'Combines output of multiple convolutional towers into a single tensor.
It stacks towers one on top another (in height dim) in a 4x1 grid.
The order is arbitrary design choice and shouldn\'t matter much.
Args:
nets: list of tensors of shape=[batch_size, height, width, num_features].
Returns:
A tensor of shape [batch_size, seq_length, features_size].'

def pool_views_fn(self, nets):

    with tf.variable_scope('pool_views_fn/STCK'):
        net = tf.concat(nets, 1)
        batch_size = net.get_shape().dims[0].value
        feature_size = net.get_shape().dims[3].value
        return tf.reshape(net, [batch_size, (-1), feature_size])
