DCO Remediation Commit for R. Garcia-Dias <rafaelagd@gmail.com>

I, R. Garcia-Dias <rafaelagd@gmail.com>, hereby add my Signed-off-by to this commit: 8de64af
I, R. Garcia-Dias <rafaelagd@gmail.com>, hereby add my Signed-off-by to this commit: 7c2ddb6
I, R. Garcia-Dias <rafaelagd@gmail.com>, hereby add my Signed-off-by to this commit: cfe5524
I, R. Garcia-Dias <rafaelagd@gmail.com>, hereby add my Signed-off-by to this commit: 1dec216
I, R. Garcia-Dias <rafaelagd@gmail.com>, hereby add my Signed-off-by to this commit: ec8bf1f

Signed-off-by: R. Garcia-Dias <rafaelagd@gmail.com>

Author: garciadias

tests/transforms/test_spacing.py

@@ -22,16 +22,19 @@
 from monai.data.meta_tensor import MetaTensor
 from monai.data.utils import affine_to_spacing
 from monai.transforms import Spacing
+from monai.transforms.spatial.functional import _compiled_unsupported
 from monai.utils import fall_back_tuple
 from tests.lazy_transforms_utils import test_resampler_lazy
 from tests.test_utils import TEST_DEVICES, TEST_NDARRAYS_ALL, assert_allclose, dict_product, skip_if_quick
 
-# Define the static parts of each test case
-_template_5_expected_output = (
-    torch.tensor([[[[0.75, 0.75]], [[0.75, 0.75]], [[0.75, 0.75]]]])
-    if USE_COMPILED
-    else torch.tensor([[[[0.95527864, 0.95527864]], [[1.0, 1.0]], [[1.0, 1.0]]]])
-)
+_TEMPLATE_5_COMPILED = torch.tensor([[[[0.75, 0.75]], [[0.75, 0.75]], [[0.75, 0.75]]]])
+_TEMPLATE_5_NATIVE = torch.tensor([[[[0.95527864, 0.95527864]], [[1.0, 1.0]], [[1.0, 1.0]]]])
+
+
+def _template_5_expected_output(device: torch.device) -> torch.Tensor:
+    if USE_COMPILED and not _compiled_unsupported(device):
+        return _TEMPLATE_5_COMPILED
+    return _TEMPLATE_5_NATIVE
 
 all_template_parts = [
     [
@@ -241,6 +244,8 @@ def test_spacing(
 
         test_resampler_lazy(tr, res, init_param=init_param, call_param=call_param)
 
+        if callable(expected_output):
+            expected_output = expected_output(device)
         assert_allclose(res, expected_output, atol=1e-1, rtol=1e-1)
         sr = min(len(res.shape) - 1, 3)
         if isinstance(init_param["pixdim"], float):