divide 8 are more complex to implement, but // are likely to be more efficient if necessary. table := [(1 << 4) - 1]*Nat{ // table[i] = x ^ (i+1) // newNat calls are unrolled so they are allocated on the stack. NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), } table[0].set(x).montgomeryRepresenta(m) for i := 1; i < len(table); i++ { table[i].montgomeryMul(table[i-1], table[0], m) } out.resetFor(m) out.limbs[0]...

divide 8 are more complex to implement, but // are likely to be more efficient if necessary. table := [(1 << 4) - 1]*Nat{ // table[i] = x ^ (i+1) // newNat calls are unrolled so they are allocated on the stack. NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), NewNat(), } table[0].set(x).montgomeryRepresenta(m) for i := 1; i < len(table); i++ { table[i].montgomeryMul(table[i-1], table[0], m) } out.resetFor(m) out.limbs[0]...

            AVX512_BF16, AMX, etc.
            ([Intel Cascade Lake](https://www.intel.com/content/www/us/en/products/platforms/details/cascade-lake.html)
            and newer CPUs.)
            *   [Example performance speedups.](https://medium.com/intel-analytics-software/leverage-intel-deep-learning-optimizations-in-tensorflow-129faa80ee07)

## 0.115.4 (2024-10-27)

### Refactors

* ♻️ Update logic to import and check `python-multipart` for compatibility with newer version. PR [#12627](https://github.com/fastapi/fastapi/pull/12627) by [@tiangolo](https://github.com/tiangolo).

### Docs