I am done

rl/Lib/site-packages/onnx/reference/ops/op_scatternd.py

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+# Copyright (c) ONNX Project Contributors
+
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+from onnx.reference.op_run import OpRun
+
+
+def _scatter_nd_impl(data, indices, updates, reduction=None):  # type: ignore
+    output = np.copy(data)
+    for i in np.ndindex(indices.shape[:-1]):
+        if reduction == "add":
+            output[tuple(indices[i])] += updates[i]
+        elif reduction == "mul":
+            output[tuple(indices[i])] *= updates[i]
+        elif reduction == "max":
+            output[tuple(indices[i])] = np.maximum(output[indices[i]], updates[i])
+        elif reduction == "min":
+            output[tuple(indices[i])] = np.minimum(output[indices[i]], updates[i])
+        else:
+            output[tuple(indices[i])] = updates[i]
+    return output
+
+
+class ScatterND(OpRun):
+    def _run(self, data, indices, updates, reduction=None):  # type: ignore
+        y = _scatter_nd_impl(data, indices, updates, reduction=reduction)
+        return (y,)