code: def test_from_local_uneven_sharding(self):
      mesh_shape = (self.world_size,)
      device_mesh = init_device_mesh(self.device_type, mesh_shape)
  
      uneven_dim0_size = self.world_size + 1
      global_tensor = torch.randn(uneven_dim0_size, 2)
      shard_placement = Shard(0)
      tensor_list, _ = shard_placement._split_tensor(
          global_tensor,
          device_mesh.size(mesh_dim=0),
          with_padding=False,
          contiguous=True,
      )
  
      dtensor = DTensor.from_local(
          tensor_list[self.rank],
          device_mesh,
          (Shard(0),),
          shape=global_tensor.size(),
          stride=global_tensor.stride(),
      )
  
      self.assertEqual(dtensor.size(), global_tensor.size())
      self.assertEqual(dtensor.stride(), global_tensor.stride())
  
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 6 for calling the DTensorTest obj's underlying member method code with example usage: obj.test_from_local_uneven_sharding() without return types