code: def test_gmm_e2e():
      """
      Test the GMM estimation. Since this is essentially a wrapper for the
      scikit-learn GaussianMixture class, the testing of the actual inner
      workings of the GMM estimation is left to scikit-learn and its
      dependencies.
  
      We instead simply assert that the estimation was successful based on the
      fact that the GMM object will have associated mixture weights, means, and
      variances after estimation is successful/complete.
      """
  
      gmm = learn_gmm(np.random.random((100, 64)), n_modes=5)
  
      assert gmm.means_ is not None
      assert gmm.covariances_ is not None
      assert gmm.weights_ is not None
  
file_path: scikit-image/skimage/feature/tests/test_fisher_vector.py
imports: import pytest
  import numpy
  from skimage.feature._fisher_vector import learn_gmm, fisher_vector, FisherVectorException, DescriptorException
label_desc: Use image node_id 9 for calling a global function with example usage: test_gmm_e2e() without return types