mod m, with R = 2^(_W * n) and // n = len(m.nat.limbs). // // This assumes that x is already reduced mod m. func (x *Nat) montgomeryReduction(m *Modulus) *Nat { // By Montgomery multiplying with 1 not in Montgomery representation, we // convert out back from Montgomery representation, because it works out to // dividing by R. one := NewNat().ExpandFor(m) one.limbs[0] = 1 return x.montgomeryMul(x, one, m) } // montgomeryMul calculates x = a * b / R mod m, with R = 2^(_W * n) and // n = len(m.nat.limbs),...

mod m, with R = 2^(_W * n) and // n = len(m.nat.limbs). // // This assumes that x is already reduced mod m. func (x *Nat) montgomeryReduction(m *Modulus) *Nat { // By Montgomery multiplying with 1 not in Montgomery representation, we // convert out back from Montgomery representation, because it works out to // dividing by R. one := NewNat().ExpandFor(m) one.limbs[0] = 1 return x.montgomeryMul(x, one, m) } // montgomeryMul calculates x = a * b / R mod m, with R = 2^(_W * n) and // n = len(m.nat.limbs),...

    * `tf.types.experimental.TraceType` now allows custom tf.function inputs to declare Tensor decomposition and type casting support.
    * Introducing `tf.types.experimental.FunctionType` as the comprehensive representation of the signature of `tf.function` callables. It can be accessed through the `function_type` property of `tf.function`s and `ConcreteFunction`s. See the `tf.types.experimental.FunctionType` documentation for more details.