final InvertibleCryptographer[] invertibles = new InvertibleCryptographer[threadCount];
        final OneWayCryptographer[] oneWays = new OneWayCryptographer[threadCount];

        for (int i = 0; i < threadCount; i++) {
            final int index = i;
            threads[i] = new Thread(() -> {
                invertibles[index] = securityResourceProvider.providePrimaryInvertibleCryptographer();

 *   <dt>{@link BiMap}
 *   <dd>An extension of {@link java.util.Map} that guarantees the uniqueness of its values as well
 *       as that of its keys. This is sometimes called an "invertible map," since the restriction on
 *       values enables it to support an {@linkplain BiMap#inverse inverse view} -- which is another
 *       instance of {@code BiMap}.
 *   <dt>{@link Table}

 *   <dt>{@link BiMap}
 *   <dd>An extension of {@link java.util.Map} that guarantees the uniqueness of its values as well
 *       as that of its keys. This is sometimes called an "invertible map," since the restriction on
 *       values enables it to support an {@linkplain BiMap#inverse inverse view} -- which is another
 *       instance of {@code BiMap}.
 *   <dt>{@link Table}

bits.UintSize; i++ { out.montgomeryMul(out, out, m) if k := (e >> (bits.UintSize - i - 1)) & 1; k != 0 { out.montgomeryMul(out, xR, m) } } return out.montgomeryReduction(m) } // InverseVarTime calculates x = a⁻¹ mod m and returns (x, true) if a is // invertible. Otherwise, InverseVarTime returns (x, false) and x is not // modified. // // a must be reduced modulo m, but doesn't need to have the same size. The // output will be resized to the size of m and overwritten. // //go:norace func (x *Nat) InverseVarTime(a...

        AVX support.
    *   Deprecate
        `tf.compat.v1.lite.experimental.get_potentially_supported_ops`. Use
        `tf.lite.TFLiteConverter` directly to check whether a model is
        convertible.
    *   Add support to select one of three different built-in op resolvers
    *   Enabled post training with calibrations for models that require user
        provided TensorFlow Lite custom op libraries via