of s in GF(N) using Fermat's method // (exponentiation modulo P - 2, per Euler's theorem) kInv.Exp(k, c.nMinus2, c.N) } // hashToNat sets e to the left-most bits of hash, according to // FIPS 186-5, Section 6.4.1, point 2 and Section 6.4.2, point 3. func hashToNat[P Point[P]](c *Curve[P], e *bigmod.Nat, hash []byte) { // ECDSA asks us to take the left-most log2(N) bits of hash, and use them as // an integer modulo N. This is the absolute worst of all worlds: we still // have to reduce, because the...

of s in GF(N) using Fermat's method // (exponentiation modulo P - 2, per Euler's theorem) kInv.Exp(k, c.nMinus2, c.N) } // hashToNat sets e to the left-most bits of hash, according to // FIPS 186-5, Section 6.4.1, point 2 and Section 6.4.2, point 3. func hashToNat[P Point[P]](c *Curve[P], e *bigmod.Nat, hash []byte) { // ECDSA asks us to take the left-most log2(N) bits of hash, and use them as // an integer modulo N. This is the absolute worst of all worlds: we still // have to reduce, because the...

    *   Various random number generation improvements:
    *   Add a fast path for default `random_uniform`
    *   `random_seed` documentation improvement.
    *   `RandomBinomial` broadcasts and appends the sample shape to the left
        rather than the right.
    *   Added `tf.random.stateless_binomial`, `tf.random.stateless_gamma`,
        `tf.random.stateless_poisson`
    *   `tf.random.stateless_uniform` now supports unbounded sampling of `int`