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 evaluate
  import numpy
  import torch
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  from accelerate.utils import set_seed
  from datasets import load_dataset
  from huggingface_hub import Repository, create_repo
  from torch.utils.data import DataLoader
  from tqdm.auto import tqdm
  from utils_qa import postprocess_qa_predictions_with_beam_search
  import transformers
  from transformers import AdamW, DataCollatorWithPadding, EvalPrediction, SchedulerType, XLNetConfig, XLNetForQuestionAnswering, XLNetTokenizerFast, default_data_collator, get_scheduler
  from transformers.utils import check_min_version, send_example_telemetry
  from transformers.utils.versions import require_version
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