diff --git a/tests/pytorch/demo/demo.py b/tests/pytorch/demo/demo.py
new file mode 100644
index 000000000..f189e5ebb
--- /dev/null
+++ b/tests/pytorch/demo/demo.py
@@ -0,0 +1,31 @@
+import torch
+import torch._dynamo as dynamo
+from torch._inductor.decomposition import decompositions as inductor_decomp
+
+from buddy.compiler.frontend import DynamoCompiler
+from buddy.compiler.ops import tosa
+
+# Define the target function or model.
+def foo(x, y):
+    return x * y + x
+
+# Define the input data.
+float32_in1 = torch.randn(10).to(torch.float32)
+float32_in2 = torch.randn(10).to(torch.float32)
+int32_in1 = torch.randint(0, 10, (10,)).to(torch.int32)
+int32_in2 = torch.randint(0, 10, (10,)).to(torch.int32)
+
+# Initialize the dynamo compiler.
+dynamo_compiler = DynamoCompiler(
+    primary_registry=tosa.ops_registry,
+    aot_autograd_decomposition=inductor_decomp,
+)
+
+# Pass the function and input data to the dynamo compiler's importer, the 
+# importer will first build a graph. Then, lower the graph to top-level IR. 
+# (tosa, linalg, etc.). Finally, accepts the generated module and weight parameters.
+graphs = dynamo_compiler.importer(foo, *(float32_in1, float32_in2))
+graph = graphs[0]
+graph.lower_to_top_level_ir()
+
+print(graph._imported_module)