docstr: reduce and scatter a tensor on a mesh dimension
docstr_tok: ["reduce","and","scatter","a","tensor","on","a","mesh","dimension"]
code: def _reduce_shard_tensor(
      self,
      tensor: torch.Tensor,
      mesh: DeviceMesh,
      reduce_op: c10d.ReduceOp.RedOpType,
      mesh_dim: int,
  ) -> torch.Tensor:
      """
      reduce and scatter a tensor on a mesh dimension
      """
      my_coordinate = mesh.get_coordinate()
      num_chunks = mesh.size(mesh_dim=mesh_dim)
  
      if my_coordinate is None:
          # if rank is not part of mesh, we simply return local_tensor,
          # which should be an empty tensor
          return tensor
  
      is_padded = tensor.size(self.dim) % num_chunks != 0
      if is_padded:
          scattered_list, pad_sizes = self._split_tensor(
              tensor, num_chunks, with_padding=True, contiguous=True
          )
          tensor = torch.cat(scattered_list, dim=self.dim)
      elif not tensor.is_contiguous():
          tensor = tensor.contiguous()
  
      output = funcol.reduce_scatter_tensor(
          tensor,
          reduce_op.name,
          scatter_dim=self.dim,
          group=(mesh, mesh_dim),
      )
  
      if is_padded:
          output = self._unpad_tensor(
              output, pad_sizes[my_coordinate[mesh_dim]]
          )
      return output
  
imports: from dataclasses import dataclass
  from typing import Any, cast, List, NamedTuple, Optional, Tuple
  import torch
  import torch.distributed._functional_collectives
  import torch.distributed.distributed_c10d
  from torch.distributed._tensor._collective_utils import mesh_broadcast, mesh_scatter
  from torch.distributed.device_mesh import DeviceMesh
label_desc: Use image node_id 6 for calling the Shard obj's underlying member method code with example usage: obj._reduce_shard_tensor(tensor, mesh, reduce_op, mesh_dim) and returns: output, tensor