code: def _test_optimizer(
      num_peers: int = 1,
      num_clients: int = 0,
      target_batch_size: int = 32,
      total_epochs: int = 3,
      use_local_updates: bool = False,
      reuse_grad_buffers: bool = True,
      delay_state_averaging: bool = True,
      delay_grad_averaging: bool = True,
      delay_optimizer_step: bool = True,
      average_state_every: int = 1,
  ):
      dht = hivemind.DHT(start=True)
  
      features = torch.randn(100, 5)
      targets = features @ torch.randn(5, 1)
      optimizer = None
      total_samples_accumulated = mp.Value(ctypes.c_int32, 0)
  
      def run_trainer(
          batch_size: int, batch_time: float, client_mode: bool
      ):
          nonlocal optimizer
          model = nn.Linear(5, 1)
  
          assert isinstance(
              model, torch.nn.Module
          ), "model_arch must evaluate to a pytorch module"
  
          optimizer = Optimizer(
              run_id="test_run",
              target_batch_size=target_batch_size,
              batch_size_per_step=batch_size,
              params=model.parameters(),
              optimizer=partial(torch.optim.SGD, lr=0.1),
              scheduler=partial(
                  torch.optim.lr_scheduler.StepLR,
                  gamma=0.5,
                  step_size=1,
              ),
              dht=hivemind.DHT(
                  initial_peers=dht.get_visible_maddrs(),
                  client_mode=client_mode,
                  start=True,
              ),
              tracker_opts=dict(
                  private_key=RSAPrivateKey(), max_refresh_period=1.0
              ),
              averager_opts=dict(request_timeout=0.5),
              use_local_updates=use_local_updates,
              matchmaking_time=1.0,
              averaging_timeout=5.0,
              reuse_grad_buffers=reuse_grad_buffers,
              delay_state_averaging=delay_state_averaging,
              delay_grad_averaging=delay_grad_averaging,
              delay_optimizer_step=delay_optimizer_step,
              average_state_every=average_state_every,
              client_mode=client_mode,
              verbose=False,
          )
          optimizer.load_state_from_peers()
  
          prev_time = time.perf_counter()
  
          while optimizer.local_epoch < total_epochs:
              time.sleep(
                  max(0.0, prev_time + batch_time - time.perf_counter())
              )
              batch = torch.randint(0, len(features), (batch_size,))
  
              loss = F.mse_loss(model(features[batch]), targets[batch])
              loss.backward()
  
              optimizer.step()
  
              total_samples_accumulated.value += batch_size
  
              if not reuse_grad_buffers:
                  optimizer.zero_grad()
  
              prev_time = time.perf_counter()
  
          time.sleep(1.0)
          optimizer.shutdown()
          return optimizer
  
      peers = []
  
      for index in range(num_peers):
          peers.append(
              mp.Process(
                  target=run_trainer,
                  name=f"trainer-{index}",
                  kwargs=dict(
                      batch_size=4 + index,
                      batch_time=0.3 + 0.2 * index,
                      client_mode=(index >= num_peers - num_clients),
                  ),
              )
          )
  
      for peer in peers[1:]:
          peer.start()
      peers[0].run()
      for peer in peers[1:]:
          peer.join()
  
      assert isinstance(optimizer, Optimizer)
      assert (
          optimizer.local_epoch
          == optimizer.tracker.global_epoch
          == total_epochs
      )
      expected_samples_accumulated = target_batch_size * total_epochs
      assert (
          expected_samples_accumulated
          <= total_samples_accumulated.value
          <= expected_samples_accumulated * 1.2
      )
      assert (
          4 / 0.3 * 0.8
          <= optimizer.tracker.performance_ema.samples_per_second
          <= 4 / 0.3 * 1.2
      )
  
      assert not optimizer.state_averager.is_alive()
      assert not optimizer.tracker.is_alive()
      if not use_local_updates:
          assert not optimizer.grad_averager.is_alive()
      else:
          assert optimizer.grad_averager is None
  
      assert (
          optimizer.scheduled_grads is None
          or optimizer.scheduled_grads.done()
      )
  
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 7 for calling a global function with example usage: _test_optimizer(num_peers, num_clients, target_batch_size, total_epochs, use_local_updates, reuse_grad_buffers, delay_state_averaging, delay_grad_averaging, delay_optimizer_step, average_state_every) and returns: optimizer