code: def test_discrepancy():
      space_0 = [
          [0.1, 0.5],
          [0.2, 0.4],
          [0.3, 0.3],
          [0.4, 0.2],
          [0.5, 0.1],
      ]
      space_1 = [[1, 3], [2, 6], [3, 2], [4, 5], [5, 1], [6, 4]]
      space_2 = [[1, 5], [2, 4], [3, 3], [4, 2], [5, 1], [6, 6]]
  
      corners = np.array([[0.5, 0.5], [6.5, 6.5]])
  
      npt.assert_allclose(
          sequences.discrepancy(space_0), 0.1353, atol=1e-4
      )
  
      # From Fang et al. Design and modeling for computer experiments, 2006
      npt.assert_allclose(
          sequences.discrepancy(space_1, corners), 0.0081, atol=1e-4
      )
      npt.assert_allclose(
          sequences.discrepancy(space_2, corners), 0.0105, atol=1e-4
      )
  
imports: import numpy
  import numpy.testing
  from statsmodels.tools import sequences
label_desc: Use image node_id 1 for calling a global function with example usage: test_discrepancy() without return types