code: def test_mm(self):
      device_mesh = DeviceMesh(
          self.device_type, list(range(self.world_size))
      )
      shard0_spec = Shard(0)
      shard1_spec = Shard(1)
      replica_spec = Replicate()
  
      t1 = torch.randn(12, 8, requires_grad=True)
      t2 = torch.randn(8, 16, requires_grad=True)
      local_res = torch.mm(t1, t2)
  
      def test_placement_comb(
          placements1: List[Placement], placements2: List[Placement]
      ) -> None:
          dt1 = distribute_tensor(t1, device_mesh, placements1)
          dt2 = distribute_tensor(t2, device_mesh, placements2)
          dist_res: DTensor = cast(
              DTensor, torch.mm(dt1, dt2)
          ).redistribute(device_mesh, [replica_spec])
          self.assertEqual(dist_res.to_local(), local_res)
          # backward
          grad_dist_res = torch.ones_like(dist_res)
          dist_res.backward(grad_dist_res)
          self.assertIsNotNone(dt1.grad)
  
      placement_specs = [shard0_spec, shard1_spec, replica_spec]
      shard_specs_comb = list(
          itertools.product(placement_specs, placement_specs)
      )
      for spec in shard_specs_comb:
          test_placement_comb([spec[0]], [spec[1]])
  
file_path: pytorch/test/distributed/_tensor/test_matrix_ops.py
imports: import itertools
  from typing import cast, List, Optional
  import torch
  from torch.distributed._tensor import DeviceMesh, distribute_tensor
  from torch.distributed._tensor.api import DTensor
  from torch.distributed._tensor.placement_types import _Partial, Placement, Replicate, Shard
  from torch.testing._internal.common_utils import run_tests
  from torch.testing._internal.distributed._tensor.common_dtensor import DTensorTestBase, skip_unless_torch_gpu, with_comms
label_desc: Use image node_id 3 for calling the DistMatrixOpsTest obj's underlying member method code with example usage: obj.test_mm() without return types