Is your feature request related to a problem? Please describe.

if I want to track dice score on more than one dimensions, for example, if I need both average dice per class and average dice. it seems like my only options are
(1) create multiple DiceMetric object
(2) create 1 DiceMetric object to get average dice per class, then aggregate manually on top of that

if there is a better way, please let me know!
otherwise is it possible to let user pass a reduction argument to aggregate() method instead of __init__?

Describe alternatives you've considered

def aggregate(self, reduction=None):
  data = self.get_buffer()
  if not isinstance(data, torch.Tensor):
    raise ValueError("the data to aggregate must be PyTorch Tensor.")

  # do metric reduction
  if reduction is None: 
    reduction = self.reduction 
  f, not_nans = do_metric_reduction(data, self.reduction)
  return (f, not_nans) if self.get_not_nans else f