code: def test_sample(self, implementation, n=1000):
      h = hmm.GaussianHMM(
          self.n_components,
          self.covariance_type,
          implementation=implementation,
      )
      h.startprob_ = self.startprob
      h.transmat_ = self.transmat
      # Make sure the means are far apart so posteriors.argmax()
      # picks the actual component used to generate the observations.
      h.means_ = 20 * self.means
      h.covars_ = np.maximum(self.covars, 0.1)
  
      X, state_sequence = h.sample(n, random_state=self.prng)
      assert X.shape == (n, self.n_features)
      assert len(state_sequence) == n
  
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 4 for calling the GaussianHMMTestMixin obj's underlying member method code with example usage: obj.test_sample(implementation, n) without return types