code: def apply_step(self, variables, deltas, loss_sampled):
          
          update_stats_op = self.compute_and_apply_stats(
              loss_sampled, var_list=var_list)
          grads = [(a, b) for a, b in zip(deltas, varlist)]
          kfacOptim, _ = self.apply_gradients_kfac(grads)
          return kfacOptim
docs: Applies the given (and already calculated) step deltas to the variable values.
  
          Args:
              variables: List of variables.
              deltas: List of deltas of same length.
              loss_sampled : the sampled loss
  
          Returns:
              The step-applied operation. A tf.group of tf.assign_add ops.