code: def test_prepare_module_input_multiple_inputs(self):
      mesh = init_device_mesh(self.device_type, (self.world_size,))
  
      class TestModule(torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.linear = torch.nn.Linear(8, 8)
  
          def forward(self, x, y):
              return self.linear(x) + y
  
      # Raise assertion error if input_layouts and desired_input_layouts do not have same length.
      test_mod = TestModule().to(self.device_type)
      with self.assertRaisesRegex(
          AssertionError,
          "input_layouts and desired_input_layouts should have same length!",
      ):
          prepare_inps_dimension_mismatch = PrepareModuleInput(
              input_layouts=Shard(0),
              desired_input_layouts=(Replicate(), None),
          )
      # Raise assertion error if module inputs and input_layouts do not have same length.
      prepare_inps_short_dimension = PrepareModuleInput(
          input_layouts=Shard(0), desired_input_layouts=Replicate()
      )
      parallelize_module(test_mod.linear, mesh, ColwiseParallel())
      parallelize_module(test_mod, mesh, prepare_inps_short_dimension)
      with self.assertRaisesRegex(
          ValueError,
          "module inputs and input_layouts should have same length!",
      ):
          output = test_mod(
              torch.randn(2, 8, device=self.device_type),
              torch.ones(
                  self.world_size * 2,
                  8 // self.world_size,
                  device=self.device_type,
              ),
          )
  
      test_mod = TestModule().to(self.device_type)
      prepare_inps = PrepareModuleInput(
          input_layouts=(Shard(0), None),
          desired_input_layouts=(Replicate(), None),
      )
  
      parallelize_module(test_mod.linear, mesh, ColwiseParallel())
      parallelize_module(test_mod, mesh, prepare_inps)
      output = test_mod(
          torch.randn(2, 8, device=self.device_type),
          torch.ones(
              self.world_size * 2,
              8 // self.world_size,
              device=self.device_type,
          ),
      )
      self.assertEqual(
          output.shape, (self.world_size * 2, 8 // self.world_size)
      )
  
file_path: pytorch/test/distributed/tensor/parallel/test_tp_style.py
imports: from copy import deepcopy
  import torch
  import torch.nn
  from torch.distributed._tensor import Replicate, Shard, init_device_mesh
  from torch.distributed._tensor.debug import CommDebugMode
  from torch.distributed.tensor.parallel import parallelize_module
  from torch.distributed.tensor.parallel.style import ColwiseParallel, PrepareModuleInput, PrepareModuleOutput, RowwiseParallel
  from torch.testing._internal.common_utils import run_tests
  from torch.testing._internal.distributed._tensor.common_dtensor import DTensorTestBase, with_comms, NUM_DEVICES
label_desc: Use image node_id 5 for calling the TensorParallelStyleTest obj's underlying member method code with example usage: obj.test_prepare_module_input_multiple_inputs() and returns: unknown