code: def train(model_config, model, benchmark_config, model_specs, args):
      lm_dataloader, _, _ = model_config["data"]
      criterion = benchmark_config["criterion"]
      vocab_size = model_specs["vocab_size"]
      optimizer = model_config["optimizer"]
  
      if not args.benchmark_eval:
          model.train()
      log_number_of_parameters(model)
  
      total_loss = 0.0
      word_counter = 0
  
      optimizer = optimizer(model.parameters())
  
      device = (
          torch.device("cuda")
          if torch.cuda.is_available()
          else torch.device("cpu")
      )
  
      total_tokens = 0
      total_tokens_per_log_interval = 0
      bptt = 2
      start_time = time.time()
      epoch_start_time = 0.0
  
      def get_batch(source):
          seq_len = len(source) - 1
          data = source[0:seq_len]
          target = source[1 : 1 + seq_len]
          return data, target
  
      for i, batch in enumerate(lm_dataloader):
          if i == 1:
              epoch_start_time = time.time()
  
          source, target = get_batch(batch)
          if args.full_fp16:
              # source = source.half()
              target = target.half()
          if args.max_batch and i > args.max_batch:
              break
  
          if i > 0:
              total_tokens += source.numel()
  
          if args.benchmark_eval:
              input = source.cuda()
              target = target.cuda()
              output = model(input)
              print(
                  f"output.dtype {output.dtype}, target.dtype {target.dtype}"
              )
              loss = torch.nn.CrossEntropyLoss()(
                  output.view(-1, vocab_size), target.view(-1)
              )
          else:
              optimizer.zero_grad()
              input = source.cuda()
              target = target.cuda()
              output = model(input)
  
              loss = criterion(
                  output.view(-1, vocab_size), target.view(-1)
              )
              loss.backward()
  
              torch.nn.utils.clip_grad_value_(
                  model.parameters(), model_specs["clip_value"]
              )
              optimizer.step()
  
          total_loss += loss.item()
  
          log_interval = 1
          total_tokens_per_log_interval += source.numel()
          if i % log_interval == 0 and i > 0:
              cur_loss = total_loss / log_interval
              elapsed = time.time() - start_time
              if dist.get_rank() == 0:
                  print(
                      "| batch {:5d} | wps {:5.2f} | loss {:5.2f} | ppl {:8.2f}".format(
                          i,
                          total_tokens_per_log_interval / elapsed,
                          cur_loss,
                          math.exp(cur_loss),
                      )
                  )
              total_tokens_per_log_interval = 0
              total_loss = 0
              start_time = time.time()
  
      if epoch_start_time != 0:
          torch.cuda.synchronize()
          wps = total_tokens / (time.time() - epoch_start_time)
      else:
          raise RuntimeError(
              "Unable to benchmark on a single batch. Increase the size "
              " of the dataset and rerun the benchmark."
          )
      return wps, loss.item()
  
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 10 for calling a global function with example usage: train(model_config, model, benchmark_config, model_specs, args) and returns: wps,  loss, data,  target