code: def benchmark_language_model(
      model_config, model, benchmark_config, model_specs, args
  ):
      golden_config = get_golden_config(args.model_name, args)
      epoch = benchmark_config["epochs"]
      start_time = time.time()
      if dist.get_rank() == 0:
          print("-" * 110)
          print("| start of epoch {:1d}".format(epoch))
          print("-" * 110)
      wps, loss = train(
          model_config, model, benchmark_config, model_specs, args
      )
      elapsed_time = time.time() - start_time
      if dist.get_rank() == 0:
          print("-" * 110)
          print(
              "| end of epoch {:1d} | time: {:5.2f}s | train loss {:5.2f} ".format(
                  epoch, elapsed_time, loss
              )
          )
          print("-" * 110)
          print("Throughput(wps) is {:.2f}.".format(wps))
      print(
          "Peak allocated bytes on cuda:{}: {:4f}GB".format(
              dist.get_rank(),
              torch.cuda.memory_stats(dist.get_rank())[
                  "allocated_bytes.all.peak"
              ]
              / 2**30,
          )
      )
  
      verify_lm_run(wps, golden_config, args)
  
imports: import argparse
  from collections import defaultdict
  from functools import reduce
  import gc
  import logging
  import math
  import operator
  import time
  from datasets.wikitext2_data import get_real_dataloaders
  from datasets.wikitext2_data import get_synthetic_dataloaders
  from models import transformer_lm
  import numpy
  import torch
  import torch.distributed
  import torch.multiprocessing
  from torch.nn.parallel import DistributedDataParallel
  from torch.optim import Adam
  from benchmarks.golden_configs.lm_wikitext2 import FSDP
  from fairscale.nn import auto_wrap, default_auto_wrap_policy, enable_wrap
  from fairscale.nn.data_parallel import FullyShardedDataParallel
label_desc: Use image node_id 12 for calling a global function with example usage: benchmark_language_model(model_config, model, benchmark_config, model_specs, args) without return types