Worked on stabilizing shape inference within the ROCm/onnxruntime repository, focusing on improving support for GroupNormalization operations in ONNX Runtime. Addressed a bug in the symbolic shape inference pass by implementing logic to correctly handle GroupNorm, ensuring that models using this operation could be inferred accurately. This fix enhanced the correctness and reliability of model deployments on ROCm platforms. The work involved deep learning and machine learning concepts, leveraging Python to modify and extend the existing inference tooling. By resolving this issue, the developer contributed to more robust model support and improved deployment workflows for ONNX models utilizing GroupNormalization.