code: def __hash__(self) -> int:
      # We lazily cache the spec to avoid recomputing the hash upon each
      # use, where we make sure to update the hash when the `tensor_meta`
      # changes by overriding `__setattr__`. This must be lazy so that Dynamo
      # does not try to hash non-singleton `SymInt`s for the stride.
      if self._hash is None:
          self._hash = self._hash_impl()
      return self._hash
  
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 4 for calling the DTensorSpec obj's underlying member method code with example usage: obj.__hash__() and returns: self