code: def test_trainer_torch_without_evaluator_accumulation_steps(
      model_and_optimizers: TrainerParameters,
      driver,
      device,
      accumulation_steps,
      n_epochs=2,
  ):
      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=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,
          callbacks=[
              RecordAccumulationStepsCallback_Torch(accumulation_steps)
          ],
          accumulation_steps=accumulation_steps,
      )
  
      trainer.run()
  
      if dist.is_initialized():
          dist.destroy_process_group()
  
imports: import os.path
  import subprocess
  import sys
  import pytest
  from dataclasses import dataclass
  from typing import Any
  from pathlib import Path
  from fastNLP.core.controllers.trainer import Trainer
  from tests.helpers.models.torch_model import TorchNormalModel_Classification_1, TorchNormalModel_Classification_3
  from tests.helpers.datasets.torch_data import TorchNormalDataset_Classification
  from tests.helpers.callbacks.helper_callbacks import RecordLossCallback
  from tests.helpers.callbacks.helper_callbacks_torch import RecordAccumulationStepsCallback_Torch
  from tests.helpers.utils import magic_argv_env_context, Capturing
  from fastNLP.envs.distributed import rank_zero_rm
  from fastNLP.envs.imports import _NEED_IMPORT_TORCH, _TORCH_GREATER_EQUAL_1_12
label_desc: Use image node_id 4 for calling a global function with example usage: test_trainer_torch_without_evaluator_accumulation_steps(model_and_optimizers, driver, device, accumulation_steps, n_epochs) without return types