code: def test_get_backend(self):
      from fastNLP.core.collators import Collator
      from torch.utils.data import DataLoader, Dataset
  
      class MyDatset(DataSet):
          def __len__(self):
              return 1000
  
          def __getitem__(self, item):
              return [[1, 0], [1], [1, 2, 4]], [1, 0]
  
      collate_batch = Collator(backend="auto")
      dl = DataLoader(MyDatset(), collate_fn=collate_batch)
      for batch in dl:
          print(batch)
  
file_path: fastNLP/tests/core/dataloaders/torch_dataloader/test_fdl.py
imports: import pytest
  from fastNLP.core.dataloaders.torch_dataloader import TorchDataLoader, prepare_torch_dataloader
  from fastNLP.core.dataset import DataSet
  from fastNLP.io.data_bundle import DataBundle
  from fastNLP.envs.imports import _NEED_IMPORT_TORCH
  from pkg_resources import parse_version
  from tests.helpers.utils import Capturing, recover_logger
  from fastNLP import logger
  import numpy
label_desc: Use image node_id 8 for calling the TestFdl obj's underlying member method code with example usage: obj.test_get_backend() and returns: int, list,  list