code: def test_allreduce_grid():
      dht_instances = launch_dht_instances(8)
      averagers = [
          DecentralizedAverager(
              averaged_tensors=[torch.randn(3)],
              dht=dht,
              target_group_size=2,
              prefix="mygroup",
              initial_group_bits=bin(i // 2)[2:].rjust(2, "0"),
              start=True,
          )
          for i, dht in enumerate(dht_instances)
      ]
  
      [means0], [stds0] = compute_mean_std(averagers)
      assert not torch.allclose(stds0, torch.zeros_like(stds0))
  
      prev_means, prev_stds = means0, stds0
  
      for i in range(5):
          step_futures = [
              averager.step(wait=False) for averager in averagers
          ]
          groups = [future.result() for future in step_futures]
          [means], [stds] = compute_mean_std(averagers)
          assert torch.allclose(means, prev_means, atol=1e-6, rtol=0)
          assert all(len(group) == 2 for group in groups)
  
          if i <= 2:
              assert torch.all(torch.le(stds, prev_stds))
          else:
              assert torch.allclose(
                  stds, torch.zeros_like(stds), atol=1e-6, rtol=0
              )
  
      for process in averagers + dht_instances:
          process.shutdown()
  
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 6 for calling a global function with example usage: test_allreduce_grid() without return types