code: def benchmark_optimizer(args: TrainingArguments):
      random.seed(args.seed)
      torch.manual_seed(args.seed)
      torch.set_num_threads(1)
  
      dht = hivemind.DHT(start=True)
  
      train_dataset = args.make_dataset()
      num_features = train_dataset.data[0].numel()
      num_classes = len(train_dataset.classes)
      X_train = torch.as_tensor(train_dataset.data, dtype=torch.float32)
      X_train = (
          X_train.sub_(X_train.mean((0, 1, 2)))
          .div_(X_train.std((0, 1, 2)))
          .reshape((-1, num_features))
      )
      y_train = torch.as_tensor(
          train_dataset.targets, dtype=torch.int64
      )
      del train_dataset
  
      def run_trainer(
          batch_size: int,
          batch_time: float,
          client_mode: bool,
          verbose: bool,
      ):
          model = args.make_model(num_features, num_classes).to(
              args.device
          )
  
          assert isinstance(
              model, torch.nn.Module
          ), "model_arch must evaluate to a pytorch module"
  
          optimizer = Optimizer(
              run_id=args.run_id,
              target_batch_size=args.target_batch_size,
              batch_size_per_step=batch_size,
              params=model.parameters(),
              optimizer=partial(torch.optim.SGD, lr=args.lr_base),
              scheduler=partial(
                  torch.optim.lr_scheduler.StepLR,
                  gamma=args.lr_gamma,
                  step_size=args.lr_step_size,
              ),
              dht=hivemind.DHT(
                  initial_peers=dht.get_visible_maddrs(),
                  client_mode=client_mode,
                  start=True,
              ),
              tracker_opts=dict(
                  private_key=RSAPrivateKey(),
                  max_refresh_period=args.max_refresh_period,
              ),
              matchmaking_time=args.matchmaking_time,
              averaging_timeout=args.averaging_timeout,
              reuse_grad_buffers=args.reuse_grad_buffers,
              delay_grad_averaging=args.delay_grad_averaging,
              delay_optimizer_step=args.delay_optimizer_step,
              average_state_every=args.average_state_every,
              client_mode=client_mode,
              verbose=verbose,
          )
  
          if args.use_amp and args.reuse_grad_buffers:
              grad_scaler = hivemind.GradScaler()
          else:
              # check that hivemind.Optimizer supports regular PyTorch grad scaler as well
              grad_scaler = torch.cuda.amp.GradScaler(
                  enabled=args.use_amp
              )
  
          prev_time = time.perf_counter()
  
          while optimizer.local_epoch < args.max_epoch:
              time.sleep(
                  max(
                      0.0,
                      prev_time
                      + random.gauss(batch_time, args.batch_time_std)
                      - time.perf_counter(),
                  )
              )
  
              batch = torch.randint(0, len(X_train), (batch_size,))
  
              with torch.cuda.amp.autocast() if args.use_amp else nullcontext():
                  loss = F.cross_entropy(
                      model(X_train[batch].to(args.device)),
                      y_train[batch].to(args.device),
                  )
                  grad_scaler.scale(loss).backward()
  
              grad_scaler.unscale_(optimizer)
  
              if args.use_amp:
                  grad_scaler.step(optimizer)
              else:
                  optimizer.step()
  
              grad_scaler.update()
  
              if not args.reuse_grad_buffers:
                  optimizer.zero_grad()
  
              prev_time = time.perf_counter()
  
          time.sleep(args.winddown_time)
          optimizer.shutdown()
  
      peers = []
  
      for index in range(args.num_peers):
          batch_size = random.randint(
              args.batch_size_min, args.batch_size_max
          )
          batch_time = random.uniform(
              args.batch_time_min, args.batch_time_max
          )
          peers.append(
              mp.Process(
                  target=run_trainer,
                  name=f"trainer-{index}",
                  daemon=False,
                  kwargs=dict(
                      batch_size=batch_size,
                      batch_time=batch_time,
                      client_mode=(
                          index >= args.num_peers - args.num_clients
                      ),
                      verbose=args.verbose and (index == 0),
                  ),
              )
          )
  
      try:
          for peer in peers[1:]:
              peer.start()
          peers[0].run()
          for peer in peers[1:]:
              peer.join()
      finally:
          for peer in peers[1:]:
              peer.kill()
  
file_path: hivemind/benchmarks/benchmark_optimizer.py
imports: import multiprocessing
  import random
  import time
  from contextlib import nullcontext
  from dataclasses import dataclass
  from functools import partial
  from typing import Callable
  import torch
  import torchvision
  from torch import nn
  from torch.nn import functional
  from torch.utils.data import Dataset
  import hivemind
  from hivemind.optim.optimizer import Optimizer
  from hivemind.utils.crypto import RSAPrivateKey
label_desc: Use image node_id 1 for calling a global function with example usage: benchmark_optimizer(args) without return types