code: def _hash_impl(self) -> int:
      # hashing and equality check for DTensorSpec are used to cache the sharding
      # propagation results. We only need to consider the mesh, placements, shape
      # dtype and stride.
      # Caveat: we need to keep this in mind and sync hash and eq if we add more
      # fields to them.
      if self.tensor_meta is not None:
          return hash(
              (
                  self.mesh,
                  self.placements,
                  self.tensor_meta.shape,
                  self.tensor_meta.stride,
                  self.tensor_meta.dtype,
              )
          )
      return hash((self.mesh, self.placements))
  
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 3 for calling the DTensorSpec obj's underlying member method code with example usage: obj._hash_impl() and returns: hash, hash