code: def demo_pos_bw(
      test=10,
      supervised=20,
      unsupervised=10,
      verbose=True,
      max_iterations=5,
  ):
      # demonstrates the Baum-Welch algorithm in POS tagging
  
      print()
      print("Baum-Welch demo for POS tagging")
      print()
  
      print("Training HMM (supervised, %d sentences)..." % supervised)
  
      sentences, tag_set, symbols = load_pos(
          test + supervised + unsupervised
      )
  
      symbols = set()
      for sentence in sentences:
          for token in sentence:
              symbols.add(token[_TEXT])
  
      trainer = HiddenMarkovModelTrainer(tag_set, list(symbols))
      hmm = trainer.train_supervised(
          sentences[test : test + supervised],
          estimator=lambda fd, bins: LidstoneProbDist(fd, 0.1, bins),
      )
  
      hmm.test(sentences[:test], verbose=verbose)
  
      print("Training (unsupervised, %d sentences)..." % unsupervised)
      # it's rather slow - so only use 10 samples by default
      unlabeled = _untag(sentences[test + supervised :])
      hmm = trainer.train_unsupervised(
          unlabeled, model=hmm, max_iterations=max_iterations
      )
      hmm.test(sentences[:test], verbose=verbose)
  
imports: import itertools
  import re
  from nltk.metrics import accuracy
  from nltk.probability import ConditionalFreqDist, ConditionalProbDist, DictionaryConditionalProbDist, DictionaryProbDist, FreqDist, LidstoneProbDist, MLEProbDist, MutableProbDist, RandomProbDist
  from nltk.tag.api import TaggerI
  from nltk.util import LazyMap, unique_list
label_desc: Use image node_id 11 for calling a global function with example usage: demo_pos_bw(test, supervised, unsupervised, verbose, max_iterations) without return types