diff --git a/onnxscript/rewriter/redundant_scatter_nd.py b/onnxscript/rewriter/redundant_scatter_nd.py
index 1ba6477f52..4d96360cd7 100644
--- a/onnxscript/rewriter/redundant_scatter_nd.py
+++ b/onnxscript/rewriter/redundant_scatter_nd.py
@@ -24,10 +24,6 @@
 from onnxscript.rewriter import pattern as orp
 
 
-def fail(*args):
-    return onnxscript.rewriter.MatchResult().fail(*args)
-
-
 class ScatterAllDynamic(orp.RewriteRuleClassBase):
     def pattern(self, op, data, axis, transposed_data, updates):
         # Construct update-indices spanning an entire axis:
@@ -41,24 +37,26 @@ def pattern(self, op, data, axis, transposed_data, updates):
     def check(self, context, data, axis, transposed_data, **_):
         # Check that updated-indices represent the full range of the first dimension of the transposed data.
         # That is: check that the data.shape[axis] matches transposed_data.shape[0].
+        result = onnxscript.rewriter.MatchResult()
         axis_value = ir_utils.get_singleton_value(axis)
         if not isinstance(axis_value, int):
-            return fail("Axis value must be a constant integer.", axis)
+            return result.fail("Axis value must be a constant integer.", axis)
         shape: ir.Shape | None = data.shape
         if shape is None:
-            return fail("Data shape is not statically known.", data)
+            return result.fail("Data shape is not statically known.", data)
         updated_dim_value = shape[axis_value]
         transposed_data_shape: ir.Shape | None = transposed_data.shape
         if transposed_data_shape is None:
-            return fail("Transposed data shape is not statically known.", transposed_data)
+            return result.fail(
+                "Transposed data shape is not statically known.", transposed_data
+            )
         actual_dim_value = transposed_data_shape[0]
         if updated_dim_value != actual_dim_value:
             # The first dimension of the transposed data does not match the updated dimension,
             # so we cannot apply this rule.
-            return fail(
+            return result.fail(
                 "The first dimension of the transposed data does not match the updated dimension.",
-                data,
-                transposed_data,
+                [data, transposed_data],
             )
         return True
 
@@ -81,20 +79,23 @@ def check(self, context, data, indices, updates, **_):
         """Check if the ScatterND is redundant due to static indices covering entire tensor."""
         # To validate data can be replaced directly by updates, we need to check the following:
         # 1. they have the same shape
+        result = onnxscript.rewriter.MatchResult()
         if data.shape is None:
-            return fail("The value 'data' shape is not statically known.", data)
+            return result.fail("The value 'data' shape is not statically known.", data)
         if updates.shape is None:
-            return fail("The value 'updates' shape is not statically known.", updates)
+            return result.fail("The value 'updates' shape is not statically known.", updates)
         if data.shape != updates.shape:
-            return fail("The shape of 'data' and 'updates' are different.", data, updates)
+            return result.fail(
+                "The shape of 'data' and 'updates' are different.", [data, updates]
+            )
 
         # 2. the indices is referring to the whole data, which is from 0 to data.shape[0]
         if indices.const_value is None:
-            return fail("The value 'indices' is not statically known.", indices)
+            return result.fail("The value 'indices' is not statically known.", indices)
         expected_indices = [[i] for i in range(data.shape[0])]
         actual_indices = indices.const_value.numpy().tolist()
         if actual_indices != expected_indices:
-            return fail("The 'indices' is not referring to the whole data.", indices)
+            return result.fail("The 'indices' is not referring to the whole data.", indices)
 
         return True