code: def setup_method(self, method):
      n_components = 8
      n_samples = 10
  
      self.log_frameprob = np.log(
          np.random.random((n_samples, n_components))
      )
  
      h = StubHMM(n_components)
      h.log_frameprob = self.log_frameprob
  
      # If startprob and transmat are uniform across all states (the
      # default), the transitions are uninformative - the model
      # reduces to a GMM with uniform mixing weights (in terms of
      # posteriors, not likelihoods).
      h.startprob_ = np.ones(n_components) / n_components
      h.transmat_ = np.ones((n_components, n_components)) / n_components
  
      self.hmm = h
  
imports: import numpy
  from numpy.testing import assert_allclose
  import pytest
  from scipy import special
  from hmmlearn.base import BaseHMM, ConvergenceMonitor
  from hmmlearn import _hmmc
label_desc: Use image node_id 1 for calling the TestBaseConsistentWithGMM obj's underlying member method code with example usage: obj.setup_method(method) without return types