code: def test_score_samples_and_decode(self, implementation):
      h = hmm.GaussianHMM(
          self.n_components,
          self.covariance_type,
          init_params="st",
          implementation=implementation,
      )
      h.means_ = self.means
      h.covars_ = self.covars
  
      # Make sure the means are far apart so posteriors.argmax()
      # picks the actual component used to generate the observations.
      h.means_ = 20 * h.means_
  
      gaussidx = np.repeat(np.arange(self.n_components), 5)
      n_samples = len(gaussidx)
      X = (
          self.prng.randn(n_samples, self.n_features)
          + h.means_[gaussidx]
      )
      h._init(X, [n_samples])
      ll, posteriors = h.score_samples(X)
      assert posteriors.shape == (n_samples, self.n_components)
      assert_allclose(posteriors.sum(axis=1), np.ones(n_samples))
  
      viterbi_ll, stateseq = h.decode(X)
      assert_allclose(stateseq, gaussidx)
  
imports: import numpy
  from numpy.testing import assert_allclose
  import pytest
  from sklearn.utils import check_random_state
  from ..None import hmm
  from .None import assert_log_likelihood_increasing, make_covar_matrix, normalized
label_desc: Use image node_id 3 for calling the GaussianHMMTestMixin obj's underlying member method code with example usage: obj.test_score_samples_and_decode(implementation) without return types