Worked on the keras-team/keras repository to address a misleading error message in the ReLU activation function, focusing on improving developer experience and code reliability. Applied Python and deep learning expertise to deliver a targeted bug fix, while also expanding the unit testing framework to cover ReLU, constraints, and regularizers. Enhanced code quality by refining formatting, sorting imports, and updating the .gitignore file in response to code review feedback. Integrated continuous integration checks into the workflow, ensuring that all changes aligned with repository standards. The work emphasized maintainability and stability, contributing to a more robust and user-friendly machine learning library.