Allow forgiving tolerance on some array comparision tests

This may have comes with the numpy 2 change where the precision of scalars is now preserved consistently. See https://numpy.org/devdocs/numpy_2_0_migration_guide.html#changes-to-numpy-data-type-promotion

Observed testing results which failed only on the macOS runner:

Mismatched elements: 1 / 1 (100%)
Max absolute difference among violations: 2.74195588e-09
Max relative difference among violations: 3.36410326e-09
 ACTUAL: array([0.815063], dtype=float32)
 DESIRED: array(0.815063)

Author: jamesobutler

tests/metrics/test_surface_dice.py

@@ -82,7 +82,7 @@ def test_tolerance_euclidean_distance_with_spacing(self):
         expected_res0[1, 1] = np.nan
         for b, c in np.ndindex(batch_size, n_class):
             np.testing.assert_allclose(expected_res0[b, c], res0[b, c].cpu())
-        np.testing.assert_array_equal(agg0.cpu(), np.nanmean(np.nanmean(expected_res0, axis=1), axis=0))
+        np.testing.assert_allclose(agg0.cpu(), np.nanmean(np.nanmean(expected_res0, axis=1), axis=0))
         np.testing.assert_equal(not_nans.cpu(), torch.tensor(2))
 
     def test_tolerance_euclidean_distance(self):
@@ -126,7 +126,7 @@ def test_tolerance_euclidean_distance(self):
         expected_res0[1, 1] = np.nan
         for b, c in np.ndindex(batch_size, n_class):
             np.testing.assert_allclose(expected_res0[b, c], res0[b, c].cpu())
-        np.testing.assert_array_equal(agg0.cpu(), np.nanmean(np.nanmean(expected_res0, axis=1), axis=0))
+        np.testing.assert_allclose(agg0.cpu(), np.nanmean(np.nanmean(expected_res0, axis=1), axis=0))
         np.testing.assert_equal(not_nans.cpu(), torch.tensor(2))
 
     def test_tolerance_euclidean_distance_3d(self):
@@ -173,7 +173,7 @@ def test_tolerance_euclidean_distance_3d(self):
         expected_res0[1, 1] = np.nan
         for b, c in np.ndindex(batch_size, n_class):
             np.testing.assert_allclose(expected_res0[b, c], res0[b, c].cpu())
-        np.testing.assert_array_equal(agg0.cpu(), np.nanmean(np.nanmean(expected_res0, axis=1), axis=0))
+        np.testing.assert_allclose(agg0.cpu(), np.nanmean(np.nanmean(expected_res0, axis=1), axis=0))
         np.testing.assert_equal(not_nans.cpu(), torch.tensor(2))
 
     def test_tolerance_all_distances(self):