code: def save_prefixed_metrics(
      results,
      output_dir,
      file_name: str = "all_results.json",
      metric_key_prefix: str = "eval",
  ):
      """
      Save results while prefixing metric names.
  
      Args:
          results: (:obj:`dict`):
              A dictionary of results.
          output_dir: (:obj:`str`):
              An output directory.
          file_name: (:obj:`str`, `optional`, defaults to :obj:`all_results.json`):
              An output file name.
          metric_key_prefix: (:obj:`str`, `optional`, defaults to :obj:`eval`):
              A metric name prefix.
      """
      # Prefix all keys with metric_key_prefix + '_'
      for key in list(results.keys()):
          if not key.startswith(f"{metric_key_prefix}_"):
              results[f"{metric_key_prefix}_{key}"] = results.pop(key)
  
      with open(os.path.join(output_dir, file_name), "w") as f:
          json.dump(results, f, indent=4)
  
imports: import argparse
  import json
  import logging
  import math
  import os
  import random
  from pathlib import Path
  import datasets
  import numpy
  import torch
  from datasets import load_dataset
  from torch.utils.data import DataLoader
  from tqdm.auto import tqdm
  import evaluate
  import transformers
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  from accelerate.utils import set_seed
  from huggingface_hub import Repository
  from transformers import CONFIG_MAPPING, MODEL_MAPPING, AutoConfig, AutoModelForQuestionAnswering, AutoTokenizer, DataCollatorWithPadding, EvalPrediction, SchedulerType, default_data_collator, get_scheduler
  from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
  from transformers.utils.versions import require_version
  from utils_qa import postprocess_qa_predictions
label_desc: Use image node_id 1 for calling a global function with example usage: save_prefixed_metrics(results, output_dir, file_name, metric_key_prefix) without return types