code: def generate_driver(
      labels,
      features,
      device=[0, 1],
      fp16=False,
      output_from_new_proc="only_error",
  ):
      paddle_model = PaddleNormalModel_Classification_1(
          labels, features
      )
      paddle_opt = paddle.optimizer.Adam(
          parameters=paddle_model.parameters(), learning_rate=0.01
      )
      driver = PaddleFleetDriver(
          model=paddle_model,
          parallel_device=device,
          fp16=fp16,
          output_from_new_proc=output_from_new_proc,
      )
      driver.set_optimizers(paddle_opt)
      driver.setup()
  
      return driver
  
imports: import pytest
  from pathlib import Path
  from fastNLP.core.drivers.paddle_driver.fleet import PaddleFleetDriver
  from fastNLP.core.samplers import RandomSampler, UnrepeatedSampler, BucketedBatchSampler, UnrepeatedRandomSampler, UnrepeatedSequentialSampler
  from tests.helpers.models.paddle_model import PaddleNormalModel_Classification_1
  from tests.helpers.datasets.paddle_data import PaddleNormalDataset, PaddleNormalXYDataset
  from tests.helpers.utils import magic_argv_env_context, recover_logger
  from fastNLP.envs.distributed import rank_zero_rm
  from fastNLP import prepare_paddle_dataloader
  from fastNLP.core.drivers.paddle_driver.dist_utils import fastnlp_paddle_all_gather
  from fastNLP.envs.imports import _NEED_IMPORT_PADDLE
  from fastNLP import logger
label_desc: Use image node_id 1 for calling a global function with example usage: generate_driver(labels, features, device, fp16, output_from_new_proc) and returns: driver