code: def print_ldamodel_distribution(distrib, row_labels, val_labels, top_n=10):
      
  
      df_values = top_n_from_distribution(distrib, top_n=top_n, row_labels=row_labels, val_labels=None)
      df_labels = top_n_from_distribution(distrib, top_n=top_n, row_labels=row_labels, val_labels=val_labels)
  
      for i, (ind, row) in enumerate(df_labels.iterrows()):
          print(ind)
          for j, label in enumerate(row):
              val = df_values.iloc[i, j]
              print('> #%d. %s (%f)' % (j + 1, label, val))
docs: Print `n_top` top values from a LDA model's distribution `distrib`. Can be used for topic-word distributions and
      document-topic distributions.