code: def test_op_out_variant(self):
      mesh = DeviceMesh(self.device_type, list(range(self.world_size)))
      input_tensor = torch.randn((12, 3), device=self.device_type)
      sharded_dt_input = distribute_tensor(
          input_tensor, mesh, [Shard(0)]
      )
      expected_dt = sharded_dt_input.clone() + 3
      sharded_dt_out = sharded_dt_input.clone()
      res = torch.add(sharded_dt_input, 3, out=sharded_dt_out)
      # op out variant should be the same instance before and after
      self.assertTrue(res is sharded_dt_out)
      self.assertEqual(
          sharded_dt_out.to_local(), expected_dt.to_local()
      )
  
      # test op out variant with other spec and make sure out spec not change
      replica_spec = [Replicate()]
      replicate_out = distribute_tensor(
          input_tensor, mesh, replica_spec
      )
      expected_dt = replicate_out.clone() + 3
      res = torch.add(sharded_dt_input, 3, out=replicate_out)
      self.assertTrue(res is replicate_out)
      self.assertTrue(res.placements == tuple(replica_spec))
      self.assertEqual(replicate_out.to_local(), expected_dt.to_local())
  
imports: import torch
  from torch.distributed._tensor import DeviceMesh, distribute_tensor, DTensor
  from torch.distributed._tensor.placement_types import _Partial, Replicate, Shard
  from torch.testing._internal.common_utils import run_tests
  from torch.testing._internal.distributed._tensor.common_dtensor import DTensorConverter, DTensorTestBase, with_comms
label_desc: Use image node_id 6 for calling the DistTensorOpsTest obj's underlying member method code with example usage: obj.test_op_out_variant() without return types