class_docstr: This class is for testing via an external script - can do multiple gpus
class_docstr_tok: ["This","class","is","for","testing","via","an","external","script","-","can","do","multiple","gpus"]
code: def test_do_eval_no_train(self):
      # testing only zero3 since zero2 makes no sense with inference
      self.run_and_check(
          stage=ZERO3,
          dtype=FP16,
          eval_steps=1,
          distributed=False,
          do_train=False,
          do_eval=True,
      )
  
imports: import dataclasses
  import io
  import itertools
  import json
  import os
  import unittest
  from copy import deepcopy
  import datasets
  from parameterized import parameterized
  from tests.trainer.test_trainer import TrainerIntegrationCommon
  from transformers import AutoModel, TrainingArguments, is_torch_available, logging
  from transformers.deepspeed import HfDeepSpeedConfig, is_deepspeed_available, unset_hf_deepspeed_config
  from transformers.testing_utils import CaptureLogger, CaptureStd, CaptureStderr, LoggingLevel, TestCasePlus, execute_subprocess_async, get_gpu_count, mockenv_context, require_deepspeed, require_optuna, require_torch_gpu, require_torch_multi_gpu, slow
  from transformers.trainer_utils import get_last_checkpoint, set_seed
  from transformers.utils import WEIGHTS_NAME, is_torch_bf16_gpu_available
label_desc: Use image node_id 2 for calling the TestDeepSpeedWithLauncher obj's underlying member method code with example usage: obj.test_do_eval_no_train() without return types