The stand-alone implementation of TORO is coded in Python and consists of less than 500 lines of code. We rely on the PyTorch framework which is designed to exploit raw computing power by enabling parallelism, either on GPUs or CPU multi-cores. A cornerstone of PyTorch's coding principles is the use of large batches to be processed in parallel. For TORO this is not straightforward as different frames have different number of strong reflections. While Although XGandalf identifies some additional frames for Lysozyme, CXI ID 21 and CXI ID 180, its performance there is inferior to TORO, except for CXI ID 21 where they compare in terms of I/σ and Redundancy, while having a better CC at low resolution. For ID 83, XGandalf's performance on its uniquely indexed frames is less consistent, appearing to capture mostly noise. Data were generated using indexamajig with --no-retry --no-refine --no-check-cell.