code: def regularize_cost_from_collection(name='regularize_cost'):
      
      ctx = get_current_tower_context()
      if not ctx.is_training:
          # TODO Currently cannot build the wd_cost correctly at inference,
          # because ths vs_name used in inference can be '', therefore the
          # variable filter will fail
          return tf.constant(0, dtype=tf.float32, name='empty_' + name)
  
      # NOTE: this collection doesn't always grow with towers.
      # It only grows with actual variable creation, but not get_variable call.
      if ctx.has_own_variables:   # be careful of the first tower (name='')
          losses = ctx.get_collection_in_tower(tfv1.GraphKeys.REGULARIZATION_LOSSES)
      else:
          losses = tfv1.get_collection(tfv1.GraphKeys.REGULARIZATION_LOSSES)
      if len(losses) > 0:
          logger.info("regularize_cost_from_collection() found {} regularizers "
                      "in REGULARIZATION_LOSSES collection.".format(len(losses)))
  
          def maploss(l):
              assert l.dtype.is_floating, l
              if l.dtype != tf.float32:
                  l = tf.cast(l, tf.float32)
              return l
  
          losses = [maploss(l) for l in losses]
          reg_loss = tf.add_n(losses, name=name)
          return reg_loss
      else:
          return tf.constant(0, dtype=tf.float32, name='empty_' + name)
docs: Get the cost from the regularizers in ``tf.GraphKeys.REGULARIZATION_LOSSES``.
      If in replicated mode, will only regularize variables created within the current tower.
  
      Args:
          name (str): the name of the returned tensor
  
      Returns:
          tf.Tensor: a scalar, the total regularization cost.