code: def test_scatter_uneven(self):
      device_mesh = DeviceMesh(
          self.device_type, list(range(self.world_size))
      )
      my_rank = device_mesh.get_rank()
      tensor_to_split = torch.randn(
          device_mesh.size() + 3,
          device_mesh.size() + 1,
          device=self.device_type,
      )
  
      for shard_dim in range(tensor_to_split.ndim):
          shard_placement = Shard(shard_dim)
  
          tensor_to_scatter = tensor_to_split.clone()
          tensor_splitted_list = list(
              torch.chunk(
                  tensor_to_split, self.world_size, dim=shard_dim
              )
          )
          for _ in range(self.world_size - len(tensor_splitted_list)):
              tensor_splitted_list.append(
                  torch.tensor([], device=self.device_type)
              )
  
          padded_tensor_list, pad_sizes = shard_placement._split_tensor(
              tensor_to_scatter,
              device_mesh.size(),
              with_padding=True,
              contiguous=True,
          )
  
          scattered_tensor = torch.empty_like(
              padded_tensor_list[my_rank]
          )
          mesh_scatter(
              scattered_tensor,
              padded_tensor_list,
              device_mesh,
              mesh_dim=0,
          )
  
          if pad_sizes[my_rank] != 0:
              scattered_tensor = shard_placement._unpad_tensor(
                  scattered_tensor, pad_sizes[my_rank]
              )
  
          if scattered_tensor.numel() == 0:
              # We need to check numel() instead of size if a tensor is ([]) after unpadding,
              # since the size could be ([0, 8]) after unpadding.
              self.assertEqual(
                  scattered_tensor.numel(),
                  tensor_splitted_list[my_rank].numel(),
              )
          else:
              self.assertEqual(
                  scattered_tensor.size(),
                  tensor_splitted_list[my_rank].size(),
              )
              self.assertEqual(
                  scattered_tensor, tensor_splitted_list[my_rank]
              )
  
imports: import os
  import torch
  import torch.distributed._functional_collectives
  from torch.distributed._tensor import DTensor
  from torch.distributed._tensor._collective_utils import mesh_all_to_all, mesh_broadcast, mesh_scatter
  from torch.distributed._tensor.placement_types import _Partial, Shard
  from torch.distributed.device_mesh import _mesh_resources, DeviceMesh, init_device_mesh
  from torch.distributed.distributed_c10d import get_global_rank, get_world_size, init_process_group, is_initialized, is_nccl_available, ProcessGroup
  from torch.testing._internal.common_utils import run_tests
  from torch.testing._internal.distributed._tensor.common_dtensor import DTensorTestBase, with_comms
  from torch.testing._internal.distributed.fake_pg import FakeStore
label_desc: Use image node_id 4 for calling the DeviceMeshCollectiveTest obj's underlying member method code with example usage: obj.test_scatter_uneven() without return types