code: def benchmark_process_group(
      pg, benchmark, use_ddp_for_single_rank=True
  ):
      torch.manual_seed(pg.rank())
      torch.cuda.manual_seed(pg.rank())
  
      model = benchmark.create_model()
      data = [
          (benchmark.generate_inputs(), benchmark.generate_target())
      ]
      criterion = nn.CrossEntropyLoss()
      optimizer = optim.SGD(
          model.parameters(), 0.001, momentum=0.9, weight_decay=1e-4
      )
      if use_ddp_for_single_rank or pg.size() > 1:
          model = torch.nn.parallel.DistributedDataParallel(
              model,
              device_ids=[torch.cuda.current_device()],
              broadcast_buffers=False,
              process_group=pg,
              bucket_cap_mb=benchmark.bucket_size,
          )
  
      measurements = []
      warmup_iterations = 5
      measured_iterations = 10
      for inputs, target in data * (
          warmup_iterations + measured_iterations
      ):
          start = time.time()
          output = model(*inputs)
          loss = criterion(output, target)
          loss.backward()
          optimizer.step()
          torch.cuda.synchronize()
          measurements.append(time.time() - start)
  
      # Throw away measurements for warmup iterations
      return measurements[warmup_iterations:]
  
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 4 for calling a global function with example usage: benchmark_process_group(pg, benchmark, use_ddp_for_single_rank) and returns: measurements