class_docstr: A class used to manage a set of deterministic random number generators.
  
  The main goal of this class is to provide a simplified mechanism to
  improve determinism on the RNG side. This class includes a method to set
  a manual seed of all number generators and a method to register new
  generators.
  
  By default, Python (`random` module), NumPy, and PyTorch global generators
  are managed.
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docstr: Initializes this object, registering the default supported generators.
docstr_tok: ["Initializes","this","object",",","registering","the","default","supported","generators","."]
code: def __init__(self):
      """
      Initializes this object, registering the default supported generators.
      """
      self.random_generators = OrderedDict()
      self._register_default_generators()
  
file_path: avalanche/avalanche/training/determinism/rng_manager.py
imports: import random
  from collections import OrderedDict
  from typing import Any, Dict, Type
  import numpy
  import torch
  from avalanche.training.determinism.cuda_rng import cuda_rng_seed, cuda_rng_save_state, cuda_rng_load_state, cuda_rng_step, cpu_rng_seed
label_desc: Use image node_id 1 to create a new _RNGManager object  with example: obj = _RNGManager()