def testGenerateSobolPoints(self):
    bounds = [(0.0, 1.0) for _ in range(3)]
    linear_constraints = (torch.tensor([[1, (- 1), 0]], dtype=torch.double), torch.tensor([[0]], dtype=torch.double))

    def test_rounding_func(x: Tensor) -> Tensor:
        return x
    gen_sobol = _generate_sobol_points(n_sobol=100, bounds=bounds, device=torch.device('cpu'), linear_constraints=linear_constraints, rounding_func=test_rounding_func)
    self.assertEqual(len(gen_sobol), 100)
    self.assertIsInstance(gen_sobol, Tensor)
def testGenerateSobolPoints(self):
    bounds = [(0.0, 1.0) for _ in range(3)]
    linear_constraints = (torch.tensor([[1, (- 1), 0]], dtype=torch.double), torch.tensor([[0]], dtype=torch.double))

    def test_rounding_func(x: Tensor) -> Tensor:
        return x
    gen_sobol = _generate_sobol_points(n_sobol=100, bounds=bounds, device=torch.device('cpu'), linear_constraints=linear_constraints, rounding_func=test_rounding_func)
    self.assertEqual(len(gen_sobol), 100)
    self.assertIsInstance(gen_sobol, Tensor)