code: def cuda_rng_load_state(rng_state):
      """
      Loads the state of cuda RNGs.
  
      :param rng_state: The list of RNG states (one per cuda device).
      """
      n_devices = torch.cuda.device_count()
      n_states = len(rng_state)
      if n_states < n_devices:
          warnings.warn(
              "Problem when reloading the state of torch.cuda RNGs: the given"
              "checkpoint contain a number of RNG states less than the "
              "number of currently available cuda devices "
              f"(got {n_states}, expected {n_devices}). "
              f"The RNG of cuda devices with ID >= {n_states} will not be "
              f"initialized!"
          )
  
      # The standard situation is n_devices == n_states: just re-load the state
      # of the RNGs of currently available GPUs. This reasoning also applies if
      # n_states > n_devices. However, this is a bad fit for n_states < n_devices
      # (and because of this, we show the warning above).
      for device_id, rng_state in enumerate(rng_state[:n_devices]):
          torch.cuda.set_rng_state(rng_state, f"cuda:{device_id}")
  
file_path: avalanche/avalanche/training/determinism/cuda_rng.py
imports: import warnings
  import torch
  from torch import default_generator
label_desc: Use image node_id 4 for calling a global function with example usage: cuda_rng_load_state(rng_state) without return types