I think that the dataloader will fetch samples(which are tensors) in a batch and put them in a tensor with the first dimension being the batch size. If you want to get a list of lists then you probably need to make your own collate function.
If you don’t care about having a list of lists then you can put the labels in a tensor. For example, wrapping your list in a tensor will work so instead of this:
def __getitem__(self, index):
    return [1.0, 2.0, 3.0]

you can use this:
def __getitem__(self, index):
    return torch.FloatTensor([1.0, 2.0, 3.0])

If you want to see how to make your own collate function then look here:

  
    
    
    How to create a dataloader with variable-size input vision
  
  
    you need to write a custom collate_fn and pass it to your data loader. Questions about Dataloader and Dataset