import tensorflow as tf

            # Convolution bank: concatenate on the last axis to stack channels from all convolutions
            conv_outputs = tf.concat(
                [conv1d(inputs, k, 128, tf.nn.relu, is_training, 'conv1d_%d' % k) for k in range(1, K + 1)],
                axis=-1
            )

        # Maxpooling:
        maxpool_output = tf.layers.max_pooling1d(
            conv_outputs,
            pool_size=2,
            strides=1,
            padding='same')

        # Two projection layers:
