So, to answer my own question. According to the (pending) contribution to the pytorch library by the author here 5, the documentation of the newly added class function swap_swa_sgd_update_bn says
    def swap_swa_sgd_update_bn(self, loader, model, device=None):
        r"""Swaps variables and swa buffers and updates BatchNorm buffers.
        This method is equivalent to subsequently calling 
        :meth:`opt.swap_swa_sgd()` and 
        :meth:`opt.bn_update(loader, model, device)`. It's meant to be called in
        the end of training to use the collected swa running averages and update
        the Batch Normalization running activation statistics in the model. It 
        can also be used to evaluate the SWA running averages during training; 
        to continue training `swap_swa_sgd` should be called again.
        """
        self.swap_swa_sgd()
        self.bn_update(loader, model, device)