code: def main():
      parser = argparse.ArgumentParser()
      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",
          type=str,
          help="Directory to ouput the result",
      )
      parser.add_argument(
          "--resume",
          "-r",
          type=str,
          help="Resume the training from snapshot",
      )
      parser.add_argument(
          "--epoch",
          "-e",
          default=400,
          type=int,
          help="number of epochs to learn",
      )
      parser.add_argument(
          "--unit", "-u", default=30, type=int, help="number of units"
      )
      parser.add_argument(
          "--batchsize",
          "-b",
          type=int,
          default=25,
          help="learning minibatch size",
      )
      parser.add_argument(
          "--label", "-l", type=int, default=5, help="number of labels"
      )
      parser.add_argument(
          "--epocheval",
          "-p",
          type=int,
          default=5,
          help="number of epochs per evaluation",
      )
      parser.add_argument("--test", dest="test", action="store_true")
      parser.set_defaults(test=False)
      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()
  
      if chainer.get_dtype() == np.float16:
          warnings.warn(
              "This example may cause NaN in FP16 mode.", RuntimeWarning
          )
  
      n_epoch = args.epoch  # number of epochs
      n_units = args.unit  # number of units per layer
      batchsize = args.batchsize  # minibatch size
      n_label = args.label  # number of labels
      epoch_per_eval = args.epocheval  # number of epochs per evaluation
  
      if args.test:
          max_size = 10
      else:
          max_size = None
  
      device = chainer.get_device(args.device)
      device.use()
  
      vocab = {}
      train_data = [
          convert_tree(vocab, tree)
          for tree in data.read_corpus("trees/train.txt", max_size)
      ]
      train_iter = chainer.iterators.SerialIterator(
          train_data, batchsize
      )
      validation_data = [
          convert_tree(vocab, tree)
          for tree in data.read_corpus("trees/dev.txt", max_size)
      ]
      validation_iter = chainer.iterators.SerialIterator(
          validation_data, batchsize, repeat=False, shuffle=False
      )
      test_data = [
          convert_tree(vocab, tree)
          for tree in data.read_corpus("trees/test.txt", max_size)
      ]
  
      model = RecursiveNet(len(vocab), n_units, n_label)
      model.to_device(device)
  
      # Setup optimizer
      optimizer = optimizers.AdaGrad(lr=0.1)
      optimizer.setup(model)
      optimizer.add_hook(chainer.optimizer_hooks.WeightDecay(0.0001))
  
      # Setup updater
      updater = chainer.training.StandardUpdater(
          train_iter, optimizer, converter=convert, device=device
      )
  
      # Setup trainer and run
      trainer = chainer.training.Trainer(
          updater, (n_epoch, "epoch"), args.out
      )
      trainer.extend(
          extensions.Evaluator(
              validation_iter, model, converter=convert, device=device
          ),
          trigger=(epoch_per_eval, "epoch"),
      )
      trainer.extend(extensions.LogReport())
  
      trainer.extend(
          extensions.MicroAverage(
              "main/correct", "main/total", "main/accuracy"
          )
      )
      trainer.extend(
          extensions.MicroAverage(
              "validation/main/correct",
              "validation/main/total",
              "validation/main/accuracy",
          )
      )
  
      trainer.extend(
          extensions.PrintReport(
              [
                  "epoch",
                  "main/loss",
                  "validation/main/loss",
                  "main/accuracy",
                  "validation/main/accuracy",
                  "elapsed_time",
              ]
          )
      )
  
      trainer.extend(
          extensions.snapshot(
              filename="snapshot_epoch_{.updater.epoch}"
          ),
          trigger=(epoch_per_eval, "epoch"),
      )
  
      trainer.extend(extensions.ProgressBar(update_interval=10))
  
      if args.resume is not None:
          chainer.serializers.load_npz(args.resume, trainer)
  
      trainer.run()
  
      print("Test evaluation")
      evaluate(model, test_data)
  
imports: import argparse
  import collections
  import warnings
  import numpy
  import chainer
  from chainer.backends import cuda
  import chainer.functions
  import chainer.links
  from chainer import optimizers
  from chainer import reporter
  from chainer.training import extensions
  import data
label_desc: Use image node_id 4 for calling a global function with example usage: main() without return types