code: def benchmark_single_process(config_class, args):
      """Benchmark a given model using a single process and multiple devices."""
  
      world_size = (
          torch.cuda.device_count() if torch.cuda.is_available() else 1
      )
      assert world_size > 0
      benchmark_config = utils.create_benchmark_config(
          args.model_name, config_class
      )
      model_specs = utils.get_model_specs(args.model_name, config_class)
  
      mp.spawn(
          train,
          args=(world_size, benchmark_config, model_specs, args),
          nprocs=world_size,
          join=True,
      )
  
file_path: fairscale/benchmarks/moe.py
imports: import logging
  import math
  import time
  from golden_configs.lm_wikitext2 import MOE
  import torch
  import torch.distributed
  import torch.multiprocessing
  from torch.nn.parallel import DistributedDataParallel
  import utils
label_desc: Use image node_id 1 for calling a global function with example usage: benchmark_single_process(config_class, args) without return types