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: Set the initial seed of all number generators.
  
  :param random_seed: The initial seed. It should be a value compatible
      with all registered number generators. A native `int` value in
      range `[0, 2^32 - 1)` is usually acceptable.
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code: def set_random_seeds(self, random_seed):
      """
      Set the initial seed of all number generators.
  
      :param random_seed: The initial seed. It should be a value compatible
          with all registered number generators. A native `int` value in
          range `[0, 2^32 - 1)` is usually acceptable.
      """
  
      for gen_name, gen_dict in self.random_generators.items():
          gen_dict["seed"](random_seed)
  
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 4 for calling the _RNGManager obj's underlying member method code with example usage: obj.set_random_seeds(random_seed) without return types