TORO's implementation in Python and PyTorch further enhances its appeal. This implementation facilitates ease of maintenance and updates, scalability across CPUs and GPUs, and capitalizes on the wide array of libraries and tools offered by the modern AI development stack. Moreover, the streamlined codebase of a few hundred lines delivers a state-of-the-art performance, highlighting the remarkable advantages introduced by PyTorch in scientific software development. The ease of use of this codebase provides developers the ability to further modify and tailor this indexer to their specific needs.