code: def test_fit(self, implementation):
      n_iter = 5
      n_samples = 1000
      lengths = None
      h = self.new_hmm(implementation)
      X, _state_sequence = h.sample(n_samples)
  
      # Mess up the parameters and see if we can re-learn them.
      covs0, means0, priors0, trans0, weights0 = prep_params(
          self.n_components,
          self.n_mix,
          self.n_features,
          self.covariance_type,
          self.low,
          self.high,
          np.random.RandomState(15),
      )
      h.covars_ = covs0 * 100
      h.means_ = means0
      h.startprob_ = priors0
      h.transmat_ = trans0
      h.weights_ = weights0
      assert_log_likelihood_increasing(h, X, lengths, n_iter)
  
imports: import numpy
  from numpy.testing import assert_allclose, assert_array_less
  import pytest
  from sklearn.utils import check_random_state
  from ..hmm import GMMHMM
  from .test_gmm_hmm import create_random_gmm
  from .None import assert_log_likelihood_increasing, normalized
label_desc: Use image node_id 7 for calling the GMMHMMTestMixin obj's underlying member method code with example usage: obj.test_fit(implementation) without return types