import tensorflow as tf

    return outputs


def highwaynet(inputs, num_units=None, scope="highwaynet"):
    if not num_units:
        num_units = inputs.get_shape()[-1]
    with tf.variable_scope(scope):
        H = tf.layers.dense(inputs, units=num_units, activation=tf.nn.relu, name="dense1")
        T = tf.layers.dense(
            inputs,
            units=num_units,
            activation=tf.nn.sigmoid,
            bias_initializer=tf.constant_initializer(-1.0),
            name="dense2",
        )
        outputs = H * T + inputs * (1.0 - T)
    return outputs


def conv1d_banks(inputs, K=16, is_training=True, scope="conv1d_banks"):
    with tf.variable_scope(scope):
        outputs = tf.layers.conv1d(inputs, embed_size // 2, 1, padding="SAME")
        for k in range(2, K + 1):
            with tf.variable_scope("num_{}".format(k)):
