diff --git a/test/test_tensorclass.py b/test/test_tensorclass.py
index 48711851c..01a5aec37 100644
--- a/test/test_tensorclass.py
+++ b/test/test_tensorclass.py
@@ -125,7 +125,6 @@ def test_tensorclass_stub_methods():
     ]
 
     if missing_methods:
-        raise Exception(f"Missing methods in tensorclass.pyi: {missing_methods}")
         raise Exception(
             f"Missing methods in tensorclass.pyi: {sorted(missing_methods)}"
         )
@@ -156,6 +155,34 @@ class X:
         )
 
 
+def test_sorted_methods():
+    from tensordict.tensorclass import (
+        _FALLBACK_METHOD_FROM_TD,
+        _FALLBACK_METHOD_FROM_TD_FORCE,
+        _FALLBACK_METHOD_FROM_TD_NOWRAP,
+        _METHOD_FROM_TD,
+    )
+
+    lists_to_check = [
+        _FALLBACK_METHOD_FROM_TD_NOWRAP,
+        _METHOD_FROM_TD,
+        _FALLBACK_METHOD_FROM_TD_FORCE,
+        _FALLBACK_METHOD_FROM_TD,
+    ]
+    # Check that each list is sorted and has unique elements
+    for lst in lists_to_check:
+        assert lst == sorted(lst), f"List {lst} is not sorted"
+        assert len(lst) == len(set(lst)), f"List {lst} has duplicate elements"
+    # Check that no two lists share any elements
+    for i, lst1 in enumerate(lists_to_check):
+        for j, lst2 in enumerate(lists_to_check):
+            if i != j:
+                shared_elements = set(lst1) & set(lst2)
+                assert (
+                    not shared_elements
+                ), f"Lists {lst1} and {lst2} share elements: {shared_elements}"
+
+
 def _make_data(shape):
     return MyData(
         X=torch.rand(*shape),