code: def form_lemma_tokeyword_map(annotated_nodes, node_to_lemma_tokeywordbag):
      
      # Reduce relevant lemma-to-original keyword bags.
      lemma_to_keywordbag = defaultdict(lambda: defaultdict(int))
      for node in annotated_nodes:
          for lemma, keywordbag in node_to_lemma_tokeywordbag[node].items():
              for keyword, multiplicity in keywordbag.items():
                  lemma_to_keywordbag[lemma][keyword] += multiplicity
  
      lemma_to_keyword = dict()
      for lemma, keywordbag in lemma_to_keywordbag.items():
          lemma_to_keyword[lemma] = max(keywordbag.items(), key=itemgetter(1))[0]
  
      return lemma_to_keyword
docs: Forms the aggregated dictionary that maps lemmas/stems to the most popular topic keyword.
  
      Inputs: - annotated_nodes: A numpy array of anonymized nodes that are annotated.
              - node_to_lemma_tokeywordbag: A map from nodes to maps from lemmas to bags of keywords.
  
      Output: - lemma_to_keyword: A dictionary that maps lemmas to keywords.