code: def test_fit_mcgrory_titterington1d(self, implementation):
      random_state = check_random_state(234234)
      # Setup to assure convergence
  
      sequences, lengths = get_sequences(
          500, 1, model=get_mcgrory_titterington(), rs=random_state
      )
      model = vhmm.VariationalGaussianHMM(
          5,
          n_iter=1000,
          tol=1e-9,
          random_state=random_state,
          init_params="mc",
          covariance_type=self.covariance_type,
          implementation=implementation,
      )
      vi_uniform_startprob_and_transmat(model, lengths)
      model.fit(sequences, lengths)
      # Perform one check that we are converging to the right answer
      assert list(sorted(model.means_posterior_.ravel()))[
          3
      ] == pytest.approx(
          self.test_fit_mcgrory_titterington1d_mean
      ), model.means_posterior_
  
      em_hmm = hmm.GaussianHMM(
          n_components=model.n_components,
          implementation=implementation,
          covariance_type=self.covariance_type,
      )
      em_hmm.startprob_ = model.startprob_
      em_hmm.transmat_ = model.transmat_
      em_hmm.means_ = model.means_posterior_
      em_hmm.covars_ = model._covars_
  
      compare_variational_and_em_models(
          model, em_hmm, sequences, lengths
      )
  
imports: import numpy
  import pytest
  from sklearn.utils import check_random_state
  from hmmlearn import hmm, vhmm
  from .None import assert_log_likelihood_increasing, compare_variational_and_em_models, make_covar_matrix, normalized, vi_uniform_startprob_and_transmat
label_desc: Use image node_id 2 for calling the _TestGaussian obj's underlying member method code with example usage: obj.test_fit_mcgrory_titterington1d(implementation) without return types