code: def train(model_config, model, benchmark_config, model_specs, args):
      device = torch.device("cuda")
      torch.cuda.set_device(0)
  
      lm_dataloader, _, _ = model_config["data"]
      criterion = benchmark_config["criterion"]
      vocab_size = model_specs["vocab_size"]
      optimizer = model_config["optimizer"]
  
      model.train()
      log_number_of_parameters(model)
  
      total_loss = 0.0
      word_counter = 0
  
      optimizer = optimizer(model.parameters())
  
      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):
          # TODO(anj): Make this a flag for both "lm" and "seq" models.
          if i == 5:
              break
  
          if i == 1:
              epoch_start_time = time.time()
  
          source, target = get_batch(batch)
          source, target = source.cuda(), target.cuda()
  
          if i > 0:
              total_tokens += source.numel()
  
          with _get_profiler_context(args.use_profiler) as prof:
              optimizer.zero_grad()
              with _get_profiler_record_context(
                  "FW pass", args.use_profiler
              ):
                  output = model(source)
              with _get_profiler_record_context(
                  "Loss", args.use_profiler
              ):
                  loss = criterion(
                      output.view(-1, vocab_size), target.view(-1)
                  )
              with _get_profiler_record_context(
                  "BW pass", args.use_profiler
              ):
                  loss.backward()
              torch.nn.utils.clip_grad_value_(
                  model.parameters(), model_specs["clip_value"]
              )
              with _get_profiler_record_context(
                  "Opt step", args.use_profiler
              ):
                  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
              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 args.use_profiler:
              prof.export_chrome_trace("/tmp/offload_prof")
  
      if epoch_start_time != 0:
          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
  import contextlib
  from functools import reduce
  import logging
  import math
  import operator
  import time
  import numpy
  import torch
  from torch.optim import Adam
  from torch.utils.data.dataloader import DataLoader
  from torchvision.datasets import FakeData
  from torchvision.transforms import ToTensor
  from benchmarks.datasets.wikitext2_data import get_real_dataloaders
  from benchmarks.datasets.wikitext2_data import get_synthetic_dataloaders
  from benchmarks.golden_configs.lm_wikitext2 import Offload_Sequential
  from benchmarks.golden_configs.lm_wikitext2 import Offload_Transformer
  from benchmarks.models import transformer_lm
  from fairscale.experimental.nn.offload import OffloadModel
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