class_docstr: Wilcoxon Rank Sum (inspired by https://github.com/Zeta-and-Company/pydistinto/blob/main/scripts/measures/wilcoxon_ranksum.py)
  Pseudo counts of 0.0001 are added to each document.
docstr: In this case, args aren't used, since this information is taken
  directly from the corpus categories.
  
  Returns
  -------
  np.array, scores
docstr_tok: ["In","this","case",",","args","are","n't","used",",","since","this","information","is","taken","directly","from","the","corpus","categories",".","Returns","--","--","--","-","np.array",",","scores"]
code: def get_scores(self, *args):
      """
      In this case, args aren't used, since this information is taken
      directly from the corpus categories.
  
      Returns
      -------
      np.array, scores
      """
      return self.get_score_df().Score
  
file_path: scattertext/scattertext/termscoring/rank_sum.py
imports: import pandas
  from scipy.stats import norm, mannwhitneyu
  from scattertext.termscoring.CorpusBasedTermScorer import CorpusBasedTermScorer
label_desc: Use image node_id 2 for calling the RankSum obj's underlying member method code with example usage: obj.get_scores() and returns: self