During May 2025, Mateusz Dziadowiec enhanced the backward graph copy pipeline in the pytorch/pytorch repository, focusing on cross-device support and improved robustness for autograd in fused modules. Using Python and leveraging deep learning and machine learning expertise, Mateusz implemented call_module support within copy_paste_aot_backward_graph, ensuring consistent behavior across CPU and HPU environments. The work included concrete error handling for tensor indexing, reducing failure modes during model export and import, and refined gradient retention for non-leaf tensors to maintain correct gradient flow in complex, multi-module models. This targeted feature deepened production portability and reliability for advanced PyTorch architectures.