diff --git a/onnxscript/optimizer/_remove_unused_test.py b/onnxscript/optimizer/_remove_unused_test.py
index b87a176f6..425a00a44 100644
--- a/onnxscript/optimizer/_remove_unused_test.py
+++ b/onnxscript/optimizer/_remove_unused_test.py
@@ -11,6 +11,8 @@
 
 @parameterized.parameterized_class(("using_ir",), [(False,), (True,)])
 class RemoveUnusedTest(unittest.TestCase):
+    using_ir: bool
+
     def remove_unused_nodes(self, model: onnx.ModelProto):
         if self.using_ir:
             model_ir = ir.serde.deserialize_model(model)
@@ -81,11 +83,7 @@ def test_remove_unused_optional_outputs_maxpool(self):
         model = self.remove_unused_nodes(model)
         self.assertEqual(len(model.graph.node), 1)
         self.assertEqual(model.graph.node[0].op_type, "MaxPool")
-        if self.using_ir:
-            expected_outputs = ["z", ""]
-        else:
-            expected_outputs = ["z"]
-        self.assertEqual(model.graph.node[0].output, expected_outputs)
+        self.assertEqual(model.graph.node[0].output, ["z"])
 
     def test_remove_unused_optional_outputs_dropout_in_function(self):
         model = onnx.parser.parse_model(
@@ -110,11 +108,7 @@ def test_remove_unused_optional_outputs_dropout_in_function(self):
         self.assertEqual(len(model.functions), 1)
         self.assertEqual(len(model.functions[0].node), 1)
         self.assertEqual(model.functions[0].node[0].op_type, "MaxPool")
-        if self.using_ir:
-            expected_outputs = ["z", ""]
-        else:
-            expected_outputs = ["z"]
-        self.assertEqual(model.functions[0].node[0].output, expected_outputs)
+        self.assertEqual(model.functions[0].node[0].output, ["z"])
 
     def test_remove_used_optional_outputs_maxpool(self):
         model = onnx.parser.parse_model(
@@ -150,11 +144,7 @@ def test_remove_multiple_unused_optional_outputs_layernorm(self):
         model = self.remove_unused_nodes(model)
         self.assertEqual(len(model.graph.node), 3)
         self.assertEqual(model.graph.node[2].op_type, "LayerNormalization")
-        if self.using_ir:
-            expected_outputs = ["z", "", ""]
-        else:
-            expected_outputs = ["z"]
-        self.assertEqual(list(model.graph.node[2].output), expected_outputs)
+        self.assertEqual(list(model.graph.node[2].output), ["z"])
 
     def test_remove_trailing_unused_optional_outputs_layernorm(self):
         model = onnx.parser.parse_model(
@@ -173,11 +163,7 @@ def test_remove_trailing_unused_optional_outputs_layernorm(self):
         model = self.remove_unused_nodes(model)
         self.assertEqual(len(model.graph.node), 3)
         self.assertEqual(model.graph.node[2].op_type, "LayerNormalization")
-        if self.using_ir:
-            expected_outputs = ["z", "mean", ""]
-        else:
-            expected_outputs = ["z", "mean"]
-        self.assertEqual(list(model.graph.node[2].output), expected_outputs)
+        self.assertEqual(list(model.graph.node[2].output), ["z", "mean"])
 
     def test_avoid_remove_non_trailing_unused_optional_outputs_layernorm(self):
         model = onnx.parser.parse_model(
@@ -212,11 +198,7 @@ def test_remove_trailing_unused_optional_outputs_batchnorm(self):
         self.assertEqual(len(model.graph.node), 1)
         self.assertEqual(model.graph.node[0].op_type, "BatchNormalization")
         # Check that both the mean/var outputs are removed, and training_mode attribute is removed.
-        if self.using_ir:
-            expected_outputs = ["z", "", ""]
-        else:
-            expected_outputs = ["z"]
-        self.assertEqual(list(model.graph.node[0].output), expected_outputs)
+        self.assertEqual(list(model.graph.node[0].output), ["z"])
         self.assertEqual(len(model.graph.node[0].attribute), 0)
 
     def test_avoid_remove_used_optional_outputs_batchnorm(self):