code: def test_trainer_torch_with_evaluator_fp16_accumulation_steps(
      model_and_optimizers: TrainerParameters,
      driver,
      device,
      fp16,
      accumulation_steps,
      n_epochs=6,
  ):
      callbacks = [
          RecordMetricCallback(
              monitor="acc", metric_threshold=0.1, larger_better=True
          )
      ]
      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=callbacks,
          fp16=fp16,
          accumulation_steps=accumulation_steps,
          output_from_new_proc="all",
      )
  
      trainer.run()
  
      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 3 for calling a global function with example usage: test_trainer_torch_with_evaluator_fp16_accumulation_steps(model_and_optimizers, driver, device, fp16, accumulation_steps, n_epochs) without return types