when you use reduction='none', it will compute the MSE-loss per element. So you can add an additional torch.sum() or torch.mean() after the output of MSE-loss. Alternatively, you can use reduction='sum' or reduction='mean' to the same thing.
However, using reduction='none' gives you more flexibility. For example, if you want to calculate the sum over one dimension but the calculate the mean of the other dimension, you can use loss=MSELoss(reduction='none') followed by loss=torch.mean(loss, dim=1) and then loss=torch.sum(loss, dim=0)