addressing L2 CI errors

Author: apbose

tests/py/dynamo/distributed/test_nccl_ops.py

@@ -26,6 +26,7 @@
 )
 from parameterized import parameterized
 from torch.testing._internal.common_utils import run_tests
+from torch_tensorrt._features import ENABLED_FEATURES
 
 
 def is_distributed_nccl_available():
@@ -90,11 +91,15 @@ def forward(self, x):
 
 class TestNcclOpsConverter(DispatchTestCase):
     # 1. Skip if NCCL backend is not available (e.g., Windows, Jetson) - hard requirement
-    # 2. Don't skip if TRTLLM is unavailable (e.g., CUDA 13) - falls back to PyTorch
+    # 2. Skip if TRTLLM is unavailable (e.g., CUDA 13) - no converters registered
     @unittest.skipIf(
         not is_distributed_nccl_available(),
         "Skipped: NCCL backend is not available (Windows/Jetson Orin not supported).",
     )
+    @unittest.skipIf(
+        not ENABLED_FEATURES.trtllm_for_nccl,
+        "Skipped: TensorRT-LLM plugin for NCCL is not available (e.g., CUDA 13).",
+    )
     @classmethod
     def setUpClass(cls):
         cls.world_size = int(os.environ.get("OMPI_COMM_WORLD_SIZE", 1))