code: def average_state_dicts(state_dicts):
      """Produces an average state_dict from an iterator over state_dicts.
  
      Note that at one time, this keeps two of the state_dicts in memory, which
      is the minimum memory requirement.
  
      Arguments
      ---------
      state_dicts : iterator, list
          The state_dicts to average.
  
      Returns
      -------
      state_dict
          The averaged state_dict.
      """
      iterator = iter(state_dicts)
      try:
          running_sum = next(iterator)
      except StopIteration:
          raise ValueError("No state dicts to average.")
      num_dicts = 1
      with torch.no_grad():
          # First sum all state_dicts together:
          for state_dict in iterator:
              for pname, param in state_dict.items():
                  running_sum[pname] += param.data
              num_dicts += 1
          # Finally, divide by number of dicts:
          for pname, param in running_sum.items():
              running_sum[pname] = param.data / float(num_dicts)
      return running_sum
  
imports: import torch
  import collections
  import collections.abc
  import os
  import time
  import yaml
  import pathlib
  import inspect
  import shutil
  import logging
  import warnings
  from packaging import version
  import speechbrain.utils._workarounds
  from speechbrain.utils.distributed import main_process_only, if_main_process, ddp_barrier
label_desc: Use image node_id 10 for calling a global function with example usage: average_state_dicts(state_dicts) and returns: running_sum