docstr: Test the callable's cov factor for a multi-dimensional array.
docstr_tok: ["Test","the","callable","'s","cov","factor","for","a","multi-dimensional","array","."]
code: def test_callable_covariance_dataset(self):
      """Test the callable's cov factor for a multi-dimensional array."""
      np.random.seed(8765678)
      n_basesample = 50
      multidim_data = [np.random.randn(n_basesample) for i in range(5)]
  
      def callable_fun(x):
          return 0.55
  
      kde = mlab.GaussianKDE(multidim_data, bw_method=callable_fun)
      assert kde.covariance_factor() == 0.55
  
imports: from numpy.testing import assert_allclose, assert_almost_equal, assert_array_equal, assert_array_almost_equal_nulp
  import numpy
  import pytest
  from matplotlib import mlab
label_desc: Use image node_id 9 for calling the TestGaussianKDECustom obj's underlying member method code with example usage: obj.test_callable_covariance_dataset() and returns: int