I am done

rl/Lib/site-packages/onnxruntime/tools/qdq_helpers/optimize_qdq_model.py

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+#!/usr/bin/env python3
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import argparse
+import os
+import pathlib
+
+import onnx
+
+
+def optimize_qdq_model():
+    parser = argparse.ArgumentParser(
+        os.path.basename(__file__),
+        description="Update a QDQ format ONNX model to ensure optimal performance when executed using ONNX Runtime.",
+    )
+
+    parser.add_argument("input_model", type=pathlib.Path, help="Provide path to ONNX model to update.")
+    parser.add_argument("output_model", type=pathlib.Path, help="Provide path to write updated ONNX model to.")
+
+    args = parser.parse_args()
+
+    model = onnx.load(str(args.input_model.resolve(strict=True)))
+
+    # run QDQ model optimizations here
+
+    # Originally, the fixing up of DQ nodes with multiple consumers was implemented as one such optimization.
+    # That was moved to an ORT graph transformer.
+    print("As of ORT 1.15, the fixing up of DQ nodes with multiple consumers is done by an ORT graph transformer.")
+
+    # There are no optimizations being run currently but we expect that there may be in the future.
+
+    onnx.save(model, str(args.output_model.resolve()))
+
+
+if __name__ == "__main__":
+    optimize_qdq_model()