code: def generate_training_sequences(self):
      sampler = hmmlearn.hmm.GaussianHMM(
          n_components=4,
          covariance_type="full",
          init_params="",
          verbose=self.verbose,
      )
  
      sampler.startprob_ = np.asarray([0, 0, 0, 1])
      sampler.transmat_ = np.asarray(
          [
              [0.2, 0.2, 0.3, 0.3],
              [0.3, 0.2, 0.2, 0.3],
              [0.2, 0.3, 0.3, 0.2],
              [0.3, 0.3, 0.2, 0.2],
          ]
      )
      sampler.means_ = np.asarray([-1.5, 0, 1.5, 3]).reshape(4, 1)
      sampler.covars_ = np.asarray([0.5, 0.5, 0.5, 0.5]).reshape(
          4,
          1,
          1,
      )
  
      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 GaussianBenchmark obj's underlying member method code with example usage: obj.generate_training_sequences() and returns: sequences,  lengths