class_docstr: Reduce corpus to the X most important terms.  Great for plotting a big corpus.
  Will return between X/2 and X terms.
  
  Returns the terms with the X highest absolute scores, background corpus scores, and term frequencies.
docstr: Parameters
  ----------
  term_doc_matrix: TermDocMatrix or descendant
  scores: array-like
          Same length as number of terms in TermDocMatrix.
  num_term_to_keep: int, default=4000.
          Should be> 0. Number of terms to keep. Will keep between num_terms_to_keep/2 and num_terms_to_keep.
  
  Returns
  -------
  TermDocMatrix stripped of non-important terms., array of scores
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code: def reduce_terms(term_doc_matrix, scores, num_term_to_keep=None):
      """
      Parameters
      ----------
      term_doc_matrix: TermDocMatrix or descendant
      scores: array-like
              Same length as number of terms in TermDocMatrix.
      num_term_to_keep: int, default=4000.
              Should be> 0. Number of terms to keep. Will keep between num_terms_to_keep/2 and num_terms_to_keep.
  
      Returns
      -------
      TermDocMatrix stripped of non-important terms., array of scores
      """
      terms_to_show = AutoTermSelector.get_selected_terms(
          term_doc_matrix, scores, num_term_to_keep
      )
      return term_doc_matrix.remove_terms(
          set(term_doc_matrix.get_terms()) - set(terms_to_show)
      )
  
label_desc: Use image node_id 1 for calling the AutoTermSelector obj's underlying member method code with example usage: obj.reduce_terms(term_doc_matrix, scores, num_term_to_keep) and returns: term_doc_matrix