Worked on the pytorch/torchrec repository to enhance error handling within the distributed sharding process. Focused on addressing a specific bug, the developer improved the debugging workflow by adding contextual information to error messages when a ZeroDivisionError occurs in the sharding path. This approach enables faster identification and resolution of issues during distributed training, reducing downtime and clarifying failure modes for both users and developers. The solution was implemented using Python and leveraged knowledge of distributed systems and robust error handling. The work demonstrated disciplined contribution practices, including clear commit documentation and direct linkage to tracked issues for traceability.