code: def benchmark_language_model(
      model_config,
      model,
      benchmark_config,
      model_specs,
      config_class,
      args,
  ):
      golden_config = get_golden_config(
          args.model_name, config_class, args
      )
      epoch = benchmark_config["epochs"]
      start_time = time.time()
      if dist.get_rank() == dist.get_world_size() - 1:
          logging.debug("-" * 110)
          logging.debug("| start of epoch {:1d}".format(epoch))
          logging.debug("-" * 110)
      wps, loss = train(
          model_config, model, benchmark_config, model_specs, args
      )
      elapsed_time = time.time() - start_time
      if dist.get_rank() == dist.get_world_size() - 1:
          logging.debug("-" * 110)
          logging.debug(
              "| end of epoch {:1d} | time: {:5.2f}s | train loss {:5.2f} ".format(
                  epoch, elapsed_time, loss
              )
          )
          logging.debug("-" * 110)
          logging.debug("Throughput(wps) is {:.2f}.".format(wps))
      logging.debug(
          "Peak allocated bytes on cuda:{}: {:1d}".format(
              dist.get_rank(),
              torch.cuda.memory_stats(dist.get_rank())[
                  "allocated_bytes.all.peak"
              ],
          )
      )
  
      if len(model.balance) == 4:
          if args.model_name == "lm":
              verify_lm_run(wps, golden_config, args)
          else:
              raise RuntimeError(
                  "Unrecognized args.model_name " % args.model_name
              )
  
imports: from collections import defaultdict
  import gc
  import logging
  import math
  import time
  import torch
  import torch.distributed
  from torch.distributed import rpc
  from torch.nn.parallel import DistributedDataParallel
  import utils
  from benchmarks.golden_configs.lm_wikitext2 import Pipe
  from fairscale.fair_dev.testing.testing import dist_init
  from fairscale.nn import Pipe
  from fairscale.nn.model_parallel import initialize_model_parallel
label_desc: Use image node_id 9 for calling a global function with example usage: benchmark_language_model(model_config, model, benchmark_config, model_specs, config_class, args) without return types