Tensor labels unexpected behaviour storage policies

Hi, I don't have time for PR atm, but quickly reporting a bug when iterating targets in the `storage_policy.py`.
Iteration over the targets in ClassBalanced and Parameterized Buffer will see each sample as a new class when `targets` is a tensor.

For example

for idx, target in enumerate(new_data.targets):
# if target is tensor, will be seen as separate class (because different id between tensors)

# Quick fix:
target = int(target)
...

For me this was the case when creating a `dataset_benchmark`, when first applying `wrap_with_task_labels`, this would return tensor-labels (even if the original dataset returned int's).

The quick fix above worked, but the below solution did not, by explicitly passing a targets transform:

target_to_int = transforms.Lambda(lambda x: int(x))
return dataset_benchmark(
train_datasets=wrap_with_task_labels(train_sets, target_transform=target_to_int),
...
)

def wrap_with_task_labels(datasets, target_transform):
return [AvalancheDataset(ds, task_labels=idx, target_transform=target_transform) for idx, ds in enumerate(datasets)]

Hope it helps!