code: def generate_training_sequences(self):
      sampler = hmmlearn.hmm.GMMHMM(
          n_components=4,
          n_mix=3,
          covariance_type="full",
          init_params="",
      )
  
      sampler.startprob_ = [0.25, 0.25, 0.25, 0.25]
      sampler.transmat_ = [
          [0.1, 0.3, 0.3, 0.3],
          [0.3, 0.1, 0.3, 0.3],
          [0.3, 0.3, 0.1, 0.3],
          [0.3, 0.3, 0.3, 0.1],
      ]
      sampler.weights_ = [
          [0.2, 0.2, 0.6],
          [0.6, 0.2, 0.2],
          [0.2, 0.6, 0.2],
          [0.1, 0.1, 0.8],
      ]
      sampler.means_ = np.asarray(
          [
              [[-10], [-12], [-9]],
              [[-5], [-4], [-3]],
              [[-1.5], [0], [1.5]],
              [[5], [7], [9]],
          ]
      )
  
      sampler.covars_ = np.asarray(
          [
              [[[0.125]], [[0.125]], [[0.125]]],
              [[[0.125]], [[0.125]], [[0.125]]],
              [[[0.125]], [[0.125]], [[0.125]]],
              [[[0.125]], [[0.125]], [[0.125]]],
          ]
      )
  
      n_sequences = 10
      length = 5_000
      sequences = []
      for i in range(n_sequences):
          sequences.append(sampler.sample(5000)[0])
      return np.concatenate(sequences), [length] * n_sequences
  
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 GMMBenchmark obj's underlying member method code with example usage: obj.generate_training_sequences() and returns: np,  unknown