code: def run_benchmark(benchmark, ranks, opts):
      group = dist.new_group(
          ranks=ranks, backend=benchmark.distributed_backend
      )
      measurements = []
      if dist.get_rank() in set(ranks):
          if not opts:
              opts = {}
          measurements = benchmark_process_group(
              group, benchmark, **opts
          )
      dist.destroy_process_group(group)
      dist.barrier()
  
      # Aggregate measurements for better estimation of percentiles
      return list(itertools.chain(*allgather_object(measurements)))
  
imports: import argparse
  import itertools
  import json
  import os
  import shlex
  import subprocess
  import sys
  import time
  import numpy
  import torch
  import torch.distributed
  import torch.nn
  import torch.optim
  import torchvision
label_desc: Use image node_id 5 for calling a global function with example usage: run_benchmark(benchmark, ranks, opts) and returns: list