code: def test(self, model_family, model_name, task):
      # imports here to avoid import errors when pytest collects tests
      import lm_eval
      import lm_eval.models
      import lm_eval.tasks
      import lm_eval.evaluator
  
      # The bootstrap_stderr function in lm_eval.metrics uses a
      # multiprocessing Pool to increase performance. Since we use a Pool for
      # our distributed tests and cannot nest Pools, we must redefine and
      # patch this function with a version that does not use Pool.
      def no_pool_bootstrap_stderr(f, xs, iters):
          from lm_eval.metrics import _bootstrap_internal
          from lm_eval.metrics import sample_stddev
  
          res = []
          chunk_size = min(1000, iters)
          for i in range(iters // chunk_size):
              res.extend(_bootstrap_internal(f, chunk_size)((i, xs)))
          return sample_stddev(res)
  
      lm_eval.metrics.bootstrap_stderr = no_pool_bootstrap_stderr
  
      local_rank = os.getenv("LOCAL_RANK", "0")
      device = torch.device(get_accelerator().device_name(local_rank))
      dtype = torch.float
      task_dict = lm_eval.tasks.get_task_dict([task])
  
      if "gpt-j-6b" in model_name:
          dtype = torch.half
          lm = lm_eval.models.get_model(
              model_family
          ).create_from_arg_string(
              f"pretrained={model_name}", {"device": "cpu"}
          )
          setattr(
              lm,
              model_family,
              getattr(lm, model_family).half().to(device),
          )
          lm._device = device
      else:
          lm = lm_eval.models.get_model(
              model_family
          ).create_from_arg_string(
              f"pretrained={model_name}",
              {"device": get_accelerator().device_name()},
          )
  
      get_accelerator().synchronize()
      start = time.time()
      bs_output = lm_eval.evaluator.evaluate(lm=lm, task_dict=task_dict)
      get_accelerator().synchronize()
      bs_time = time.time() - start
  
      getattr(lm, model_family).to("cpu")
      ds_model = deepspeed.init_inference(
          getattr(lm, model_family),
          mp_size=1,
          dtype=dtype,
          replace_with_kernel_inject=True,
          enable_cuda_graph=False,
      )
      check_injection(ds_model)
      setattr(lm, model_family, ds_model)
      get_accelerator().synchronize()
      start = time.time()
      ds_output = lm_eval.evaluator.evaluate(lm=lm, task_dict=task_dict)
      get_accelerator().synchronize()
      ds_time = time.time() - start
  
      ppl_diff = abs(
          bs_output["results"][task]["ppl"]
          - ds_output["results"][task]["ppl"]
      )
      # assert ds_time <= bs_time
      assert ppl_diff < 0.01
  
imports: import os
  import time
  import torch
  import pytest
  import itertools
  import deepspeed
  from deepspeed.git_version_info import torch_info
  from unit.common import DistributedTest
  from packaging import version
  from deepspeed.ops.op_builder import OpBuilder
  from transformers import pipeline, AutoTokenizer
  from transformers.models.t5.modeling_t5 import T5Block
  from transformers.models.roberta.modeling_roberta import RobertaLayer
  from huggingface_hub import HfApi
  from deepspeed.model_implementations import DeepSpeedTransformerInference
  from torch import nn
  from deepspeed.accelerator import get_accelerator
  from deepspeed.ops.op_builder import InferenceBuilder
label_desc: Use image node_id 1 for calling the TestLMCorrectness obj's underlying member method code with example usage: obj.test(model_family, model_name, task) and returns: sample_stddev