def METHOD_NAME(self):
    """
    Synchronize running_mean, and running_var to self.bn.
    Call this before eval, then call model.eval();
    When eval, forward function will call self.bn instead of self.split_bn,
    During this time the running_mean, and running_var of self.bn has been obtained from
    self.split_bn.
    """
    if self.split_bn.training:
        bn_mean_tensor, bn_variance_tensor = self._get_aggregated_mean_std(
            self.split_bn._mean,
            self.split_bn._variance,
            self.num_splits,
        )
        self.bn._mean.set_value(bn_mean_tensor)
        self.bn._variance.set_value(bn_variance_tensor)