Worked on improving the reliability of the CUDA test suite in the pytorch/pytorch repository by addressing issues related to test failures in environments lacking the CUDA toolkit. Implemented a conditional skip mechanism using Python’s unittest framework, specifically adding logic to skip CUDA-related tests when NVCC is not available. This approach reduced false negatives and minimized noise in continuous integration pipelines by ensuring that tests only run when the necessary CUDA dependencies are present. The solution involved integrating CUDA detection and proper test gating, resulting in more stable and accurate test outcomes. Focus areas included Python, CUDA, and unit testing practices.