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(
          "--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() == numpy.float16:
          warnings.warn(
              "This example may cause NaN in FP16 mode.", RuntimeWarning
          )
  
      vocab = {}
      max_size = None
      train_trees = data.read_corpus("trees/train.txt", max_size)
      test_trees = data.read_corpus("trees/test.txt", max_size)
  
      device = chainer.get_device(args.device)
      device.use()
      xp = device.xp
  
      train_data = [linearize_tree(vocab, t, xp) for t in train_trees]
      train_iter = chainer.iterators.SerialIterator(
          train_data, args.batchsize
      )
      test_data = [linearize_tree(vocab, t, xp) for t in test_trees]
      test_iter = chainer.iterators.SerialIterator(
          test_data, args.batchsize, repeat=False, shuffle=False
      )
  
      model = ThinStackRecursiveNet(len(vocab), args.unit, args.label)
      model.to_device(device)
  
      optimizer = chainer.optimizers.AdaGrad(0.1)
      optimizer.setup(model)
  
      updater = training.StandardUpdater(
          train_iter, optimizer, converter=convert, device=device
      )
      trainer = training.Trainer(updater, (args.epoch, "epoch"))
      trainer.extend(
          extensions.Evaluator(
              test_iter, model, converter=convert, device=device
          ),
          trigger=(args.epocheval, "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.ProgressBar(update_interval=10))
  
      trainer.run()
  
imports: import argparse
  import warnings
  import numpy
  import chainer
  import chainer.functions
  import chainer.links
  from chainer import reporter
  from chainer import training
  from chainer.training import extensions
  import data
  import thin_stack
label_desc: Use image node_id 3 for calling a global function with example usage: main() without return types