code: def test_correctness(self, batch_size, model_name):
      pytest.skip("skip test for now, will fix in follow-up PR")
      model = self.get_model(model_name)
      tokenizer = self.get_tokenizer(model_name)
      prompt = self.get_prompt(batch_size)
  
      base_out = self._generate(model, tokenizer, prompt)
  
      ds_config = {
          "train_batch_size": 1,
          "fp16": {"enabled": True},
          "hybrid_engine": {"enabled": True},
      }
      model, *_ = deepspeed.initialize(model=model, config=ds_config)
  
      model.eval()
      ds1_out = self._generate(model, tokenizer, prompt)
      assert (
          base_out == ds1_out
      ), f"base_out: {base_out}, ds1_out: {ds1_out}"
  
      model.train()
      model.eval()
      ds2_out = self._generate(model, tokenizer, prompt)
      assert base_out == ds2_out
  
file_path: DeepSpeed/tests/unit/hybrid_engine/test_he_llama.py
imports: import os
  import torch
  import pytest
  import deepspeed
  from deepspeed.ops.op_builder import OpBuilder
  from unit.common import DistributedTest
  from deepspeed.accelerator import get_accelerator
  from transformers import AutoConfig, AutoTokenizer, AutoModelForCausalLM
  from deepspeed.ops.op_builder import InferenceBuilder
label_desc: Use image node_id 5 for calling the TestHybridEngineLlama obj's underlying member method code with example usage: obj.test_correctness(batch_size, model_name) without return types