code: def lengths_to_area_mask(feature_length, length, max_area_size):
    
  
    paddings = tf.cast(tf.expand_dims(
        tf.logical_not(
            tf.sequence_mask(feature_length, maxlen=length)), 2), tf.float32)
    _, _, area_sum, _, _ = compute_area_features(paddings,
                                                 max_area_width=max_area_size)
    mask = tf.squeeze(tf.logical_not(tf.cast(area_sum, tf.bool)), [2])
    return mask
docs: Generates a non-padding mask for areas based on lengths.
  
    Args:
      feature_length: a tensor of [batch_size]
      length: the length of the batch
      max_area_size: the maximum area size considered
    Returns:
      mask: a tensor in shape of [batch_size, num_areas]