code: def test_kmeans(self, implementation):
      # Generate two isolated cluster.
      # The second cluster has no. of points less than n_mix.
      np.random.seed(0)
      data1 = np.random.uniform(low=0, high=1, size=(100, 2))
      data2 = np.random.uniform(low=5, high=6, size=(5, 2))
      data = np.r_[data1, data2]
      model = GMMHMM(
          n_components=2,
          n_mix=10,
          n_iter=5,
          implementation=implementation,
      )
      model.fit(data)  # _init() should not fail here
      # test whether the means are bounded by the data lower- and upperbounds
      assert_array_less(0, model.means_)
      assert_array_less(model.means_, 6)
  
imports: import numpy
  from numpy.testing import assert_allclose, assert_array_less
  import pytest
  from sklearn.utils import check_random_state
  from ..hmm import GMMHMM
  from .test_gmm_hmm import create_random_gmm
  from .None import assert_log_likelihood_increasing, normalized
label_desc: Use image node_id 1 for calling the TestGMMHMM_KmeansInit obj's underlying member method code with example usage: obj.test_kmeans(implementation) without return types