code: def train_cifar(
      model,
      config,
      num_steps=400,
      average_dp_losses=True,
      fp16=True,
      seed=123,
  ):
      with torch.random.fork_rng(devices=[torch.cuda.current_device()]):
          ds_utils.set_random_seed(seed)
  
          # disable dropout
          model.eval()
  
          trainset = cifar_trainset(fp16=fp16)
          config["local_rank"] = dist.get_rank()
  
          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).cuda()
              dist.all_reduce(loss_tensor)
              loss_tensor /= dist.get_world_size()
              losses = loss_tensor.tolist()
  
      return losses
  
imports: import pytest
  import torch
  import torch.nn
  import torch.nn.functional
  import deepspeed
  import deepspeed.comm
  import deepspeed.runtime.utils
  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