Liliana Terry focused on enhancing the robustness of quantization workflows in the pytorch/ao repository by addressing dependency management challenges related to optional GPU libraries. She removed the hard dependency on fbgemm_gpu, updating the quantization function logic to gracefully handle its absence and prevent runtime errors. This work involved careful rollback of previous dependency additions and defensive programming to ensure stable behavior across diverse environments. Using Python and leveraging her expertise in machine learning and quantization, Liliana improved runtime stability and reduced the risk of import-time failures, demonstrating thoughtful engineering depth in maintaining project reliability during evolving hardware support scenarios.