diff --git a/e2eshark/onnx/operators/expand/model.py b/e2eshark/onnx/operators/expand/model.py
new file mode 100644
index 000000000..95c190ffa
--- /dev/null
+++ b/e2eshark/onnx/operators/expand/model.py
@@ -0,0 +1,94 @@
+# Copyright 2024 Advanced Micro Devices
+#
+# Licensed under the Apache License v2.0 with LLVM Exceptions.
+# See https://llvm.org/LICENSE.txt for license information.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+# run.py creates runmodel.py by concatenating this file model.py
+# and tools/stubs/onnxmodel.py
+# Description: testing Expand
+# See https://onnx.ai/onnx/intro/python.html for intro on creating
+# onnx model using python onnx API
+import numpy, torch, sys
+import onnxruntime
+from onnx import numpy_helper, TensorProto, save_model
+from onnx.helper import (
+    make_model,
+    make_node,
+    make_graph,
+    make_tensor_value_info,
+    make_tensor,
+)
+from onnx.checker import check_model
+
+# import from e2eshark/tools to allow running in current dir, for run through
+# run.pl, commutils is symbolically linked to allow any rundir to work
+sys.path.insert(0, "../../../tools/stubs")
+from commonutils import E2ESHARK_CHECK_DEF
+
+# Create an instance of it for this test
+E2ESHARK_CHECK = dict(E2ESHARK_CHECK_DEF)
+
+# Create an input (ValueInfoProto)
+X = make_tensor_value_info("X", TensorProto.FLOAT, [4, 5])
+
+shape_tensor = make_tensor(
+    name="shape",
+    data_type=TensorProto.INT64,
+    dims=(3,),
+    vals=[3, 4, 5],
+)
+
+shape_node = make_node(
+    "Constant",
+    inputs=[],
+    outputs=["shape"],
+    value=shape_tensor,
+)
+
+# Create an output
+Z = make_tensor_value_info("Z", TensorProto.FLOAT, [3, 4, 5])
+
+# Create a node (NodeProto)
+expand_node = make_node(
+    "Expand",
+    ["X", "shape"],
+    ["Z"],
+    "expand_node",  # node name  # inputs  # outputs
+)
+
+# Create the graph (GraphProto)
+graph = make_graph(
+    [shape_node, expand_node],
+    "expand_graph",
+    [X],
+    [Z],
+)
+
+# Create the model (ModelProto)
+onnx_model = make_model(graph)
+onnx_model.opset_import[0].version = 19
+
+# Save the model
+with open("model.onnx", "wb") as f:
+    f.write(onnx_model.SerializeToString())
+
+session = onnxruntime.InferenceSession("model.onnx", None)
+model_input_X = numpy.random.randn(4, 5).astype(numpy.float32)
+inputs = session.get_inputs()
+outputs = session.get_outputs()
+
+model_output = session.run(
+    [outputs[0].name],
+    {inputs[0].name: model_input_X},
+)
+
+print("Input shape:", model_input_X.shape)
+print("Output shape:", numpy.array(model_output[0]).shape)
+
+# Moving to torch to handle bfloat16 as numpy does not support bfloat16
+E2ESHARK_CHECK["input"] = [torch.from_numpy(model_input_X)]
+E2ESHARK_CHECK["output"] = [torch.from_numpy(arr) for arr in model_output]
+
+print("Input:", E2ESHARK_CHECK["input"])
+print("Output:", E2ESHARK_CHECK["output"])