class_docstr: Tracker for the cuda RNG states.
  
  Using the `add` method, a cuda rng state is initialized based on
  the input `seed` and is assigned to `name`. Later, by forking the
  rng state, we can perform operations and return to our starting
  cuda state.
class_docstr_tok: ["Tracker","for","the","cuda","RNG","states",".","Using","the","`","add","`","method",",","a","cuda","rng","state","is","initialized","based","on","the","input","`","seed","`","and","is","assigned","to","`","name","`",".","Later",",","by","forking","the","rng","state",",","we","can","perform","operations","and","return","to","our","starting","cuda","state","."]
docstr: Set to the initial state (no tracker).
docstr_tok: ["Set","to","the","initial","state","(","no","tracker",")","."]
code: def reset(self) -> None:
      """Set to the initial state (no tracker)."""
      self.states_ = {}
      self.seeds_ = set()
  
imports: import contextlib
  from typing import Dict, Iterator, Set, Union
  import torch
  from torch.cuda import _lazy_call
  from torch.utils.checkpoint import detach_variable
  from .initialize import get_data_parallel_rank, get_model_parallel_rank
label_desc: Use image node_id 2 for calling the CudaRNGStatesTracker obj's underlying member method code with example usage: obj.reset() without return types