code: def test_score_samples_and_decode(self):
      h = hmm.GMMHMM(
          self.n_components, covariance_type=self.covariance_type
      )
      h.startprob_ = self.startprob
      h.transmat_ = self.transmat
      h.gmms_ = self.gmms
  
      # Make sure the means are far apart so posteriors.argmax()
      # picks the actual component used to generate the observations.
      for g in h.gmms_:
          g.means_ *= 20
  
      refstateseq = np.repeat(np.arange(self.n_components), 5)
      n_samples = len(refstateseq)
      X = [h.gmms_[x].sample(1).flatten() for x in refstateseq]
  
      _ll, posteriors = h.score_samples(X)
  
      assert posteriors.shape == (n_samples, self.n_components)
      assert_allclose(posteriors.sum(axis=1), np.ones(n_samples))
  
      _log_prob, stateseq = h.decode(X)
      assert_allclose(stateseq, refstateseq)
  
imports: import numpy
  from numpy.testing import assert_allclose
  import pytest
  from sklearn.mixture import GaussianMixture
  from sklearn.utils import check_random_state
  from hmmlearn import hmm
  from .None import assert_log_likelihood_increasing, make_covar_matrix, normalized
label_desc: Use image node_id 2 for calling the GMMHMMTestMixin obj's underlying member method code with example usage: obj.test_score_samples_and_decode() without return types