code: def test_weighted_pdf_non_fft(self):
      kde = nparam.KDEUnivariate(self.noise)
      kde.fit(weights=self.weights, fft=False, bw="scott")
  
      grid = kde.support
      testx = [grid[10 * i] for i in range(6)]
  
      # Test against values from R 'ks' package
      kde_expected = [
          9.1998858033950757e-05,
          0.018761981151370496,
          0.14425925509365087,
          0.30307631742267443,
          0.2405445849994125,
          0.06433170684797665,
      ]
  
      kde_vals0 = kde.density[10 * np.arange(6)]
      kde_vals = kde.evaluate(testx)
  
      npt.assert_allclose(kde_vals, kde_expected, atol=1e-6)
      npt.assert_allclose(kde_vals0, kde_expected, atol=1e-6)
  
imports: import numpy
  import numpy.testing
  import pytest
  from numpy.testing import assert_allclose, assert_equal
  import statsmodels.api
label_desc: Use image node_id 2 for calling the TestKDEUnivariate obj's underlying member method code with example usage: obj.test_weighted_pdf_non_fft() without return types