code: def runtest(world_rank: int, world_size: int):
      from pylibwholegraph.torch.initialize import (
          init_torch_env_and_create_wm_comm,
      )
  
      wm_comm, _ = init_torch_env_and_create_wm_comm(
          world_rank,
          world_size,
          world_rank,
          world_size,
      )
      wm_comm = wm_comm.wmb_comm
  
      generator = np.random.default_rng(62)
      arr = (
          generator.integers(low=0, high=100, size=100_000)
          .reshape(10_000, -1)
          .astype("float64")
      )
  
      fs = FeatureStore(backend="wholegraph")
      fs.add_data(arr, "type2", "feat1")
      wm_comm.barrier()
  
      indices_to_fetch = np.random.randint(
          low=0, high=len(arr), size=1024
      )
      output_fs = fs.get_data(
          indices_to_fetch, type_name="type2", feat_name="feat1"
      )
      assert isinstance(output_fs, torch.Tensor)
      assert output_fs.is_cuda
      expected = arr[indices_to_fetch]
      np.testing.assert_array_equal(output_fs.cpu().numpy(), expected)
  
      wmb.finalize()
  
imports: import pytest
  import numpy
  from cugraph.gnn import FeatureStore
  from cugraph.utilities.utils import import_optional, MissingModule
label_desc: Use image node_id 1 for calling a global function with example usage: runtest(world_rank, world_size) without return types