remaining limbs, reducing modulo m each time. for i >= 0 { out.shiftIn(x.limbs[i], m) i-- } return out } // ExpandFor ensures x has the right size to work with operations modulo m. // // The announced size of x must be smaller than or equal to that of m. func (x *Nat) ExpandFor(m *Modulus) *Nat { return x.expand(len(m.nat.limbs)) } // resetFor ensures out has the right size to work with operations modulo m. // // out is zeroed and may start at any size. func (out *Nat) resetFor(m *Modulus) *Nat { return...

remaining limbs, reducing modulo m each time. for i >= 0 { out.shiftIn(x.limbs[i], m) i-- } return out } // ExpandFor ensures x has the right size to work with operations modulo m. // // The announced size of x must be smaller than or equal to that of m. func (x *Nat) ExpandFor(m *Modulus) *Nat { return x.expand(len(m.nat.limbs)) } // resetFor ensures out has the right size to work with operations modulo m. // // out is zeroed and may start at any size. func (out *Nat) resetFor(m *Modulus) *Nat { return...

    *   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`
        types.