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diff --git a/config-model/src/test/derived/globalphase_token_functions/files/m.py b/config-model/src/test/derived/globalphase_token_functions/files/m.py
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+++ b/config-model/src/test/derived/globalphase_token_functions/files/m.py
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+# imports
+
+from onnx import TensorProto
+from onnx.helper import (
+    make_model, make_node, make_graph,
+    make_tensor_value_info, make_value_info)
+from onnx.checker import check_model
+
+# inputs
+
+# TensorProto.DOUBLE is the element type, [128] the shape
+A = make_tensor_value_info('input_ids',      TensorProto.DOUBLE, [128])
+B = make_tensor_value_info('attention_mask', TensorProto.DOUBLE, [128])
+C = make_tensor_value_info('token_type_ids', TensorProto.DOUBLE, [128])
+
+# outputs, the shape is defined
+Y = make_tensor_value_info('vector_Y', TensorProto.DOUBLE, [128])
+S = make_tensor_value_info('score', TensorProto.DOUBLE, [1])
+
+# Creates node defined by the operator type, inputs, outputs, and possibly options
+node1 = make_node('Mul', ['input_ids', 'attention_mask'], ['masked'])
+node2 = make_node('Add', ['masked', 'token_type_ids'], ['vector_Y'])
+node3 = make_node('ReduceSum', inputs=['vector_Y'], outputs=['score'], keepdims=1)
+
+# from nodes to graph
+# the graph is built from the list of nodes, the list of inputs,
+# the list of outputs and a name.
+
+graph = make_graph([node1, node2, node3],  # nodes
+                    'ranking_model',  # a name
+                    [A, B, C],  # inputs
+                    [S])  # outputs
+
+# onnx graph to model
+onnx_model = make_model(graph)
+
+# ensure we do not get too new opset version:
+del onnx_model.opset_import[:]
+opset = onnx_model.opset_import.add()
+opset.version = 17
+
+# Let's check the model is consistent, this function is described in
+# section Checker and Shape Inference.
+check_model(onnx_model)
+
+# The serialization
+with open("ranking_model.onnx", "wb") as f:
+    f.write(onnx_model.SerializeToString())
+
+# the work is done, let's display it...
+print(onnx_model)