code: def generate_training_sequences(self):
      sampler = hmmlearn.hmm.MultinomialHMM(n_components=3)
      sampler.startprob_ = np.array([0.6, 0.3, 0.1])
      sampler.transmat_ = np.array(
          [[0.6, 0.2, 0.2], [0.3, 0.5, 0.2], [0.4, 0.3, 0.3]]
      )
  
      sampler.emissionprob_ = np.array(
          [
              [0.1, 0.5, 0.1, 0.3],
              [0.1, 0.2, 0.4, 0.3],
              [0, 0.5, 0.5, 0.0],
          ]
      )
  
      sequences, states = sampler.sample(50000)
      lengths = [len(sequences)]
      return sequences, lengths
  
imports: import argparse
  import collections
  import logging
  import time
  import hmmlearn.hmm
  import numpy
  import sklearn.base
label_desc: Use image node_id 2 for calling the MultinomialBenchmark obj's underlying member method code with example usage: obj.generate_training_sequences() and returns: sequences,  lengths