code: def nonprojective_conll_parse_demo():
      from nltk.parse.dependencygraph import conll_data2
  
      graphs = [
          DependencyGraph(entry)
          for entry in conll_data2.split("\n\n")
          if entry
      ]
      npp = ProbabilisticNonprojectiveParser()
      npp.train(graphs, NaiveBayesDependencyScorer())
      for parse_graph in npp.parse(
          ["Cathy", "zag", "hen", "zwaaien", "."],
          ["N", "V", "Pron", "Adj", "N", "Punc"],
      ):
          print(parse_graph)
  
imports: import logging
  import math
  from nltk.parse.dependencygraph import DependencyGraph
label_desc: Use image node_id 3 for calling a global function with example usage: nonprojective_conll_parse_demo() without return types