code: def main():
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--out", type=str, default="result", help="Output directory"
      )
      parser.add_argument(
          "--resume",
          "-r",
          type=str,
          help="Resume the training from snapshot",
      )
      parser.add_argument(
          "--mscoco-root",
          type=str,
          default="data",
          help="MSOCO dataset root directory",
      )
      parser.add_argument(
          "--max-iters",
          type=int,
          default=50000,
          help="Maximum number of iterations to train",
      )
      parser.add_argument(
          "--batch-size", type=int, default=128, help="Minibatch size"
      )
      parser.add_argument(
          "--dropout-ratio",
          type=float,
          default=0.5,
          help="Language model dropout ratio",
      )
      parser.add_argument(
          "--val-keep-quantity",
          type=int,
          default=100,
          help="Keep every N-th validation image",
      )
      parser.add_argument(
          "--val-iter",
          type=int,
          default=100,
          help="Run validation every N-th iteration",
      )
      parser.add_argument(
          "--log-iter",
          type=int,
          default=1,
          help="Log every N-th iteration",
      )
      parser.add_argument(
          "--snapshot-iter",
          type=int,
          default=1000,
          help="Model snapshot every N-th iteration",
      )
      parser.add_argument(
          "--rnn",
          type=str,
          default="nsteplstm",
          choices=["nsteplstm", "lstm"],
          help="Language model layer type",
      )
      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(
          "--max-caption-length",
          type=int,
          default=30,
          help="Maxium caption length when using LSTM 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)
  
      print("Device: {}".format(device))
      print()
  
      # Load the MSCOCO dataset. Assumes that the dataset has been downloaded
      # already using e.g. the `download.py` script
      train, val = datasets.get_mscoco(args.mscoco_root)
  
      # Validation samples are used to address overfitting and see how well your
      # model generalizes to yet unseen data. However, since the number of these
      # samples in MSCOCO is quite large (~200k) and thus require time to
      # evaluate, you may choose to use only a fraction of the available samples
      val = val[:: args.val_keep_quantity]
  
      # Number of unique words that are found in the dataset
      vocab_size = len(train.vocab)
  
      # Instantiate the model to be trained either with LSTM layers or with
      # NStepLSTM layers
      model = ImageCaptionModel(
          vocab_size, dropout_ratio=args.dropout_ratio, rnn=args.rnn
      )
      model.to_device(device)
  
      def transform(in_data):
          # Called for each sample and applies necessary preprocessing to the
          # image such as resizing and normalizing
          img, caption = in_data
          img = model.prepare(img)
          return img, caption
  
      # We need to preprocess the images since their sizes may vary (and the
      # model requires that they have the exact same fixed size)
      train = TransformDataset(train, transform)
      val = TransformDataset(val, transform)
  
      train_iter = iterators.MultiprocessIterator(
          train, args.batch_size, shared_mem=700000
      )
      val_iter = chainer.iterators.MultiprocessIterator(
          val,
          args.batch_size,
          repeat=False,
          shuffle=False,
          shared_mem=700000,
      )
  
      optimizer = optimizers.Adam()
      optimizer.setup(model)
  
      @chainer.dataset.converter()
      def converter(batch, device):
          # The converted receives a batch of input samples any may modify it if
          # necessary. In our case, we need to align the captions depending on if
          # we are using LSTM layers of NStepLSTM layers in the model.
          if args.rnn == "lstm":
              max_caption_length = args.max_caption_length
          elif args.rnn == "nsteplstm":
              max_caption_length = None
          else:
              raise ValueError("Invalid RNN type.")
          return datasets.converter(
              batch, device, max_caption_length=max_caption_length
          )
  
      updater = training.updater.StandardUpdater(
          train_iter,
          optimizer=optimizer,
          device=device,
          converter=converter,
      )
  
      trainer = training.Trainer(
          updater,
          out=args.out,
          stop_trigger=(args.max_iters, "iteration"),
      )
      trainer.extend(
          extensions.Evaluator(
              val_iter,
              target=model,
              converter=converter,
              device=device,
          ),
          trigger=(args.val_iter, "iteration"),
      )
      trainer.extend(
          extensions.LogReport(
              ["main/loss", "validation/main/loss"],
              trigger=(args.log_iter, "iteration"),
          )
      )
      trainer.extend(
          extensions.PlotReport(
              ["main/loss", "validation/main/loss"],
              trigger=(args.log_iter, "iteration"),
          )
      )
      trainer.extend(
          extensions.PrintReport(
              [
                  "elapsed_time",
                  "epoch",
                  "iteration",
                  "main/loss",
                  "validation/main/loss",
              ]
          ),
          trigger=(args.log_iter, "iteration"),
      )
  
      # Save model snapshots so that later on, we can load them and generate new
      # captions for any image. This can be done in the `predict.py` script
      trainer.extend(
          extensions.snapshot(filename="snapshot_{.updater.iteration}"),
          trigger=(args.snapshot_iter, "iteration"),
      )
      trainer.extend(
          extensions.snapshot_object(
              model, "model_{.updater.iteration}"
          ),
          trigger=(args.snapshot_iter, "iteration"),
      )
      trainer.extend(extensions.ProgressBar())
  
      if args.resume is not None:
          chainer.serializers.load_npz(args.resume, trainer)
      trainer.run()
  
file_path: chainer/examples/image_captioning/train.py
imports: import argparse
  import chainer
  from chainer.datasets import TransformDataset
  from chainer import iterators
  from chainer import optimizers
  from chainer import training
  from chainer.training import extensions
  import datasets
  from model import ImageCaptionModel
  import matplotlib
label_desc: Use image node_id 1 for calling a global function with example usage: main() and returns: img,  caption, datasets