code: def test_customized_batch_sampler_dataloader(inherit):
      try:
          logger.set_stdout("raw", level="info")
          # 需要检验一下 set_dist_repro_dataloader 是否可以在定制 batch_sampler 的情况下正确运行
          num_samples = 10
          dataset = TorchNormalXYDataset(num_samples)
          if inherit:
  
              class BatchSampler(torch.utils.data.BatchSampler):
                  def __init__(self, dataset, batch_size):
                      self.dataset = dataset
                      self.batch_size = batch_size
  
                  def __iter__(self):
                      indices = list(range(len(dataset)))
                      for i in range(len(self)):
                          start = i * self.batch_size
                          end = (i + 1) * self.batch_size
                          return indices[start:end]
  
                  def __len__(self):
                      return (
                          len(self.dataset) + self.batch_size - 1
                      ) // self.batch_size
  
          else:
  
              class BatchSampler:
                  def __init__(self, dataset, batch_size):
                      self.dataset = dataset
                      self.batch_size = batch_size
  
                  def __iter__(self):
                      indices = list(range(len(dataset)))
                      for i in range(len(self)):
                          start = i * self.batch_size
                          end = (i + 1) * self.batch_size
                          return indices[start:end]
  
                  def __len__(self):
                      return (
                          len(self.dataset) + self.batch_size - 1
                      ) // self.batch_size
  
          dl = prepare_torch_dataloader(
              dataset, batch_sampler=BatchSampler(dataset, batch_size=4)
          )
          model = TorchNormalModel_Classification_1(10, 32)
          device = [torch.device(i) for i in [0, 1]]
          driver = TorchDDPDriver(model, parallel_device=device)
          driver.setup()
          # TODO 这里需要raise
          with pytest.raises(TypeError):
              dl = driver.set_dist_repro_dataloader(
                  dataloader=dl, dist="dist", reproducible=False
              )
      finally:
          if dist.is_initialized():
              dist.barrier()
              dist.destroy_process_group()
  
imports: import os
  import pytest
  from pathlib import Path
  from fastNLP.core.drivers.torch_driver.ddp import TorchDDPDriver
  from fastNLP import prepare_torch_dataloader
  from fastNLP.core.samplers import RandomSampler, UnrepeatedSampler, BucketedBatchSampler, UnrepeatedRandomSampler, UnrepeatedSequentialSampler
  from tests.helpers.models.torch_model import TorchNormalModel_Classification_1
  from tests.helpers.datasets.torch_data import TorchNormalDataset, TorchNormalXYDataset
  from tests.helpers.utils import magic_argv_env_context, recover_logger, Capturing
  from fastNLP.envs.distributed import rank_zero_rm
  from fastNLP import logger
  from fastNLP.core.drivers.torch_driver.dist_utils import fastnlp_torch_all_gather
  from fastNLP.envs.imports import _NEED_IMPORT_TORCH
label_desc: Use image node_id 7 for calling a global function with example usage: test_customized_batch_sampler_dataloader(inherit) and returns: unknown, unknown, indices, indices