Worked on improving the robustness of fused operations in the tensorflow/tensorflow repository, specifically targeting the FuseFullyConnectedAndAdd operator path. Addressed a critical issue where the absence of a bias could lead to segmentation faults and incorrect broadcasting by ensuring that, when bias is NoneType, the operator falls back to using the add output type for bias typing. This change enhanced the stability of FP32 and FP16 inference and training workloads that rely on fused tensor operations. The solution was implemented in C++ and leveraged expertise in compiler design and machine learning, with focused validation to ensure correct operator behavior in production scenarios.