code: def test_feature_store_to_device(device):
      with tempfile.TemporaryDirectory() as test_dir:
          a = torch.tensor([[1, 2, 4], [2, 5, 3]])
          b = torch.tensor([[[1, 2], [3, 4]], [[2, 5], [3, 4]]])
          write_tensor_to_disk(test_dir, "a", a, fmt="torch")
          write_tensor_to_disk(test_dir, "b", b, fmt="numpy")
          feature_data = [
              gb.OnDiskFeatureData(
                  domain="node",
                  type="paper",
                  name="a",
                  format="torch",
                  path=os.path.join(test_dir, "a.pt"),
              ),
              gb.OnDiskFeatureData(
                  domain="edge",
                  type="paper:cites:paper",
                  name="b",
                  format="numpy",
                  path=os.path.join(test_dir, "b.npy"),
              ),
          ]
          feature_store = gb.TorchBasedFeatureStore(feature_data)
          feature_store2 = feature_store.to(device)
          if device == "pinned":
              is_feature_store_pinned(feature_store2)
          elif device == "cuda":
              is_feature_store_on_cuda(feature_store2)
  
          # The original variable should be untouched.
          is_feature_store_on_cpu(feature_store)
  
imports: import os
  import tempfile
  import unittest
  from functools import reduce
  from operator import mul
  import backend
  import numpy
  import pydantic
  import pytest
  import torch
  from dgl import graphbolt
label_desc: Use image node_id 6 for calling a global function with example usage: test_feature_store_to_device(device) without return types