code: def test_torch_based_feature_store_repr(in_memory):
      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"),
                  in_memory=True,
              ),
              gb.OnDiskFeatureData(
                  domain="edge",
                  type="paper:cites:paper",
                  name="b",
                  format="numpy",
                  path=os.path.join(test_dir, "b.npy"),
                  in_memory=in_memory,
              ),
          ]
          feature_store = gb.TorchBasedFeatureStore(feature_data)
  
          expected_feature_store_str = (
              "TorchBasedFeatureStore(\n"
              "    {(<OnDiskFeatureDataDomain.NODE: 'node'>, 'paper', 'a'): TorchBasedFeature(\n"
              "        feature=tensor([[1, 2, 4],\n"
              "                        [2, 5, 3]]),\n"
              "        metadata={},\n"
              "    ), (<OnDiskFeatureDataDomain.EDGE: 'edge'>, 'paper:cites:paper', 'b'): TorchBasedFeature(\n"
              "        feature=tensor([[[1, 2],\n"
              "                         [3, 4]],\n"
              "\n"
              "                        [[2, 5],\n"
              "                         [3, 4]]]),\n"
              "        metadata={},\n"
              "    )}\n"
              ")"
          )
  
          assert (
              repr(feature_store) == expected_feature_store_str
          ), feature_store
  
          a = b = feature_data = None
          feature_store = expected_feature_store_str = None
  
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 11 for calling a global function with example usage: test_torch_based_feature_store_repr(in_memory) without return types