Fix AutoencoderKlMaisi forcing CUDA transfer on CPU inputs (#8736)

Fixes #8735 

### Description

This PR fixes a bug in `AutoencoderKlMaisi` where the model would force
a transfer to `cuda` even if the input tensors and the model were placed
on the `CPU`.

### 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: ytl0623 <david89062388@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com>

Author: 3 people

monai/apps/generation/maisi/networks/autoencoderkl_maisi.py

@@ -214,6 +214,8 @@ def _concatenate_tensors(self, outputs: list[torch.Tensor], split_size: int, pad
         if max(outputs[0].size()) < 500:
             x = torch.cat(outputs, dim=self.dim_split + 2)
         else:
+            target_device = outputs[0].device
+
             x = outputs[0].clone().to("cpu", non_blocking=True)
             outputs[0] = torch.Tensor(0)
             _empty_cuda_cache(self.save_mem)
@@ -225,7 +227,9 @@ def _concatenate_tensors(self, outputs: list[torch.Tensor], split_size: int, pad
                 if self.print_info:
                     logger.info(f"MaisiConvolution concat progress: {k + 1}/{len(outputs) - 1}.")
 
-            x = x.to("cuda", non_blocking=True)
+            if target_device.type != "cpu":
+                x = x.to(target_device, non_blocking=True)
+
         return x
 
     def forward(self, x: torch.Tensor) -> torch.Tensor: