Worked on the pytorch/pytorch repository to address a core issue in tensor operations, focusing on numerical correctness and stable data type propagation. Delivered a targeted fix for the vector_norm function by introducing the missing to_result_dtype call, ensuring that output tensors consistently match the expected data type. This change improved reliability for downstream models, particularly in mixed-precision workflows where dtype consistency is critical. The work involved Python and leveraged skills in data manipulation and numerical computing to enhance the robustness of a frequently used mathematical operation, reducing the risk of dtype-related errors across models that depend on vector_norm’s output.