code: def test_split_tensor_1D(self) -> None:
      mesh = DeviceMesh(self.device_type, torch.arange(self.world_size))
      shard_placement = Shard(0)
  
      for size in range(8):
          tensor = self._create_tensor(size)
          (
              splitted_tensor_list,
              pad_sizes,
          ) = shard_placement._split_tensor(
              tensor,
              mesh.size(),
              with_padding=True,
              contiguous=True,
          )
          if size == 0:
              # when tensor size is 0, there is no padding needed for all the ranks.
              expected_pad_sizes = [0] * self.world_size
              assert_array_equal(expected_pad_sizes, pad_sizes)
  
              is_tensor_empty = [
                  False if splitted_tensor.numel() > 0 else True
                  for splitted_tensor in splitted_tensor_list
              ]
              expected_is_tensor_empty = [True] * self.world_size
              assert_array_equal(
                  expected_is_tensor_empty, is_tensor_empty
              )
          else:
              expected_pad_sizes = [
                  0 if idx < size else 1
                  for idx, _ in enumerate(range(self.world_size))
              ]
              assert_array_equal(expected_pad_sizes, pad_sizes)
  
              unpadded_list = [
                  shard_placement._unpad_tensor(tensor, pad_sizes[i])
                  if pad_sizes[i] > 0
                  else tensor
                  for i, tensor in enumerate(splitted_tensor_list)
              ]
              expected_is_tensor_empty = [
                  False if idx < size else True
                  for idx, _ in enumerate(range(self.world_size))
              ]
              is_tensor_empty = [
                  False if unpadded_tensor.numel() > 0 else True
                  for unpadded_tensor in unpadded_list
              ]
              assert_array_equal(
                  expected_is_tensor_empty, is_tensor_empty
              )
  
imports: import torch
  import torch.nn.functional
  from numpy.testing import assert_array_equal
  from torch.distributed._functional_collectives import AsyncCollectiveTensor
  from torch.distributed._tensor import DeviceMesh, distribute_tensor, DTensor, init_device_mesh
  from torch.distributed._tensor.placement_types import _Partial, Replicate, Shard
  from torch.distributed.tensor.parallel import ColwiseParallel, parallelize_module, 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 TestDTensorPlacementTypes obj's underlying member method code with example usage: obj.test_split_tensor_1D() without return types