Worked on stabilizing distributed operations in the pytorch/TensorRT repository by reverting recent changes related to NCCL and complex-number handling. Addressed a regression by restoring the original behavior for distributed calls such as nccl_gather and nccl_reduce_scatter, ensuring compatibility and correct shape inference for complex numbers in distributed workflows. Simplified the PythonTorchTensorRTModule forward pass by removing complex-number processing logic, which reduced maintenance overhead and potential sources of error. Utilized Python and deep learning frameworks, focusing on distributed systems and TensorRT integration. The work prioritized maintaining performance characteristics and minimizing regression risk in large-scale distributed training environments.