code: def test_fit(self, implementation, params="stmc", n_iter=5, **kwargs):
      h = hmm.GaussianHMM(
          self.n_components,
          self.covariance_type,
          implementation=implementation,
      )
      h.startprob_ = self.startprob
      h.transmat_ = normalized(
          self.transmat + np.diag(self.prng.rand(self.n_components)), 1
      )
      h.means_ = 20 * self.means
      h.covars_ = self.covars
  
      lengths = [10] * 10
      X, _state_sequence = h.sample(
          sum(lengths), random_state=self.prng
      )
  
      # Mess up the parameters and see if we can re-learn them.
      # TODO: change the params and uncomment the check
      h.fit(X, lengths=lengths)
  
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 5 for calling the GaussianHMMTestMixin obj's underlying member method code with example usage: obj.test_fit(implementation, params, n_iter) without return types