code: def _run_finetune(
      self, gpus: int, distributed_retriever: str = "pytorch"
  ):
      tmp_dir = self.get_auto_remove_tmp_dir()
      output_dir = os.path.join(tmp_dir, "output")
      data_dir = os.path.join(tmp_dir, "data")
      self._create_dummy_data(data_dir=data_dir)
  
      testargs = f"""
                  --data_dir {data_dir} \
                  --output_dir {output_dir} \
                  --model_name_or_path facebook/rag-sequence-base \
                  --model_type rag_sequence \
                  --do_train \
                  --do_predict \
                  --n_val -1 \
                  --val_check_interval 1.0 \
                  --train_batch_size 2 \
                  --eval_batch_size 1 \
                  --max_source_length 25 \
                  --max_target_length 25 \
                  --val_max_target_length 25 \
                  --test_max_target_length 25 \
                  --label_smoothing 0.1 \
                  --dropout 0.1 \
                  --attention_dropout 0.1 \
                  --weight_decay 0.001 \
                  --adam_epsilon 1e-08 \
                  --max_grad_norm 0.1 \
                  --lr_scheduler polynomial \
                  --learning_rate 3e-04 \
                  --num_train_epochs 1 \
                  --warmup_steps 4 \
                  --gradient_accumulation_steps 1 \
                  --distributed-port 8787 \
                  --use_dummy_dataset 1 \
                  --distributed_retriever {distributed_retriever} \
              """.split()
  
      if gpus > 0:
          testargs.append(f"--gpus={gpus}")
          if is_apex_available():
              testargs.append("--fp16")
      else:
          testargs.append("--gpus=0")
          testargs.append("--distributed_backend=ddp_cpu")
          testargs.append("--num_processes=2")
  
      cmd = [
          sys.executable,
          str(Path(finetune_rag.__file__).resolve()),
      ] + testargs
      execute_subprocess_async(cmd, env=self.get_env())
  
      metrics_save_path = os.path.join(output_dir, "metrics.json")
      with open(metrics_save_path) as f:
          result = json.load(f)
      return result
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/examples/research_projects/rag/_test_finetune_rag.py
imports: import json
  import logging
  import os
  import sys
  from pathlib import Path
  import finetune_rag
  from transformers.file_utils import is_apex_available
  from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_ray, require_torch_gpu, require_torch_multi_gpu
label_desc: Use image node_id 2 for calling the RagFinetuneExampleTests obj's underlying member method code with example usage: obj._run_finetune(gpus, distributed_retriever) and returns: result