code: def process_run(
      id,
      cmd,
      variation_key,
      variation,
      longest_variation_len,
      target_metric_key,
      report_metric_keys,
      repeat_times,
      output_dir,
      verbose,
  ):
      results = []
      metrics = []
      preamble = f"{id}: {variation:<{longest_variation_len}}"
      outcome = f"{preamble}: "
      metric_keys = set(report_metric_keys + [target_metric_key])
      for i in tqdm(range(repeat_times), desc=preamble, leave=False):
          single_run_metrics = process_run_single(
              id,
              cmd,
              variation,
              output_dir,
              target_metric_key,
              metric_keys,
              verbose,
          )
          result = single_run_metrics[target_metric_key]
          if not math.isnan(result):
              metrics.append(single_run_metrics)
              results.append(result)
              outcome += "✓"
          else:
              outcome += "✘"
      outcome = f"\33[2K\r{outcome}"
      if len(metrics) > 0:
          mean_metrics = {
              k: fmean([x[k] for x in metrics])
              for k in metrics[0].keys()
          }
          mean_target = round(mean_metrics[target_metric_key], 2)
          results_str = f"{outcome} {mean_target}"
          if len(metrics) > 1:
              results_str += f" {tuple(round(x, 2) for x in results)}"
          print(results_str)
          mean_metrics[variation_key] = variation
          return mean_metrics
      else:
          print(outcome)
          return {variation_key: variation, target_metric_key: nan}
  
imports: import argparse
  import datetime
  import io
  import itertools
  import json
  import math
  import os
  import platform
  import re
  import shlex
  import subprocess
  import sys
  from pathlib import Path
  from statistics import fmean
  import pandas
  import torch
  from tqdm import tqdm
  import transformers
label_desc: Use image node_id 4 for calling a global function with example usage: process_run(id, cmd, variation_key, variation, longest_variation_len, target_metric_key, report_metric_keys, repeat_times, output_dir, verbose) and returns: mean_metrics, dict