match TF Core/C++; i.e., a contiguous array of
        `tensorflow::tstring`/`TF_TString`s.
    *   C-API functions `TF_StringDecode`, `TF_StringEncode`, and
        `TF_StringEncodedSize` are no longer relevant and have been removed; see
        `core/platform/ctstring.h` for string access/modification in C.
    *   `tensorflow.python`, `tensorflow.core` and `tensorflow.compiler` modules

In some cases, for pure data validation and processing, you can get performance improvements of **20x** or more. This means 2,000% or more. 🤯

panic("bigmod: modulus for Exp must be odd") } // We use a 4 bit window. For our RSA workload, 4 bit windows are faster // than 2 bit windows, but use an extra 12 nats worth of scratch space. // Using bit sizes that don't 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(),...

numbers (except 2), such that the product of any // three primes fits in a uint32. // // More primes cause fewer Miller-Rabin tests of composites (nothing can help // with the final test on the actual prime) but have diminishing returns: these // 255 primes catch 84.9% of composites, the next 255 would catch 1.5% more. // Adding primes can still be marginally useful since they only compete with the // (much more expensive) first Miller-Rabin round for candidates that were not // rejected by the previous...