docstr: 测试 set_dist_repro_dataloader 中 dist 为 None、reproducible 为 True 时的表现
  当用户在 driver 之外初始化了分布式环境时，fastnlp 不支持进行断点重训，此时应该报错
docstr_tok: ["\u6d4b\u8bd5","set_dist_repro_dataloader","\u4e2d","dist","\u4e3a","None\u3001reproducible","\u4e3a","True","\u65f6\u7684\u8868\u73b0","\u5f53\u7528\u6237\u5728","driver","\u4e4b\u5916\u521d\u59cb\u5316\u4e86\u5206\u5e03\u5f0f\u73af\u5883\u65f6\uff0cfastnlp","\u4e0d\u652f\u6301\u8fdb\u884c\u65ad\u70b9\u91cd\u8bad\uff0c\u6b64\u65f6\u5e94\u8be5\u62a5\u9519"]
code: def test_with_dist_none_reproducible_true(self):
      """
      测试 set_dist_repro_dataloader 中 dist 为 None、reproducible 为 True 时的表现
      当用户在 driver 之外初始化了分布式环境时，fastnlp 不支持进行断点重训，此时应该报错
      """
      driver = generate_driver(10, 10, device=self.device)
      dataloader = DataLoader(self.dataset, batch_size=4, shuffle=True)
      with pytest.raises(RuntimeError):
          # 应当抛出 RuntimeError
          replaced_loader = driver.set_dist_repro_dataloader(
              dataloader, None, True
          )
  
      comm.barrier()
  
imports: import os
  import sys
  import pytest
  from pathlib import Path
  from fastNLP.core.drivers.oneflow_driver.ddp import OneflowDDPDriver
  from fastNLP import prepare_oneflow_dataloader
  from fastNLP.core.samplers import RandomSampler, UnrepeatedSampler, BucketedBatchSampler, UnrepeatedRandomSampler, UnrepeatedSequentialSampler
  from tests.helpers.models.oneflow_model import OneflowNormalModel_Classification_1
  from tests.helpers.datasets.oneflow_data import OneflowNormalDataset, OneflowNormalXYDataset
  from tests.helpers.utils import recover_logger
  from fastNLP.envs.distributed import rank_zero_rm
  from fastNLP import logger
  from fastNLP.core.drivers.oneflow_driver.dist_utils import fastnlp_oneflow_all_gather
  from fastNLP.envs.imports import _NEED_IMPORT_ONEFLOW
label_desc: Use image node_id 5 for calling the TestSetDistReproDataloader obj's underlying member method code with example usage: obj.test_with_dist_none_reproducible_true() without return types