code: def test_other_dataset(self):
      import numpy as np
  
      class _DataSet:
          def __init__(self):
              pass
  
          def __getitem__(self, item):
              return np.random.randn(5), [[1, 2], [2, 3, 4]]
  
          def __len__(self):
              return 10
  
          def __getattribute__(self, item):
              return object.__getattribute__(self, item)
  
      dataset = _DataSet()
      dl = TorchDataLoader(dataset, batch_size=2, shuffle=True)
      # dl.set_inputs('data', 'labels')
      # dl.set_pad_val('labels', val=None)
      for batch in dl:
          assert batch[0].shape == torch.Size([2, 5])
          assert batch[1].shape == torch.Size([2, 2, 3])
  
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 4 for calling the TestFdl obj's underlying member method code with example usage: obj.test_other_dataset() and returns: np,  list, int, object