code: def get_total_kl_cost(name="total_kl_cost", filter_by_name_substring=None):
    
    all_variable_metadata = get_variable_metadata(filter_by_name_substring)
    if not all_variable_metadata:
      tf.logging.warning("No Bayes by Backprop variables found!")
      return tf.constant(0.0, shape=())
    return tf.add_n([md.kl_cost for md in all_variable_metadata], name=name)
docs: Get the total cost for all (or a subset of) the stochastic variables.
  
    Args:
      name: A name for the tensor representing the total kl cost.
      filter_by_name_substring: A string used to filter which variables count
        toward the total KL cost. By default, this argument is `None`, and all
        variables trained using Bayes by Backprop are included. If this argument
        is provided, the variables whose KL costs are summed will be all those
        whose name contains `filter_by_name_substring`. An example use of this
        would be to select all variables within a particular scope.
  
    Returns:
      A tensor representing the total KL cost in the ELBO loss.