Our indexer is encapsulated by a PyTorch nn.Module and serialized into a .pt file, using LibTorch , which can later be loaded in C++ code bases. This is how we are able to develop a CrystFEL plug-in that loads this model and integrates it into their pipeline. While this might not be the best way to do it to maximize performance, it allowed us to benchmark the quality of our indexer using the integrated tools within CrystFEL. See the Appendix A.2 for more details on TORO's implementations.