code: def main():
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--batchsize",
          "-b",
          type=int,
          default=20,
          help="Number of examples in each mini-batch",
      )
      parser.add_argument(
          "--bproplen",
          "-l",
          type=int,
          default=35,
          help="Number of words in each mini-batch "
          "(= length of truncated BPTT)",
      )
      parser.add_argument(
          "--epoch",
          "-e",
          type=int,
          default=39,
          help="Number of sweeps over the dataset to train",
      )
      parser.add_argument(
          "--device",
          "-d",
          type=str,
          default="-1",
          help="Device specifier. Either ChainerX device "
          "specifier or an integer. If non-negative integer, "
          "CuPy arrays with specified device id are used. If "
          "negative integer, NumPy arrays are used",
      )
      parser.add_argument(
          "--gradclip",
          "-c",
          type=float,
          default=5,
          help="Gradient norm threshold to clip",
      )
      parser.add_argument(
          "--out",
          "-o",
          default="result",
          help="Directory to output the result",
      )
      parser.add_argument(
          "--resume",
          "-r",
          type=str,
          help="Resume the training from snapshot",
      )
      parser.add_argument(
          "--test",
          action="store_true",
          help="Use tiny datasets for quick tests",
      )
      parser.set_defaults(test=False)
      parser.add_argument(
          "--unit",
          "-u",
          type=int,
          default=650,
          help="Number of LSTM units in each layer",
      )
      parser.add_argument(
          "--model",
          "-m",
          default="model.npz",
          help="Model file name to serialize",
      )
      group = parser.add_argument_group("deprecated arguments")
      group.add_argument(
          "--gpu",
          "-g",
          dest="device",
          type=int,
          nargs="?",
          const=0,
          help="GPU ID (negative value indicates CPU)",
      )
      args = parser.parse_args()
  
      device = chainer.get_device(args.device)
      if device.xp is chainerx:
          sys.stderr.write(
              "This example does not support ChainerX devices.\n"
          )
          sys.exit(1)
  
      device.use()
  
      # Load the Penn Tree Bank long word sequence dataset
      train, val, test = chainer.datasets.get_ptb_words()
      n_vocab = max(train) + 1  # train is just an array of integers
      print("#vocab = {}".format(n_vocab))
  
      if args.test:
          train = train[:100]
          val = val[:100]
          test = test[:100]
  
      train_iter = ParallelSequentialIterator(train, args.batchsize)
      val_iter = ParallelSequentialIterator(val, 1, repeat=False)
      test_iter = ParallelSequentialIterator(test, 1, repeat=False)
  
      # Prepare an RNNLM model
      rnn = RNNForLM(n_vocab, args.unit)
      model = L.Classifier(rnn)
      model.compute_accuracy = False  # we only want the perplexity
      model.to_device(device)
  
      # Set up an optimizer
      optimizer = chainer.optimizers.SGD(lr=1.0)
      optimizer.setup(model)
      optimizer.add_hook(
          chainer.optimizer_hooks.GradientClipping(args.gradclip)
      )
  
      # Set up a trainer
      updater = BPTTUpdater(
          train_iter, optimizer, args.bproplen, device
      )
      trainer = training.Trainer(
          updater, (args.epoch, "epoch"), out=args.out
      )
  
      eval_model = (
          model.copy()
      )  # Model with shared params and distinct states
      eval_rnn = eval_model.predictor
      trainer.extend(
          extensions.Evaluator(
              val_iter,
              eval_model,
              device=device,
              # Reset the RNN state at the beginning of each evaluation
              eval_hook=lambda _: eval_rnn.reset_state(),
          )
      )
  
      interval = 10 if args.test else 500
      trainer.extend(
          extensions.LogReport(
              postprocess=compute_perplexity,
              trigger=(interval, "iteration"),
          )
      )
      trainer.extend(
          extensions.PrintReport(
              ["epoch", "iteration", "perplexity", "val_perplexity"]
          ),
          trigger=(interval, "iteration"),
      )
      trainer.extend(
          extensions.ProgressBar(update_interval=1 if args.test else 10)
      )
      trainer.extend(extensions.snapshot())
      trainer.extend(
          extensions.snapshot_object(
              model, "model_iter_{.updater.iteration}"
          )
      )
      if args.resume is not None:
          chainer.serializers.load_npz(args.resume, trainer)
  
      trainer.run()
  
      # Evaluate the final model
      print("test")
      eval_rnn.reset_state()
      evaluator = extensions.Evaluator(
          test_iter, eval_model, device=device
      )
      result = evaluator()
      print(
          "test perplexity: {}".format(
              np.exp(float(result["main/loss"]))
          )
      )
  
      # Serialize the final model
      chainer.serializers.save_npz(args.model, model)
  
imports: from __future__ import division
  import argparse
  import sys
  import numpy
  import chainer
  import chainer.functions
  import chainer.links
  from chainer import training
  from chainer.training import extensions
  import chainerx
label_desc: Use image node_id 2 for calling a global function with example usage: main() without return types