation.
      with_support : bool
          Set to True to include number of elements in `segment_sims` as a statistic in the results returned.
  
      Returns
      -------
      (float[, float[, int]])
          Tuple with (mean[, std[, support]]).
  
      Examples
      ---------
      .. sourcecode:: pycon
  
          >>> from gensim.topic_coherence import direct_confirmation_measure
          >>>
          >>> segment_sims = [0.2, 0.5, 1., 0.05]
          >>> direct_confirmation_measure.aggregate_segment_sims(segment_sims, True, True)
          (0.4375, 0.36293077852394939, 4)
          >>> direct_confirmation_measure.aggregate_segment_sims(segment_sims, False, False)
          0.4375
  
      """
      mean = np.mean(segment_sims)
      stats = [mean]
      if with_std:
          stats.append(np.std(segment_sims))
      if with_support:
          stats.append(len(segment_sims))
  
      return stats[0] if len(stats) == 1 else tuple(stats)
  
imports: import logging
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: aggregate_segment_sims(segment_sims, with_std, with_support) and returns: unknown