code: def test_grad_averager(
      grad_averager_factory: GradientAveragerFactory,
  ):
      parameter_shape = (5, 5)
  
      dht1 = hivemind.DHT(start=True)
      model1 = nn.ParameterDict(
          {"w": nn.Parameter(torch.zeros(parameter_shape))}
      )
      averager1 = grad_averager_factory(
          model1.parameters(),
          dht=dht1,
          prefix="test",
          target_group_size=2,
          reuse_grad_buffers=False,
          start=True,
      )
  
      dht2 = hivemind.DHT(
          start=True, initial_peers=dht1.get_visible_maddrs()
      )
      model2 = nn.ParameterDict(
          {"w": nn.Parameter(torch.zeros(parameter_shape))}
      )
      averager2 = grad_averager_factory(
          model2.parameters(),
          dht=dht2,
          prefix="test",
          target_group_size=2,
          reuse_grad_buffers=True,
          start=True,
      )
  
      control1 = averager1.schedule_step(hivemind.get_dht_time() + 5)
      control2 = averager2.schedule_step(hivemind.get_dht_time() + 5)
  
      for i in range(10):
          time.sleep(0.1)
          if i % 3 == 0:
              loss1 = F.mse_loss(model1.w, torch.ones(parameter_shape))
              loss1.backward()
              averager1.accumulate_grads_(
                  batch_size=2
              )  # total: 4 times * 2 samples = 8
              model1.zero_grad()
          else:
              loss2 = F.mse_loss(model2.w, -torch.ones(parameter_shape))
              loss2.backward()
              averager2.accumulate_grads_(
                  batch_size=3
              )  # total: 6 times * 3 samples = 18
              # note: we do not call zero grad here because reuse_grad_buffers=True
  
      assert (
          control1.stage
          == control2.stage
          == AveragingStage.AWAITING_TRIGGER
      )
      peer1_samples, peer1_times, peer2_samples, peer2_times = (
          8,
          4,
          18,
          6,
      )
      assert (
          averager1.local_samples_accumulated == peer1_samples
          and averager1.local_times_accumulated == peer1_times
      )
      ref_grads1 = torch.full(
          parameter_shape,
          -2
          / np.prod(parameter_shape)
          * averager1.local_times_accumulated,
      )
      assert torch.allclose(
          next(averager1._grad_accumulators()), ref_grads1
      )
  
      assert (
          averager2.local_samples_accumulated == peer2_samples
          and averager2.local_times_accumulated == peer2_times
      )
      ref_grads2 = torch.full(
          parameter_shape,
          2
          / np.prod(parameter_shape)
          * averager2.local_times_accumulated,
      )
      assert torch.allclose(
          next(averager2._grad_accumulators()), ref_grads2
      )
  
      averager1.step(control=control1, wait=False)
      averager2.step(control=control2, wait=False)
      for step in (control1, control2):
          step.result()  # wait for all-reduce to finish
  
      peer1_weight = peer1_samples / (peer1_samples + peer2_samples)
      peer2_weight = peer2_samples / (peer1_samples + peer2_samples)
      ref_average = peer1_weight * (
          ref_grads1 / peer1_times
      ) + peer2_weight * (ref_grads2 / peer2_times)
      with averager1.use_averaged_gradients():
          assert torch.allclose(model1.w.grad, ref_average)
      with averager2.use_averaged_gradients():
          assert torch.allclose(model2.w.grad, ref_average)
  
      # after no longer use_averaged_gradients
      if (
          ZERO_GRAD_SET_TO_NONE_DEFAULT
      ):  # averager1 has reuse_grad_buffers=False
          assert model1.w.grad is None
      else:
          assert not torch.allclose(model1.w.grad, ref_average)
      assert not torch.allclose(model2.w.grad, ref_average)
  
imports: import ctypes
  import multiprocessing
  import time
  from functools import partial
  import numpy
  import pytest
  import torch
  import torch.nn
  import torch.nn.functional
  import hivemind
  from hivemind.averaging.control import AveragingStage
  from hivemind.optim.grad_averager import GradientAverager, GradientAveragerFactory
  from hivemind.optim.optimizer import Optimizer
  from hivemind.optim.power_sgd_averager import PowerSGDGradientAverager
  from hivemind.optim.progress_tracker import ProgressTracker
  from hivemind.optim.state_averager import ZERO_GRAD_SET_TO_NONE_DEFAULT, TrainingStateAverager
  from hivemind.utils.crypto import RSAPrivateKey
label_desc: Use image node_id 1 for calling a global function with example usage: test_grad_averager(grad_averager_factory) without return types