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_fp32_non_distributed(self, stage, dtype):
      # real model needs too much GPU memory under stage2+fp32, so using tiny random model here -
      # therefore no quality checks, just basic completion checks are done
      self.run_and_check(
          stage=stage,
          dtype=dtype,
          model_name=T5_TINY,
          distributed=False,
          do_train=True,
          do_eval=True,
          quality_checks=False,
          fp32=True,
      )
  
imports: import dataclasses
  import io
  import itertools
  import json
  import os
  import unittest
  from copy import deepcopy
  from functools import partial
  import datasets
  from parameterized import parameterized
  import tests.trainer.test_trainer
  from tests.trainer.test_trainer import TrainerIntegrationCommon
  from transformers import AutoModel, TrainingArguments, is_torch_available, logging
  from transformers.integrations.deepspeed import HfDeepSpeedConfig, is_deepspeed_available, unset_hf_deepspeed_config
  from transformers.testing_utils import CaptureLogger, CaptureStd, CaptureStderr, LoggingLevel, TestCasePlus, backend_device_count, execute_subprocess_async, mockenv_context, require_deepspeed, require_optuna, require_torch_accelerator, require_torch_multi_accelerator, slow, torch_device
  from transformers.trainer_utils import get_last_checkpoint, set_seed
  from transformers.utils import SAFE_WEIGHTS_NAME, is_torch_bf16_available_on_device
label_desc: Use image node_id 3 for calling the TestDeepSpeedWithLauncher obj's underlying member method code with example usage: obj.test_fp32_non_distributed(stage, dtype) without return types