import tensorflow as tf

        depth=depth)


def cbhg(inputs, input_lengths, is_training, scope, K, projections, depth):
    with tf.variable_scope(scope):
        with tf.variable_scope('conv_bank'):
            # 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
