code: def generate_random_driver(
      labels, features, fp16=False, device="cpu"
  ):
      """
      生成driver
      """
      model = TorchNormalModel_Classification_1(labels, features)
      opt = torch.optim.Adam(params=model.parameters(), lr=0.01)
      driver = TorchSingleDriver(model, device=device, fp16=fp16)
      driver.set_optimizers(opt)
      driver.setup()
  
      return driver
  
imports: import pytest
  from pathlib import Path
  from pkg_resources import parse_version
  from fastNLP.core.drivers.torch_driver.single_device import TorchSingleDriver
  from fastNLP.core.samplers import ReproduceBatchSampler, RandomSampler
  from tests.helpers.models.torch_model import TorchNormalModel_Classification_1
  from tests.helpers.datasets.torch_data import TorchNormalDataset, TorchNormalXYDataset
  from tests.helpers.datasets.paddle_data import PaddleNormalDataset
  from tests.helpers.models.paddle_model import PaddleNormalModel_Classification_1
  from fastNLP.envs.distributed import rank_zero_rm
  from fastNLP.envs.imports import _NEED_IMPORT_PADDLE, _NEED_IMPORT_TORCH
  from fastNLP import prepare_torch_dataloader, BucketedBatchSampler
label_desc: Use image node_id 3 for calling a global function with example usage: generate_random_driver(labels, features, fp16, device) and returns: driver