Worked on enhancing the zjunlp/EasyEdit repository by addressing compatibility issues with PyTorch 2.0 forward hooks. Focused on updating the hook implementation to accept keyword arguments and robustly handle diverse model inputs and outputs, including tuples and hidden states commonly found in transformer architectures. This update improved the reliability of model behavior when using PyTorch 2.0 and above. The solution was delivered as a dedicated bug fix, complete with thorough test coverage and documentation. Leveraged expertise in Python, PyTorch, and deep learning to ensure seamless integration with existing data processing workflows and to support evolving machine learning model requirements.