If A represents the foundational idea of the Transformer model as proposed by Vaswani et al., and B represents the implementation techniques used in various applications, what does C represent when considering the contributions from Shazeer, Parmar, and Uszkoreit?

C is the collection of architectural and algorithmic refinements that extend the base Transformer idea and the practical implementation tricks. Shazeer introduced sparse attention to cut down computation, Parmar worked on efficient transformer variants, and Uszkoreit helped formalize multi‑head attention and its training tricks. Together these contributions make the Transformer faster, more scalable, and easier to train on very large datasets. For instance, a sparse‑attention transformer can process ten thousand tokens with far fewer operations than the original model, showing how C improves performance.