Worked on the onnx/onnx repository to address a nuanced issue in shape inference related to default attribute values. Focused on improving the robustness and correctness of model inferences by ensuring that shape inference logic correctly distinguishes between attributes that are absent and those present but unset. Leveraged protobuf and C++ to implement a patch that applies default numeric values only when appropriate, aligning with user-reported scenarios and issue #7573. Emphasized unit testing to validate the fix and reduce subtle shape errors across models. This targeted bug fix enhanced reliability in model evaluation without introducing new features during the period.