No as far as I know but you can create a new dataset class which contains both datasets as an object.
Then, just return 2 tensors concatenated in an arbitrary dimension and reshape it in the batch, something like
class BigDataset()
  dataset1 =  dataset()
  dataset2 = dataset()
  assert len(dataset1) == len(dataset2)
  __len__
  return len(dataset1)
 __getitem__
  return torch.stack([dataset1.__getitem__(idx),dataset2.__getitem__(idx)]


for i in dataloader():
  i = i.view(-1,dimensions)