code: def test_score_samples(self, implementation, n_samples=1000):
      h = self.new_hmm(implementation)
      X, state_sequence = h.sample(n_samples)
      assert X.ndim == 2
      assert len(X) == len(state_sequence) == 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))
  
file_path: hmmlearn/lib/hmmlearn/tests/test_poisson_hmm.py
imports: import numpy
  from numpy.testing import assert_allclose
  import pytest
  from sklearn.utils import check_random_state
  from hmmlearn import hmm
  from .None import assert_log_likelihood_increasing, normalized
label_desc: Use image node_id 3 for calling the TestPoissonHMM obj's underlying member method code with example usage: obj.test_score_samples(implementation, n_samples) without return types