Worked on the pytorch/ao repository to enhance the robustness of quantization workflows by addressing dependency issues related to optional GPU libraries. Focused on improving runtime stability, the developer removed the fbgemm_gpu dependency and updated quantization function handling to prevent import-time errors in environments lacking Fbgemm GPU support. This involved careful dependency management and the rollback of previous changes that introduced hard dependencies, ensuring clearer behavior across diverse setups. Utilizing Python and machine learning expertise, the work demonstrated a methodical approach to defensive programming, prioritizing stable execution paths and maintainable code in quantization processes without introducing new features.