code: def compute_mean_std(averagers, unbiased=True):
      results = []
      for averager in averagers:
          with averager.get_tensors() as tensors:
              results.append([tensor.clone() for tensor in tensors])
  
      results_stacked_per_tensor = list(map(torch.stack, zip(*results)))
      means = [
          stack.mean(dim=0) for stack in results_stacked_per_tensor
      ]
      stds = [
          stack.std(dim=0, unbiased=unbiased)
          for stack in results_stacked_per_tensor
      ]
      return means, stds
  
imports: import random
  import time
  import numpy
  import pytest
  import torch
  import hivemind
  from hivemind.averaging import DecentralizedAverager
  from hivemind.averaging.allreduce import AveragingMode
  from hivemind.averaging.control import AveragingStage
  from hivemind.averaging.key_manager import GroupKeyManager
  from hivemind.averaging.load_balancing import load_balance_peers
  from hivemind.averaging.partition import AllreduceException
  from hivemind.p2p import PeerID
  from test_utils.dht_swarms import launch_dht_instances
label_desc: Use image node_id 5 for calling a global function with example usage: compute_mean_std(averagers, unbiased) and returns: means,  stds