code: def get_eidos_bayesian_scorer(prior_counts=None):
      
      table = load_eidos_curation_table()
      subtype_counts = {'eidos': {r: [c, i] for r, c, i in
                                zip(table['RULE'], table['Num correct'],
                                    table['Num incorrect'])}}
      prior_counts = prior_counts if prior_counts else copy.deepcopy(
          default_priors)
  
      scorer = BayesianScorer(prior_counts=prior_counts,
                              subtype_counts=subtype_counts)
      return scorer
docs: Return a BayesianScorer based on Eidos curation counts.