code: def test_fit(self, params, n_iter=5):
      h = hmm.GMMHMM(
          self.n_components,
          covariance_type=self.covariance_type,
          covars_prior=1.0,
      )
      h.startprob_ = self.startprob
      h.transmat_ = normalized(
          self.transmat + np.diag(self.prng.rand(self.n_components)), 1
      )
      h.gmms_ = self.gmms
  
      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.
      h.n_iter = 0
      h.fit(X, lengths=lengths)
      h.transmat_ = normalized(
          self.prng.rand(self.n_components, self.n_components), axis=1
      )
      h.startprob_ = normalized(self.prng.rand(self.n_components))
  
      assert_log_likelihood_increasing(h, X, lengths, n_iter)
  
imports: import numpy
  from numpy.testing import assert_allclose
  import pytest
  from sklearn.mixture import GaussianMixture
  from sklearn.utils import check_random_state
  from hmmlearn import hmm
  from .None import assert_log_likelihood_increasing, make_covar_matrix, normalized
label_desc: Use image node_id 4 for calling the GMMHMMTestMixin obj's underlying member method code with example usage: obj.test_fit(params, n_iter) without return types