code: def main():
      np.random.seed(0)
      random.seed(1)
  
      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=25,
          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="0",
          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",
          default="",
          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",
      )
      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()
  
      def evaluate(model, iter):
          # Evaluation routine to be used for validation and test.
          evaluator = model.copy()  # to use different state
          evaluator.rnn.reset_state()  # initialize state
          sum_perp = 0
          data_count = 0
          words = []
          labels = []
          lossfun = softmax_cross_entropy.softmax_cross_entropy
          with configuration.using_config("train", False):
              iter.reset()
              for batch in iter:
                  word, label = convert.concat_examples(batch, device)
                  words.append(word)
                  labels.append(label)
                  data_count += 1
              outputs = evaluator(words)
  
              for ind in range(len(outputs)):
                  y = outputs[ind]
                  label = labels[ind]
                  loss = lossfun(y, label)
                  sum_perp += loss.array
          return np.exp(float(sum_perp) / data_count)
  
      # 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 =", n_vocab)
  
      if args.test:
          train = train[:100]
          val = val[:100]
          test = test[:100]
  
      # Create the dataset iterators
      train_iter = ParallelSequentialIterator(train, args.batchsize)
      val_iter = ParallelSequentialIterator(val, 1, repeat=False)
      test_iter = ParallelSequentialIterator(test, 1, repeat=False)
  
      # Prepare an RNNLM model
      model = RNNForLMUnrolled(n_vocab, args.unit)
      lossfun = softmax_cross_entropy.softmax_cross_entropy
      model.to_device(device)
  
      # Set up an optimizer
      optimizer = chainer.optimizers.SGD(lr=1.0)
      optimizer.setup(model)
      optimizer.add_hook(
          chainer.optimizer.GradientClipping(args.gradclip)
      )
  
      sum_perp = 0
      count = 0
      iteration = 0
  
      while train_iter.epoch < args.epoch:
          iteration += 1
          words = []
          labels = []
          # Progress the dataset iterator for bprop_len words at each iteration.
          for i in range(args.bproplen):
              # Get the next batch (a list of tuples of two word IDs)
              batch = train_iter.__next__()
              # Concatenate the word IDs to matrices and send them to the device
              # self.converter does this job
              # (it is chainer.dataset.concat_examples by default)
              word, label = convert.concat_examples(batch, device)
              words.append(word)
              labels.append(label)
              count += 1
  
          outputs = model(words)
  
          loss = 0
          for ind in range(len(outputs)):
              y = outputs[ind]
              label = labels[ind]
              loss += lossfun(y, label)
  
          sum_perp += loss.array
          optimizer.target.cleargrads()  # Clear the parameter gradients
          loss.backward()  # Backprop
          loss.unchain_backward()  # Truncate the graph
          optimizer.update()  # Update the parameters
  
          if iteration % 20 == 0:
              print("iteration: ", iteration)
              print(
                  "training perplexity: ",
                  np.exp(float(sum_perp) / count),
              )
              sum_perp = 0
              count = 0
  
          if train_iter.is_new_epoch:
              print("Evaluating model on validation set...")
              print("epoch: ", train_iter.epoch)
              print(
                  "validation perplexity: ", evaluate(model, val_iter)
              )
  
      # Evaluate on test dataset
      print("test")
      test_perp = evaluate(model, test_iter)
      print("test perplexity:", test_perp)
  
      # Save the model and the optimizer
      print("save the model")
      serializers.save_npz("rnnlm.model", model)
      print("save the optimizer")
      serializers.save_npz("rnnlm.state", optimizer)
  
imports: from __future__ import print_function
  import argparse
  import numpy
  import random
  import sys
  import chainer
  from chainer import configuration
  from chainer.dataset import convert
  import chainer.functions
  from chainer.functions.loss import softmax_cross_entropy
  import chainer.links
  from chainer import serializers
  from chainer import static_graph
  import chainerx
label_desc: Use image node_id 1 for calling a global function with example usage: main() and returns: np