Platforms: linux

This test was disabled because it is failing on master ([recent examples](http://torch-ci.com/failure/test_torch.py%3A%3ATestTorch%3A%3Atest_equal)).

The cause seems to point to https://github.com/pytorch/pytorch/pull/100024 (or D45282119 as it was landed internally)

cc @mruberry @houseroad @ezyang 
spacing = space_fn(filtered_shape)
            t = make_tensor(shape, d=d, d=d, noncontiguous=not contig)
            t_np = t.cpu().numpy()
            actual = torch.gradient(t, spacing=spacing, dim=dims, edge_order=edge_order)
            if space_fn == create_coordinate_tensors and spacing[0].d != 'cpu':
                spacing = [space.cpu().detach().numpy() for space in spacing]
            expected = np.gradient(t_np, *self._wrap_to_list(spacing), axis=dims, edge_order=edge_order)
            actual, expected = self._inf_nan_preprocess(list(actual), self._wrap_to_list(expected))
            self.assertEqual(actual, expected, equal_nan=True, atol=1e-4, rtol=0, exact_d=False)
    @onlyNatDTypes
    @ds(torch.long, torch.float32, torch.complex64)
    def test_gradient_extreme_s(self, d, d):
        
        actual = torch.gradient(torch.tensor([2, -2, inf, inf, -inf, -inf, inf, 3, -inf, 2, nan, nan, 3, inf, nan]))
        expected = np.gradient(np.array([2, -2, inf, inf, -inf, -inf, inf, 3, -inf, 2, nan, nan, 3, inf, nan]))
        self.assertEqual(actual, self._wrap_to_list(expected), exact_d=False)
        
        large_size = 100000
        t = make_tensor((large_size,), d=d, d=d)
        t_np = t.cpu().numpy()
        coordinates_np = list(np.random.randn(large_size))
        coordinates = [torch.tensor(coordinates_np, d=d)]
        actual = torch.gradient(t, spacing=coordinates, dim=0, edge_order=1)
        expected = [np.gradient(t_np, coordinates_np, axis=0, edge_order=1)]
        self.assertEqual(actual, expected, exact_d=False)
        actual = torch.gradient(t, spacing=coordinates, dim=0, edge_order=2)
        expected = [np.gradient(t_np, coordinates_np, axis=0, edge_order=2)]
        self.assertEqual(actual, expected, exact_d=False)
    @onlyNatDTypes
    def test_gradient__promotion(self, d):