skip lltm test if code not compiled

tests/test_lltm.py

@@ -15,6 +15,7 @@
 from parameterized import parameterized
 
 from monai.networks.layers import LLTM
+from tests.utils import SkipIfNoModule
 
 TEST_CASE_1 = [
     {"input_features": 32, "state_size": 2},
@@ -25,6 +26,7 @@
 
 class TestLLTM(unittest.TestCase):
     @parameterized.expand([TEST_CASE_1])
+    @SkipIfNoModule("monai._C")
     def test_value(self, input_param, expected_h, expected_c):
         torch.manual_seed(0)
         x = torch.randn(4, 32)
@@ -37,6 +39,7 @@ def test_value(self, input_param, expected_h, expected_c):
         torch.testing.assert_allclose(new_c, expected_c, rtol=0.0001, atol=1e-04)
 
     @parameterized.expand([TEST_CASE_1])
+    @SkipIfNoModule("monai._C")
     def test_value_cuda(self, input_param, expected_h, expected_c):
         device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu:0")
         torch.manual_seed(0)

tests/utils.py

@@ -32,6 +32,18 @@ def skip_if_quick(obj):
     return unittest.skipIf(is_quick, "Skipping slow tests")(obj)
 
 
+class SkipIfNoModule(object):
+    """Decorator to be used if test should be skipped
+    when optional module is not present."""
+
+    def __init__(self, module_name):
+        self.module_name = module_name
+        self.module_missing = not optional_import(self.module_name)[1]
+
+    def __call__(self, obj):
+        return unittest.skipIf(self.module_missing, f"optional module not present: {self.module_name}")(obj)
+
+
 def make_nifti_image(array, affine=None):
     """
     Create a temporary nifti image on the disk and return the image name.