code: def _market_hmm_example():
      """
      Return an example HMM (described at page 381, Huang et al)
      """
      states = ["bull", "bear", "static"]
      symbols = ["up", "down", "unchanged"]
      A = np.array(
          [[0.6, 0.2, 0.2], [0.5, 0.3, 0.2], [0.4, 0.1, 0.5]],
          np.float64,
      )
      B = np.array(
          [[0.7, 0.1, 0.2], [0.1, 0.6, 0.3], [0.3, 0.3, 0.4]],
          np.float64,
      )
      pi = np.array([0.5, 0.2, 0.3], np.float64)
  
      model = _create_hmm_tagger(states, symbols, A, B, pi)
      return model, states, symbols
  
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 6 for calling a global function with example usage: _market_hmm_example() and returns: model,  states,  symbols