class_docstr: RNGStateTracker stores Random Number Generator (RNG) state (a ByteTensor object)
  in a dict, mapping from a corresponding tag to each state tensor. It also provides
  a set of convenient utility methods to help access/modify the state tensors. The most
  important interface is _distribute_region which will be used when DTensor executes
  a random op (an operator that calls RNG).
class_docstr_tok: ["RNGStateTracker","stores","Random","Number","Generator","(","RNG",")","state","(","a","ByteTensor","object",")","in","a","dict",",","mapping","from","a","corresponding","tag","to","each","state","tensor",".","It","also","provides","a","set","of","convenient","utility","methods","to","help","access\/modify","the","state","tensors",".","The","most","important","interface","is","_distribute_region","which","will","be","used","when","DTensor","executes","a","random","op","(","an","operator","that","calls","RNG",")","."]
code: def _distribute_region(self, spec: DTensorSpec):
      pass
  
imports: import contextlib
  import warnings
  from typing import Dict, List, Optional
  import torch
  import torch.distributed
  from torch import Tensor
  from torch.distributed._tensor.placement_types import DTensorSpec, Shard
  from torch.distributed.device_mesh import _get_device_handle, DeviceMesh
label_desc: Use image node_id 8 for calling the RNGStateTracker obj's underlying member method code with example usage: obj._distribute_region(spec) without return types