code: def main():
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--base-cmd",
          default=None,
          type=str,
          required=True,
          help="Base cmd",
      )
      parser.add_argument(
          "--variations",
          default=None,
          type=str,
          nargs="+",
          required=True,
          help="Multi-dimensional variations, example: '|--fp16|--bf16' '|--tf32'",
      )
      parser.add_argument(
          "--base-variation",
          default=None,
          type=str,
          help="Baseline variation to compare to. if None the minimal target value will be used to compare against",
      )
      parser.add_argument(
          "--target-metric-key",
          default=None,
          type=str,
          required=True,
          help="Target metric key in output_dir/all_results.json, e.g., train_samples_per_second",
      )
      parser.add_argument(
          "--report-metric-keys",
          default="",
          type=str,
          help="Report metric keys - other metric keys from output_dir/all_results.json to report, e.g., train_loss. Use a single argument e.g., 'train_loss train_samples",
      )
      parser.add_argument(
          "--repeat-times",
          default=1,
          type=int,
          help="How many times to re-run each variation - an average will be reported",
      )
      parser.add_argument(
          "--output_dir",
          default="output_benchmark",
          type=str,
          help="The output directory where all the benchmark reports will go to and additionally this directory will be used to override --output_dir in the script that is being benchmarked",
      )
      parser.add_argument(
          "--verbose",
          default=False,
          action="store_true",
          help="Whether to show the outputs of each run or just the benchmark progress",
      )
      args = parser.parse_args()
  
      output_dir = args.output_dir
      Path(output_dir).mkdir(exist_ok=True)
      base_cmd = get_base_command(args, output_dir)
  
      # split each dimension into its --foo variations
      dims = [
          list(map(str.strip, re.split(r"\|", x)))
          for x in args.variations
      ]
      # build a cartesian product of dimensions and convert those back into cmd-line arg strings,
      # while stripping white space for inputs that were empty
      variations = list(
          map(str.strip, map(" ".join, itertools.product(*dims)))
      )
      longest_variation_len = max(len(x) for x in variations)
  
      # split wanted keys
      report_metric_keys = args.report_metric_keys.split()
  
      # capture prints into a log file for convenience
      report_fn = f"benchmark-report-{datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S')}.txt"
      print(
          f"\nNote: each run's output is also logged under {output_dir}/log.*.std*.txt"
      )
      print(f"and this script's output is also piped into {report_fn}")
  
      sys.stdout = Tee(report_fn)
  
      print(f"\n*** Running {len(variations)} benchmarks:")
      print(f"Base command: {' '.join(base_cmd)}")
  
      variation_key = "variation"
      results = []
      for id, variation in enumerate(
          tqdm(variations, desc="Total completion: ", leave=False)
      ):
          cmd = base_cmd + variation.split()
          results.append(
              process_run(
                  id + 1,
                  cmd,
                  variation_key,
                  variation,
                  longest_variation_len,
                  args.target_metric_key,
                  report_metric_keys,
                  args.repeat_times,
                  output_dir,
                  args.verbose,
              )
          )
  
      process_results(
          results,
          args.target_metric_key,
          report_metric_keys,
          args.base_variation,
          output_dir,
      )
  
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 7 for calling a global function with example usage: main() without return types