code: def demo_bw():
      # demo Baum Welch by generating some sequences and then performing
      # unsupervised training on them
  
      print()
      print("Baum-Welch demo for market example")
      print()
  
      model, states, symbols = _market_hmm_example()
  
      # generate some random sequences
      training = []
      import random
  
      rng = random.Random()
      rng.seed(0)
      for i in range(10):
          item = model.random_sample(rng, 5)
          training.append([(i[0], None) for i in item])
  
      # train on those examples, starting with the model that generated them
      trainer = HiddenMarkovModelTrainer(states, symbols)
      hmm = trainer.train_unsupervised(
          training, model=model, max_iterations=1000
      )
  
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 12 for calling a global function with example usage: demo_bw() without return types