During November 2024, Dheeraj Akula contributed to the pytorch/FBGEMM repository by enhancing meta-device compatibility and inference-mode robustness for PyTorch integration. He implemented fake tensor support for the fbgemm::all_to_one_device operator, enabling it to return correctly shaped and typed outputs on the meta device, which is essential for deployment workflows and shape inference. Additionally, Dheeraj addressed Autograd behavior for the jagged_to_padded_dense operator by registering it with CompositeImplicitAutograd, ensuring correct decomposition when Autograd is disabled during inference. His work leveraged C++, Python, and GPU computing, demonstrating a focused approach to improving reliability and deployment readiness in production environments.