code: def run(self, inputs, class_tmpdir):
      args_defaults = {
          "num_layers": 2,
          "hidden_size": 128,
          "num_attention_heads": 8,
          "max_position_embeddings": 128,
      }
  
      model = get_gpt2_model(args_defaults, mp_size=self.world_size)
      model = get_deepspeed_model(model)
  
      model.eval()
  
      with torch.no_grad():
          device_name = get_accelerator().device_name()
          baseline = model(
              inputs[0].to(device_name),
              inputs[1].to(device_name),
              inputs[2].to(device_name),
          )
          if dist.get_rank() == 0:
              save_path = os.path.join(class_tmpdir, "output.pt")
              torch.save(baseline.cpu(), save_path)
  
          state_dict = {}
          state_dict["checkpoint_version"] = get_megatron_version()
          model.save_checkpoint(class_tmpdir, client_state=state_dict)
  
imports: import os
  import torch
  import deepspeed
  import pytest
  import random
  import numpy
  import deepspeed.comm
  from deepspeed.accelerator import get_accelerator
  from unit.common import DistributedTest, DistributedFixture
  from unit.megatron_model import get_gpt2_model, get_megatron_version
  from deepspeed.runtime.utils import required_torch_version
label_desc: Use image node_id 1 for calling the baseline_mp2 obj's underlying member method code with example usage: obj.run(inputs, class_tmpdir) without return types