code: def test_fit_left_right(self, implementation):
      transmat = np.zeros((self.n_components, self.n_components))
  
      # Left-to-right: each state is connected to itself and its
      # direct successor.
      for i in range(self.n_components):
          if i == self.n_components - 1:
              transmat[i, i] = 1.0
          else:
              transmat[i, i] = transmat[i, i + 1] = 0.5
  
      # Always start in first state
      startprob = np.zeros(self.n_components)
      startprob[0] = 1.0
  
      lengths = [10, 8, 1]
      X = self.prng.rand(sum(lengths), self.n_features)
  
      h = hmm.GaussianHMM(
          self.n_components,
          covariance_type="diag",
          params="mct",
          init_params="cm",
          implementation=implementation,
      )
      h.startprob_ = startprob.copy()
      h.transmat_ = transmat.copy()
      h.fit(X)
  
      assert (h.startprob_[startprob == 0.0] == 0.0).all()
      assert (h.transmat_[transmat == 0.0] == 0.0).all()
  
      posteriors = h.predict_proba(X)
      assert not np.isnan(posteriors).any()
      assert_allclose(posteriors.sum(axis=1), 1.0)
  
      score, state_sequence = h.decode(X, algorithm="viterbi")
      assert np.isfinite(score)
  
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 2 for calling the TestGaussianHMMWithDiagonalCovars obj's underlying member method code with example usage: obj.test_fit_left_right(implementation) without return types