code: def generate_random_driver(
      labels, features, fp16=False, device="cpu"
  ):
      """
      生成driver
      """
      model = PaddleNormalModel_Classification_1(labels, features)
      opt = paddle.optimizer.Adam(
          parameters=model.parameters(), learning_rate=0.01
      )
      driver = PaddleSingleDriver(model, device=device, fp16=fp16)
      driver.set_optimizers(opt)
      driver.setup()
  
      return driver
  
imports: import pytest
  from pathlib import Path
  from fastNLP.core.drivers.paddle_driver.single_device import PaddleSingleDriver
  from fastNLP.core.samplers import ReproduceBatchSampler, RandomSampler
  from tests.helpers.models.paddle_model import PaddleNormalModel_Classification_1
  from tests.helpers.datasets.paddle_data import PaddleNormalDataset, PaddleNormalXYDataset
  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_PADDLE, _NEED_IMPORT_TORCH
  from fastNLP import prepare_paddle_dataloader, BucketedBatchSampler
label_desc: Use image node_id 1 for calling a global function with example usage: generate_random_driver(labels, features, fp16, device) and returns: driver