code: def train_cifar(
      model,
      config,
      num_steps=400,
      average_dp_losses=True,
      fp16=True,
      seed=123,
  ):
      if required_torch_version(min_version=2.1):
          fork_kwargs = {"device_type": get_accelerator().device_name()}
      else:
          fork_kwargs = {}
      with get_accelerator().random().fork_rng(
          devices=[get_accelerator().current_device_name()],
          **fork_kwargs,
      ):
          ds_utils.set_random_seed(seed)
  
          # disable dropout
          model.eval()
  
          trainset = cifar_trainset(fp16=fp16)
          config["local_rank"] = dist.get_rank()
  
          # deepspeed_io defaults to creating a dataloader that uses a
          # multiprocessing pool. Our tests use pools and we cannot nest pools in
          # python. Therefore we're injecting this kwarg to ensure that no pools
          # are used in the dataloader.
          old_method = (
              deepspeed.runtime.engine.DeepSpeedEngine.deepspeed_io
          )
  
          def new_method(*args, **kwargs):
              kwargs["num_local_io_workers"] = 0
              return old_method(*args, **kwargs)
  
          deepspeed.runtime.engine.DeepSpeedEngine.deepspeed_io = (
              new_method
          )
  
          engine, _, _, _ = deepspeed.initialize(
              config=config,
              model=model,
              model_parameters=[p for p in model.parameters()],
              training_data=trainset,
          )
  
          losses = []
          for step in range(num_steps):
              loss = engine.train_batch()
              losses.append(loss.item())
              if step % 50 == 0 and dist.get_rank() == 0:
                  print(f"STEP={step} LOSS={loss.item()}")
  
          if average_dp_losses:
              loss_tensor = torch.tensor(losses).to(
                  get_accelerator().device_name()
              )
              dist.all_reduce(loss_tensor)
              loss_tensor /= dist.get_world_size()
              losses = loss_tensor.tolist()
  
      return losses
  
imports: import pytest
  import os
  import torch
  import torch.nn
  import torch.nn.functional
  import deepspeed
  import deepspeed.comm
  import deepspeed.runtime.utils
  from deepspeed.runtime.utils import required_torch_version
  from deepspeed.accelerator import get_accelerator
  from deepspeed.runtime.pipe.module import PipelineModule, LayerSpec
label_desc: Use image node_id 3 for calling a global function with example usage: train_cifar(model, config, num_steps, average_dp_losses, fp16, seed) and returns: losses, old_method