code: def test_trainer_specific_params_2(
      model_and_optimizers: TrainerParameters,
      driver,
      device,
      n_epochs=2,
  ):
      """
      测试一些特殊的参数是否能够正确地传递；
      """
      trainer = Trainer(
          model=model_and_optimizers.model,
          driver=driver,
          device=device,
          optimizers=model_and_optimizers.optimizers,
          train_dataloader=model_and_optimizers.train_dataloader,
          evaluate_dataloaders={
              "dl": model_and_optimizers.evaluate_dataloaders
          },
          input_mapping=model_and_optimizers.input_mapping,
          output_mapping=model_and_optimizers.output_mapping,
          metrics=model_and_optimizers.metrics,
          n_epochs=n_epochs,
          output_from_new_proc="all",
          evaluate_every=-1,
          model_wo_auto_param_call=True,
          torch_kwargs={
              "ddp_kwargs": {
                  "broadcast_buffers": True,
                  "find_unused_parameters": True,
              },
              "non_blocking": False,
          },
      )
  
      assert trainer.driver.non_blocking is False
      assert trainer.driver.wo_auto_param_call is True
      assert trainer.driver.output_from_new_proc == "all"
  
      _ddp_kwargs = trainer.driver._fsdp_kwargs
      assert _ddp_kwargs.get("broadcast_buffers") is True
      assert _ddp_kwargs.get("find_unused_parameters") is True
  
      if dist.is_initialized():
          dist.destroy_process_group()
  
imports: import pytest
  from dataclasses import dataclass
  from typing import Any
  from fastNLP.core.controllers.trainer import Trainer
  from fastNLP.core.samplers import BucketedBatchSampler, RandomSampler
  from tests.helpers.models.torch_model import TorchNormalModel_Classification_1
  from tests.helpers.datasets.torch_data import TorchNormalDataset_Classification, TorchArgMaxDataset
  from tests.helpers.callbacks.helper_callbacks import RecordLossCallback, RecordMetricCallback
  from tests.helpers.utils import magic_argv_env_context
  from fastNLP.envs.imports import _NEED_IMPORT_TORCH
label_desc: Use image node_id 7 for calling a global function with example usage: test_trainer_specific_params_2(model_and_optimizers, driver, device, n_epochs) without return types