code: def main():
      parser = argparse.ArgumentParser(
          description="Chainer example: seq2seq"
      )
      parser.add_argument("SOURCE", help="source sentence list")
      parser.add_argument("TARGET", help="target sentence list")
      parser.add_argument("SOURCE_VOCAB", help="source vocabulary file")
      parser.add_argument("TARGET_VOCAB", help="target vocabulary file")
      parser.add_argument(
          "--validation-source",
          help="source sentence list for validation",
      )
      parser.add_argument(
          "--validation-target",
          help="target sentence list for validation",
      )
      parser.add_argument(
          "--batchsize",
          "-b",
          type=int,
          default=64,
          help="number of sentence pairs in each mini-batch",
      )
      parser.add_argument(
          "--epoch",
          "-e",
          type=int,
          default=20,
          help="number of sweeps over the dataset to train",
      )
      parser.add_argument(
          "--resume",
          "-r",
          type=str,
          help="resume the training from snapshot",
      )
      parser.add_argument(
          "--save",
          "-s",
          type=str,
          help="save a snapshot of the training",
      )
      parser.add_argument(
          "--unit", "-u", type=int, default=1024, help="number of units"
      )
      parser.add_argument(
          "--layer", "-l", type=int, default=3, help="number of layers"
      )
      parser.add_argument(
          "--use-dataset-api",
          default=False,
          action="store_true",
          help="use TextDataset API to reduce CPU memory usage",
      )
      parser.add_argument(
          "--min-source-sentence",
          type=int,
          default=1,
          help="minimium length of source sentence",
      )
      parser.add_argument(
          "--max-source-sentence",
          type=int,
          default=50,
          help="maximum length of source sentence",
      )
      parser.add_argument(
          "--min-target-sentence",
          type=int,
          default=1,
          help="minimium length of target sentence",
      )
      parser.add_argument(
          "--max-target-sentence",
          type=int,
          default=50,
          help="maximum length of target sentence",
      )
      parser.add_argument(
          "--log-interval",
          type=int,
          default=200,
          help="number of iteration to show log",
      )
      parser.add_argument(
          "--validation-interval",
          type=int,
          default=4000,
          help="number of iteration to evlauate the model "
          "with validation dataset",
      )
      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(
          "--out",
          "-o",
          default="result",
          help="directory to output the result",
      )
      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)
  
      print("Device: {}".format(device))
      print("# Minibatch-size: {}".format(args.batchsize))
      print("# epoch: {}".format(args.epoch))
      print("")
  
      # If the device is a ChainerX CUDA device, use the shared device memory
      # pool between ChainerX and CuPy.
      if device.xp is chainerx and device.device.backend.name == "cuda":
          # TODO(niboshi): The API is provisional.
          chainerx._cuda.cupy_share_allocator()
  
      # Load pre-processed dataset
      print(
          "[{}] Loading dataset... (this may take several minutes)".format(
              datetime.datetime.now()
          )
      )
      source_ids = load_vocabulary(args.SOURCE_VOCAB)
      target_ids = load_vocabulary(args.TARGET_VOCAB)
  
      if args.use_dataset_api:
          # By using TextDataset, you can avoid loading whole dataset on memory.
          # This significantly reduces the host memory usage.
          def _filter_func(s, t):
              sl = len(
                  s.strip().split()
              )  # number of words in source line
              tl = len(
                  t.strip().split()
              )  # number of words in target line
              return (
                  args.min_source_sentence
                  <= sl
                  <= args.max_source_sentence
                  and args.min_target_sentence
                  <= tl
                  <= args.max_target_sentence
              )
  
          train_data = load_data_using_dataset_api(
              source_ids,
              args.SOURCE,
              target_ids,
              args.TARGET,
              _filter_func,
          )
      else:
          # Load all records on memory.
          train_source = load_data(source_ids, args.SOURCE)
          train_target = load_data(target_ids, args.TARGET)
          assert len(train_source) == len(train_target)
  
          train_data = [
              (s, t)
              for s, t in six.moves.zip(train_source, train_target)
              if (
                  args.min_source_sentence
                  <= len(s)
                  <= args.max_source_sentence
                  and args.min_target_sentence
                  <= len(t)
                  <= args.max_target_sentence
              )
          ]
      print("[{}] Dataset loaded.".format(datetime.datetime.now()))
  
      if not args.use_dataset_api:
          # Skip printing statistics when using TextDataset API, as it is slow.
          train_source_unknown = calculate_unknown_ratio(
              [s for s, _ in train_data]
          )
          train_target_unknown = calculate_unknown_ratio(
              [t for _, t in train_data]
          )
  
          print("Source vocabulary size: %d" % len(source_ids))
          print("Target vocabulary size: %d" % len(target_ids))
          print("Train data size: %d" % len(train_data))
          print(
              "Train source unknown ratio: %.2f%%"
              % (train_source_unknown * 100)
          )
          print(
              "Train target unknown ratio: %.2f%%"
              % (train_target_unknown * 100)
          )
  
      target_words = {i: w for w, i in target_ids.items()}
      source_words = {i: w for w, i in source_ids.items()}
  
      # Set the current device
      device.use()
  
      # Setup model
      model = Seq2seq(
          args.layer, len(source_ids), len(target_ids), args.unit
      )
      model.to_device(device)
  
      # Setup optimizer
      optimizer = chainer.optimizers.Adam()
      optimizer.setup(model)
  
      # Setup iterator
      train_iter = chainer.iterators.SerialIterator(
          train_data, args.batchsize
      )
  
      # Setup updater and trainer
      updater = training.updaters.StandardUpdater(
          train_iter, optimizer, converter=convert, device=device
      )
      trainer = training.Trainer(
          updater, (args.epoch, "epoch"), out=args.out
      )
      trainer.extend(
          extensions.LogReport(trigger=(args.log_interval, "iteration"))
      )
      trainer.extend(
          extensions.PrintReport(
              [
                  "epoch",
                  "iteration",
                  "main/loss",
                  "main/perp",
                  "validation/main/bleu",
                  "elapsed_time",
              ]
          ),
          trigger=(args.log_interval, "iteration"),
      )
  
      trainer.extend(
          extensions.snapshot(
              filename="snapshot_epoch_{.updater.iteration}"
          ),
          trigger=(args.validation_interval, "iteration"),
      )
  
      if args.validation_source and args.validation_target:
          test_source = load_data(source_ids, args.validation_source)
          test_target = load_data(target_ids, args.validation_target)
          assert len(test_source) == len(test_target)
          test_data = list(six.moves.zip(test_source, test_target))
          test_data = [
              (s, t) for s, t in test_data if 0 < len(s) and 0 < len(t)
          ]
          test_source_unknown = calculate_unknown_ratio(
              [s for s, _ in test_data]
          )
          test_target_unknown = calculate_unknown_ratio(
              [t for _, t in test_data]
          )
  
          print("Validation data: %d" % len(test_data))
          print(
              "Validation source unknown ratio: %.2f%%"
              % (test_source_unknown * 100)
          )
          print(
              "Validation target unknown ratio: %.2f%%"
              % (test_target_unknown * 100)
          )
  
          @chainer.training.make_extension()
          def translate(trainer):
              source, target = test_data[
                  numpy.random.choice(len(test_data))
              ]
              result = model.translate([model.xp.array(source)])[0]
  
              source_sentence = " ".join(
                  [source_words[x] for x in source]
              )
              target_sentence = " ".join(
                  [target_words[y] for y in target]
              )
              result_sentence = " ".join(
                  [target_words[y] for y in result]
              )
              print("# source : " + source_sentence)
              print("# result : " + result_sentence)
              print("# expect : " + target_sentence)
  
          trainer.extend(
              translate, trigger=(args.validation_interval, "iteration")
          )
          trainer.extend(
              CalculateBleu(
                  model, test_data, "validation/main/bleu", device
              ),
              trigger=(args.validation_interval, "iteration"),
          )
  
      if args.resume is not None:
          # Resume from a snapshot
          chainer.serializers.load_npz(args.resume, trainer)
      print("start training")
  
      trainer.run()
  
      if args.save is not None:
          # Save a snapshot
          chainer.serializers.save_npz(args.save, trainer)
  
imports: import argparse
  import datetime
  import io
  from nltk.translate import bleu_score
  import numpy
  import progressbar
  import six
  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 8 for calling a global function with example usage: main() and returns: unknown