code: def generate_random_driver(
      labels, features, fp16=False, device="cpu", lr=0.01
  ):
      """
      生成driver
      """
      model = JittorNormalModel_Classification_1(labels, features)
      opt = jt.optim.Adam(params=model.parameters(), lr=lr)
      driver = JittorSingleDriver(model, device=device, fp16=fp16)
      driver.set_optimizers(opt)
      driver.setup()
  
      return driver
  
imports: import pytest
  from copy import deepcopy
  from pathlib import Path
  from fastNLP.core.drivers.jittor_driver import JittorSingleDriver
  from fastNLP.core.samplers import ReproduceBatchSampler, RandomSampler
  from fastNLP.core.dataloaders import JittorDataLoader
  from tests.helpers.models.jittor_model import JittorNormalModel_Classification_1
  from tests.helpers.datasets.jittor_data import JittorNormalDataset, JittorNormalXYDataset
  from tests.helpers.datasets.torch_data import TorchNormalDataset
  from tests.helpers.models.torch_model import TorchNormalModel_Classification_1
  from fastNLP.envs.distributed import rank_zero_rm
  from fastNLP.envs.imports import _NEED_IMPORT_JITTOR, _NEED_IMPORT_TORCH
label_desc: Use image node_id 2 for calling a global function with example usage: generate_random_driver(labels, features, fp16, device, lr) and returns: driver