code: def test_tp_transform_with_uncovered_op(self):
      model = DummyModel().to(device=self.device_type)
      inputs = (
          torch.randn(7, 3, requires_grad=False).to(
              device=self.device_type
          ),
      )
      with torch.no_grad():
          res = model(*inputs)
      exported_program = torch._export.export(
          model,
          inputs,
          constraints=None,
      )
      tp_exported_program = tensor_parallel_transformation(
          exported_program,
          self.rank,
          self.world_size,
          self.device_type,
          {"fc": ColwiseParallel},
      )
      tp_model = tp_exported_program.module()
      with torch.no_grad():
          tp_res = tp_model(*inputs)
      self.assertEqual(res, tp_res)
      # Expect all_gather to be inserted to distributed sharded fc resutls
      self.assert_has_c10d_ops(
          tp_exported_program.graph_module,
          {
              "c10d_functional.all_gather_into_tensor.default": 1,
              "c10d_functional.wait_tensor.default": 1,
          },
      )
  
imports: from collections import defaultdict
  from typing import Dict
  import torch
  from torch.distributed._tensor.experimental.tp_transform import tensor_parallel_transformation
  from torch.distributed.tensor.parallel.style import ColwiseParallel, ParallelStyle, RowwiseParallel
  from torch.testing._internal.common_utils import run_tests
  from torch.testing._internal.distributed._tensor.common_dtensor import DTensorTestBase, with_comms
label_desc: Use image node_id 3 for calling the TensorParallelTest obj's underlying member method code with example usage: obj.test_tp_transform_with_uncovered_op() without return types