code: def test_trainer_w_evaluator_overfit_torch(
      model_and_optimizers: TrainerParameters,
      driver,
      device,
      overfit_batches,
      num_train_batch_per_epoch,
  ):
      """
      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,
          overfit_batches=overfit_batches,
          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=2,
          output_from_new_proc="all",
          evaluate_every=-1,
          torch_kwargs={
              "non_blocking": False,
              "set_grad_to_none": True,
          },
      )
  
      trainer.run(num_train_batch_per_epoch=num_train_batch_per_epoch)
  
      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 8 for calling a global function with example usage: test_trainer_w_evaluator_overfit_torch(model_and_optimizers, driver, device, overfit_batches, num_train_batch_per_epoch) without return types