code: def train(model_config, model, benchmark_config, model_specs, args):
      lm_dataloader, _, _ = utils.get_data_loader(
          model_config["dataset_info"],
          args,
          benchmark_config,
          model_specs,
      )
      criterion = benchmark_config["criterion"]
      vocab_size = model_specs["vocab_size"]
      optimizer = model_config["optimizer"]
  
      model.train()
      utils.log_number_of_parameters(model)
  
      total_loss = 0.0
      word_counter = 0
  
      optimizer = optimizer(model.parameters())
  
      pipe_group = model.group if hasattr(model, "group") else None
  
      # TODO(anj-s): Avoid sending fake data to all replicas except the first and last one.
      device = (
          torch.device("cuda")
          if torch.cuda.is_available()
          else torch.device("cpu")
      )
      if (
          pipe_group
          and pipe_group.rank() != 0
          and pipe_group.rank() != (pipe_group.size() - 1)
      ):
          lm_dataloader, _, _ = get_synthetic_dataloaders(
              args, benchmark_config, model_specs
          )
  
      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.max_batch and i > args.max_batch:
              break
  
          if i > 0:
              total_tokens += source.numel()
  
          optimizer.zero_grad()
          try:
              if pipe_group is None or pipe_group.rank() == 0:
                  tmp = source.to(get_device(model, 0))
                  output = model(tmp)
              else:
                  output = model(source)
          except Exception as e:
              raise RuntimeError(
                  f"training failed on {torch.distributed.get_rank()}"
              ) from e
  
          if (
              pipe_group is None
              or pipe_group.rank() == pipe_group.size() - 1
          ):
              target = target.to(get_device(model, -1))
              output = output.to(target.device)
              loss = criterion(
                  output.view(-1, vocab_size), target.view(-1)
              )
              loss.backward()
              del target
          else:
              model.back_helper(output)
  
          del output
  
          torch.nn.utils.clip_grad_value_(
              model.parameters(), model_specs["clip_value"]
          )
          optimizer.step()
  
          if (
              pipe_group is None
              or pipe_group.rank() == pipe_group.size() - 1
          ):
              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() == dist.get_world_size() - 1:
                      logging.debug(
                          "| 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:
          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."
          )
      if dist.get_rank() == dist.get_world_size() - 1:
          return wps, loss.item()
      else:
          return 0.0, 0.0
  
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 4 for calling a global function with example usage: train(model_config, model, benchmark_config, model_specs, args) and returns: data,  target, wps,  loss, int,  int