code: def score_braycurtis(self, term1, term2, **kwargs):
  
          
  
          t1_kde = self.kde(term1, **kwargs)
          t2_kde = self.kde(term2, **kwargs)
  
          return 1-distance.braycurtis(t1_kde, t2_kde)
docs: Compute a weighting score based on the "City Block" distance between
          the kernel density estimates of two terms.
  
          Args:
              term1 (str)
              term2 (str)
  
          Returns: float