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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code: def __getstate__(self):
      all_rngs_state = dict()
      for rng_name, rng_def in self.random_generators.items():
          rng_state = dict()
          rng_state["current_state"] = rng_def["save_state"]()
          all_rngs_state[rng_name] = rng_state
      return all_rngs_state
  
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 6 for calling the _RNGManager obj's underlying member method code with example usage: obj.__getstate__() and returns: all_rngs_state