use untyped_storage if present (#5863)

Fixes #5862.

### Description

if `untyped_storage()` is present (pytorch 2) use it, else use
`storage()`.

### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [ ] Breaking change (fix or new feature that would cause existing
functionality to change).
- [ ] New tests added to cover the changes.
- [ ] Integration tests passed locally by running `./runtests.sh -f -u
--net --coverage`.
- [ ] Quick tests passed locally by running `./runtests.sh --quick
--unittests --disttests`.
- [ ] In-line docstrings updated.
- [ ] Documentation updated, tested `make html` command in the `docs/`
folder.

Signed-off-by: Richard Brown <[email protected]>

monai/data/__init__.py

@@ -117,18 +117,19 @@
 with contextlib.suppress(BaseException):
     from multiprocessing.reduction import ForkingPickler
 
-    def _rebuild_meta(cls, storage, metadata):
+    def _rebuild_meta(cls, storage, dtype, metadata):
         storage_offset, size, stride, meta_dict = metadata
-        t = cls([], dtype=storage.dtype, device=storage.device)
+        t = cls([], dtype=dtype, device=storage.device)
         t.set_(storage._untyped() if hasattr(storage, "_untyped") else storage, storage_offset, size, stride)
         t.__dict__ = meta_dict
         return t
 
     def reduce_meta_tensor(meta_tensor):
-        storage = meta_tensor.storage()
+        storage = meta_tensor.untyped_storage() if hasattr(meta_tensor, "untyped_storage") else meta_tensor.storage()
+        dtype = meta_tensor.dtype
         if storage.is_cuda:
             raise NotImplementedError("sharing CUDA metatensor across processes not implemented")
         metadata = (meta_tensor.storage_offset(), meta_tensor.size(), meta_tensor.stride(), meta_tensor.__dict__)
-        return _rebuild_meta, (type(meta_tensor), storage, metadata)
+        return _rebuild_meta, (type(meta_tensor), storage, dtype, metadata)
 
     ForkingPickler.register(MetaTensor, reduce_meta_tensor)