* Cache}</a>, <a
 * href="https://www.javadoc.io/doc/com.github.ben-manes.caffeine/caffeine/latest/com.github.benmanes.caffeine/com/github/benmanes/caffeine/cache/LoadingCache.html">{@code
 * LoadingCache}</a>, <a

 * the License.
 */

/**
 * {@linkplain CacheBuilder Discouraged} (in favor of <a
 * href="https://github.com/ben-manes/caffeine/wiki">Caffeine</a>) caching utilities.
 *
 * <p>The core interface used to represent caches is {@link Cache}. In-memory caches can be
 * configured and created using {@link CacheBuilder}, with cache entries being loaded by {@link

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.