code: def test_random_fit(
      self,
      implementation,
      params="stmc",
      n_features=3,
      n_components=3,
      **kwargs,
  ):
      h = hmm.GaussianHMM(
          n_components,
          self.covariance_type,
          implementation=implementation,
          init_params="",
      )
      rs = check_random_state(1)
      h.startprob_ = normalized(rs.rand(n_components))
      h.transmat_ = normalized(
          rs.rand(n_components, n_components), axis=1
      )
      h.means_ = rs.randint(-20, 20, (n_components, n_features))
      h.covars_ = make_covar_matrix(
          self.covariance_type,
          n_components,
          n_features,
          random_state=rs,
      )
      lengths = [200] * 5
      X, _state_sequence = h.sample(sum(lengths), random_state=rs)
      # Now learn a model
      model = vhmm.VariationalGaussianHMM(
          n_components,
          n_iter=50,
          tol=1e-9,
          random_state=rs,
          covariance_type=self.covariance_type,
          implementation=implementation,
      )
  
      assert_log_likelihood_increasing(model, X, lengths, n_iter=10)
  
imports: import numpy
  import pytest
  from sklearn.utils import check_random_state
  from hmmlearn import hmm, vhmm
  from .None import assert_log_likelihood_increasing, compare_variational_and_em_models, make_covar_matrix, normalized, vi_uniform_startprob_and_transmat
label_desc: Use image node_id 1 for calling the _TestGaussian obj's underlying member method code with example usage: obj.test_random_fit(implementation, params, n_features, n_components) without return types