code: def test_multi_optimizers():
      torch_model = TorchNormalModel_Classification_1(10, 10)
      torch_opt = torch.optim.Adam(
          params=torch_model.parameters(), lr=0.01
      )
      device = [torch.device(i) for i in [0, 1]]
      driver = DeepSpeedDriver(
          model=torch_model,
          parallel_device=device,
      )
      driver.set_optimizers([torch_opt, torch_opt])
      with pytest.raises(ValueError):
          driver.setup()
  
imports: import os
  from pathlib import Path
  import pytest
  from fastNLP.core.drivers.torch_driver.deepspeed import DeepSpeedDriver
  from fastNLP.core.samplers import RandomSampler, BucketedBatchSampler, UnrepeatedRandomSampler
  from tests.helpers.models.torch_model import TorchNormalModel_Classification_1
  from tests.helpers.datasets.torch_data import TorchNormalXYDataset
  from tests.helpers.utils import magic_argv_env_context
  from fastNLP.envs.distributed import rank_zero_rm
  from fastNLP import logger
  from fastNLP.envs.imports import _NEED_IMPORT_TORCH, _NEED_IMPORT_DEEPSPEED
label_desc: Use image node_id 4 for calling a global function with example usage: test_multi_optimizers() without return types