the i-th digit. // // Instead of running two loops, one for Step 1 and one for Steps 2–6, // the result of Step 1 is computed during the next loop. This is // possible because each iteration only uses T[i] in Step 2 and then // discards it in Step 6. d := bLimbs[i] c1 := addMulVVW(T[i:n+i], aLimbs, d) // Step 6 is replaced by shifting the virtual window we operate // over: T of the algorithm is T[i:] for us. That means that T1 in // Step 2 (T mod 2^_W) is simply T[i]. k0 in Step 3 is our m0inv. Y...

        or a list/tuple of arrays into two `Dataset` objects (e.g. train/test).
    *   Added step granularity to `BackupAndRestore` callback for handling
        distributed training failures & restarts. The training state can now be
        restored at the exact epoch and step at which it was previously saved
        before failing.
    *   Added

2F3A          ; mapped                 ; 5F61          # 3.0  KANGXI RADICAL BRISTLE
2F3B          ; mapped                 ; 5F73          # 3.0  KANGXI RADICAL STEP
2F3C          ; mapped                 ; 5FC3          # 3.0  KANGXI RADICAL HEART
2F3D          ; mapped                 ; 6208          # 3.0  KANGXI RADICAL HALBERD

* 🌐 Remove Vietnamese note about missing translation. PR [#9957](https://github.com/tiangolo/fastapi/pull/9957) by [@tiangolo](https://github.com/tiangolo).

### Internal

* 👷 Add GitHub Actions step dump context to debug external failures. PR [#10008](https://github.com/tiangolo/fastapi/pull/10008) by [@tiangolo](https://github.com/tiangolo).