Fix MatchResult.fail() call signature in redundant_scatter_nd.py (#2431)

The `fail` helper function in
`onnxscript/rewriter/redundant_scatter_nd.py` was incorrectly passing
multiple arguments to `MatchResult.fail()`, causing a TypeError when
pattern matching failed.

## Problem

The error occurred when the rewriter tried to report match failures with
multiple failure sources:

```python
return fail("The shape of 'data' and 'updates' are different.", data, updates)
```

This resulted in:
```
TypeError: MatchResult.fail() takes from 1 to 3 positional arguments but 4 were given
```

The issue was that `MatchResult.fail()` only accepts 2 parameters after
`self`:
- `reason: str` - the failure reason
- `failure_source: Union[ir.Node, ir.Value, list[...]] | None` - a
single item or list of failure sources

But the helper function was passing all arguments directly:
`MatchResult().fail(*args)`.

## Solution

Modified the `fail` helper function to properly handle multiple failure
sources by collecting them into a list when calling
`MatchResult.fail()`:

```python
def fail(reason, *failure_sources):
    if failure_sources:
        return onnxscript.rewriter.MatchResult().fail(reason, list(failure_sources))
    else:
        return onnxscript.rewriter.MatchResult().fail(reason)
```

This change:
- ✅ Fixes the TypeError for calls with multiple failure sources
- ✅ Maintains backward compatibility for existing single-argument calls
- ✅ Follows the same pattern used correctly in other rewriter modules
like `matmul_add_to_gemm.py`

## Testing

Verified that all existing call patterns in the file work correctly:
- `fail("message")` - reason only
- `fail("message", node)` - reason + single source  
- `fail("message", node1, node2)` - reason + multiple sources

Fixes #2430.

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Author: Copilot

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