code: def generate_training_sequences(self):
      sampler = hmmlearn.hmm.GaussianHMM(
          n_components=4, covariance_type="full", init_params=""
      )
  
      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], [0, 0], [1.5, 0], [3, 0]])
      sampler.covars_ = np.asarray(
          [
              [[0.5, 0], [0, 0.5]],
              [[0.5, 0], [0, 0.5]],
              [[0.5, 0], [0, 0.5]],
              [[0.5, 0], [0, 0.5]],
          ]
      )
  
      observed, hidden = sampler.sample(50000)
      lengths = [len(observed)]
      return observed, lengths
  
imports: import argparse
  import collections
  import logging
  import time
  import hmmlearn.hmm
  import numpy
  import sklearn.base
label_desc: Use image node_id 1 for calling the MultivariateGaussianBenchmark obj's underlying member method code with example usage: obj.generate_training_sequences() and returns: observed,  lengths