throw new RuntimeException("Failed to create Mac instance", e);
            }

            mac.update(data, offset, length);
            final byte[] sig = mac.doFinal();
            System.arraycopy(sig, 0, data, offset + SIGNATURE_OFFSET, SIGNATURE_LENGTH);
        } finally {
            this.signingLock.unlock();
        }
    }

    /**
     *

        // Process message (will be encrypted)
        final byte[] output = new byte[cipher.getOutputSize(message.length)];
        int len = cipher.processBytes(message, 0, message.length, output, 0);
        len += cipher.doFinal(output, len);

        // Split ciphertext and authentication tag
        final int tagLength = getAuthTagLength();
        final byte[] ciphertext = new byte[message.length];

            GCMParameterSpec gcmSpec = new GCMParameterSpec(GCM_TAG_SIZE, iv);
            cipher.init(Cipher.ENCRYPT_MODE, masterKey, gcmSpec);

            // Encrypt
            byte[] ciphertext = cipher.doFinal(plaintextBytes);

            // Combine IV and ciphertext
            byte[] result = new byte[GCM_IV_SIZE + ciphertext.length];
            System.arraycopy(iv, 0, result, 0, GCM_IV_SIZE);

            log.debug("Truncated " + Hexdump.toHexString(trunc));
        }

        if ((this.ntlmsspFlags & NtlmFlags.NTLMSSP_NEGOTIATE_KEY_EXCH) != 0) {
            try {
                trunc = this.sealClientHandle.doFinal(trunc);
                if (log.isDebugEnabled()) {
                    log.debug("Encrypted " + Hexdump.toHexString(trunc));
                }
            } catch (final GeneralSecurityException e) {

            for (int i = 0; i < SIGNATURE_LENGTH; i++) {
                data[SIGNATURE_OFFSET + i] = 0;
            }

            mac.update(data, 0, data.length);
            byte[] signature = mac.doFinal();

            // Place signature in data
            System.arraycopy(signature, 0, data, SIGNATURE_OFFSET, SIGNATURE_LENGTH);

            boolean result = digest.verify(data, 0, data.length, 0, msg);

        *   `tf.math.argmin`/`tf.math.argmax` for input data type `tf.bool` on
            CPU.
        *   `tf.nn.gelu` op for output data type `tf.float32` and quantization
            on CPU.
    *   Add nominal support for unsigned 16-bit integer tensor types. Note that
        very few TFLite kernels support this type natively, so its use in mobile
        ML authoring is generally discouraged.