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t0 + t0 z3.Mul(t0, z3) // Z3 := t0 * Z3 x3.Sub(x3, z3) // X3 := X3 - Z3 z3.Mul(t0, t1) // Z3 := t0 * t1 z3.Add(z3, z3) // Z3 := Z3 + Z3 z3.Add(z3, z3) // Z3 := Z3 + Z3 q.x.Set(x3) q.y.Set(y3) q.z.Set(z3) return q } // p256AffinePoint is a point in affine coordinates (x, y). x and y are still // Montgomery domain elements. The point can't be the point at infinity. type p256AffinePoint struct { x, y fiat.P256Element } func (p *p256AffinePoint) Projective() *P256Point { pp := &P256Point{x: p.x, y: p.y}...

t0 + t0 z3.Mul(t0, z3) // Z3 := t0 * Z3 x3.Sub(x3, z3) // X3 := X3 - Z3 z3.Mul(t0, t1) // Z3 := t0 * t1 z3.Add(z3, z3) // Z3 := Z3 + Z3 z3.Add(z3, z3) // Z3 := Z3 + Z3 q.x.Set(x3) q.y.Set(y3) q.z.Set(z3) return q } // p256AffinePoint is a point in affine coordinates (x, y). x and y are still // Montgomery domain elements. The point can't be the point at infinity. type p256AffinePoint struct { x, y fiat.P256Element } func (p *p256AffinePoint) Projective() *P256Point { pp := &P256Point{x: p.x, y: p.y}...

    flexible and reproducible package, is available via the new
    `tf.contrib.data.Dataset.from_generator` method!
*   Fix `tf.contrib.distributions.Affine` incorrectly computing
    log-det-jacobian.
*   Fix `tf.random_gamma` incorrectly handling non-batch, scalar draws.
*   Resolved a race condition in TensorForest TreePredictionsV4Op.