code: def test_fit(self, implementation, params="stl", n_iter=5):
      h = self.new_hmm(implementation)
      h.params = params
  
      lengths = np.array([10] * 10)
      X, _state_sequence = h.sample(lengths.sum())
  
      # Mess up the parameters and see if we can re-learn them.
      np.random.seed(0)
      h.startprob_ = normalized(np.random.random(self.n_components))
      h.transmat_ = normalized(
          np.random.random((self.n_components, self.n_components)),
          axis=1,
      )
      h.lambdas_ = np.random.gamma(
          shape=2, size=(self.n_components, self.n_features)
      )
  
      assert_log_likelihood_increasing(h, X, lengths, n_iter)
  
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 4 for calling the TestPoissonHMM obj's underlying member method code with example usage: obj.test_fit(implementation, params, n_iter) without return types