code: def train(args):
      """Train with the given args
  
      :param Namespace args: The program arguments
      """
      # TODO(karita): support this
      if args.model_module != "default":
          raise NotImplementedError(
              "chainer backend does not support --model-module"
          )
  
      # display chainer version
      logging.info("chainer version = " + chainer.__version__)
  
      set_deterministic_chainer(args)
  
      # check cuda and cudnn availability
      if not chainer.cuda.available:
          logging.warning("cuda is not available")
      if not chainer.cuda.cudnn_enabled:
          logging.warning("cudnn is not available")
  
      # get special label ids
      unk = args.char_list_dict["<unk>"]
      eos = args.char_list_dict["<eos>"]
      # read tokens as a sequence of sentences
      train = read_tokens(args.train_label, args.char_list_dict)
      val = read_tokens(args.valid_label, args.char_list_dict)
      # count tokens
      n_train_tokens, n_train_oovs = count_tokens(train, unk)
      n_val_tokens, n_val_oovs = count_tokens(val, unk)
      logging.info("#vocab = " + str(args.n_vocab))
      logging.info(
          "#sentences in the training data = " + str(len(train))
      )
      logging.info(
          "#tokens in the training data = " + str(n_train_tokens)
      )
      logging.info(
          "oov rate in the training data = %.2f %%"
          % (n_train_oovs / n_train_tokens * 100)
      )
      logging.info(
          "#sentences in the validation data = " + str(len(val))
      )
      logging.info(
          "#tokens in the validation data = " + str(n_val_tokens)
      )
      logging.info(
          "oov rate in the validation data = %.2f %%"
          % (n_val_oovs / n_val_tokens * 100)
      )
  
      use_sortagrad = args.sortagrad == -1 or args.sortagrad > 0
  
      # Create the dataset iterators
      train_iter = ParallelSentenceIterator(
          train,
          args.batchsize,
          max_length=args.maxlen,
          sos=eos,
          eos=eos,
          shuffle=not use_sortagrad,
      )
      val_iter = ParallelSentenceIterator(
          val,
          args.batchsize,
          max_length=args.maxlen,
          sos=eos,
          eos=eos,
          repeat=False,
      )
      epoch_iters = int(len(train_iter.batch_indices) / args.accum_grad)
      logging.info("#iterations per epoch = %d" % epoch_iters)
      logging.info(
          "#total iterations = " + str(args.epoch * epoch_iters)
      )
      # Prepare an RNNLM model
      rnn = RNNLM(args.n_vocab, args.layer, args.unit, args.type)
      model = ClassifierWithState(rnn)
      if args.ngpu > 1:
          logging.warning(
              "currently, multi-gpu is not supported. use single gpu."
          )
      if args.ngpu > 0:
          # Make the specified GPU current
          gpu_id = 0
          chainer.cuda.get_device_from_id(gpu_id).use()
          model.to_gpu()
      else:
          gpu_id = -1
  
      # Save model conf to json
      model_conf = args.outdir + "/model.json"
      with open(model_conf, "wb") as f:
          logging.info("writing a model config file to " + model_conf)
          f.write(
              json.dumps(
                  vars(args),
                  indent=4,
                  ensure_ascii=False,
                  sort_keys=True,
              ).encode("utf_8")
          )
  
      # Set up an optimizer
      opt_class = dynamic_import_optimizer(args.opt, args.backend)
      optimizer = opt_class.from_args(model, args)
      if args.schedulers is None:
          schedulers = []
      else:
          schedulers = [
              dynamic_import_scheduler(v)(k, args)
              for k, v in args.schedulers
          ]
  
      optimizer.setup(model)
      optimizer.add_hook(
          chainer.optimizer.GradientClipping(args.gradclip)
      )
  
      updater = BPTTUpdater(
          train_iter, optimizer, schedulers, gpu_id, args.accum_grad
      )
      trainer = training.Trainer(
          updater, (args.epoch, "epoch"), out=args.outdir
      )
      trainer.extend(LMEvaluator(val_iter, model, device=gpu_id))
      trainer.extend(
          extensions.LogReport(
              postprocess=compute_perplexity,
              trigger=(args.report_interval_iters, "iteration"),
          )
      )
      trainer.extend(
          extensions.PrintReport(
              [
                  "epoch",
                  "iteration",
                  "perplexity",
                  "val_perplexity",
                  "elapsed_time",
              ]
          ),
          trigger=(args.report_interval_iters, "iteration"),
      )
      trainer.extend(
          extensions.ProgressBar(
              update_interval=args.report_interval_iters
          )
      )
      trainer.extend(
          extensions.snapshot(filename="snapshot.ep.{.updater.epoch}")
      )
      trainer.extend(
          extensions.snapshot_object(
              model, "rnnlm.model.{.updater.epoch}"
          )
      )
      # MEMO(Hori): wants to use MinValueTrigger, but it seems to fail in resuming
      trainer.extend(
          MakeSymlinkToBestModel("validation/main/loss", "rnnlm.model")
      )
  
      if use_sortagrad:
          trainer.extend(
              ShufflingEnabler([train_iter]),
              trigger=(
                  args.sortagrad
                  if args.sortagrad != -1
                  else args.epoch,
                  "epoch",
              ),
          )
  
      if args.resume:
          logging.info("resumed from %s" % args.resume)
          chainer.serializers.load_npz(args.resume, trainer)
  
      set_early_stop(trainer, args, is_lm=True)
      if (
          args.tensorboard_dir is not None
          and args.tensorboard_dir != ""
      ):
          try:
              from tensorboardX import SummaryWriter
          except Exception:
              logging.error("Please install tensorboardx")
              raise
          writer = SummaryWriter(args.tensorboard_dir)
          trainer.extend(
              TensorboardLogger(writer),
              trigger=(args.report_interval_iters, "iteration"),
          )
  
      trainer.run()
      check_early_stop(trainer, args.epoch)
  
      # compute perplexity for test set
      if args.test_label:
          logging.info("test the best model")
          chainer.serializers.load_npz(
              args.outdir + "/rnnlm.model.best", model
          )
          test = read_tokens(args.test_label, args.char_list_dict)
          n_test_tokens, n_test_oovs = count_tokens(test, unk)
          logging.info(
              "#sentences in the test data = " + str(len(test))
          )
          logging.info(
              "#tokens in the test data = " + str(n_test_tokens)
          )
          logging.info(
              "oov rate in the test data = %.2f %%"
              % (n_test_oovs / n_test_tokens * 100)
          )
          test_iter = ParallelSentenceIterator(
              test,
              args.batchsize,
              max_length=args.maxlen,
              sos=eos,
              eos=eos,
              repeat=False,
          )
          evaluator = LMEvaluator(test_iter, model, device=gpu_id)
          with chainer.using_config("train", False):
              result = evaluator()
          logging.info(
              "test perplexity: "
              + str(np.exp(float(result["main/loss"])))
          )
  
imports: import copy
  import json
  import logging
  import chainer
  import chainer.functions
  import chainer.links
  import numpy
  from chainer import link, reporter, training
  from chainer.dataset import convert
  from chainer.functions.loss import softmax_cross_entropy
  from chainer.training import extensions
  import espnet.nets.chainer_backend.deterministic_embed_id
  from espnet.lm.lm_utils import MakeSymlinkToBestModel, ParallelSentenceIterator, compute_perplexity, count_tokens, read_tokens
  from espnet.nets.lm_interface import LMInterface
  from espnet.optimizer.factory import dynamic_import_optimizer
  from espnet.scheduler.chainer import ChainerScheduler
  from espnet.scheduler.scheduler import dynamic_import_scheduler
  from espnet.utils.deterministic_utils import set_deterministic_chainer
  from espnet.utils.training.evaluator import BaseEvaluator
  from espnet.utils.training.iterators import ShufflingEnabler
  from espnet.utils.training.tensorboard_logger import TensorboardLogger
  from espnet.utils.training.train_utils import check_early_stop, set_early_stop
label_desc: Use image node_id 1 for calling a global function with example usage: train(args) without return types