code: def main():
      parser = argparse.ArgumentParser(
          description="Chainer example: MNIST"
      )
      parser.add_argument(
          "--batchsize",
          "-b",
          type=int,
          default=100,
          help="Number of images 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(
          "--frequency",
          "-f",
          type=int,
          default=-1,
          help="Frequency of taking a snapshot",
      )
      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",
      )
      parser.add_argument(
          "--resume",
          "-r",
          type=str,
          help="Resume the training from snapshot",
      )
      parser.add_argument(
          "--autoload",
          action="store_true",
          help="Automatically load trainer snapshots in case"
          " of preemption or other temporary system failure",
      )
      parser.add_argument(
          "--unit", "-u", type=int, default=1000, help="Number of units"
      )
      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("# unit: {}".format(args.unit))
      print("# Minibatch-size: {}".format(args.batchsize))
      print("# epoch: {}".format(args.epoch))
      print("")
  
      # Set up a neural network to train
      # Classifier reports softmax cross entropy loss and accuracy at every
      # iteration, which will be used by the PrintReport extension below.
      model = L.Classifier(MLP(args.unit, 10))
      model.to_device(device)
      device.use()
  
      # Setup an optimizer
      optimizer = chainer.optimizers.Adam()
      optimizer.setup(model)
  
      # Load the MNIST dataset
      train, test = chainer.datasets.get_mnist()
  
      train_iter = chainer.iterators.SerialIterator(
          train, args.batchsize
      )
      test_iter = chainer.iterators.SerialIterator(
          test, args.batchsize, repeat=False, shuffle=False
      )
  
      # Set up a trainer
      updater = training.updaters.StandardUpdater(
          train_iter, optimizer, device=device
      )
      trainer = training.Trainer(
          updater, (args.epoch, "epoch"), out=args.out
      )
  
      # Evaluate the model with the test dataset for each epoch
      trainer.extend(
          extensions.Evaluator(test_iter, model, device=device),
          call_before_training=True,
      )
  
      # Dump a computational graph from 'loss' variable at the first iteration
      # The "main" refers to the target link of the "main" optimizer.
      # TODO(niboshi): Temporarily disabled for chainerx. Fix it.
      if device.xp is not chainerx:
          trainer.extend(extensions.DumpGraph("main/loss"))
  
      # Take a snapshot for each specified epoch
      frequency = (
          args.epoch if args.frequency == -1 else max(1, args.frequency)
      )
      # Take a snapshot each ``frequency`` epoch, delete old stale
      # snapshots and automatically load from snapshot files if any
      # files are already resident at result directory.
      trainer.extend(
          extensions.snapshot(n_retains=1, autoload=args.autoload),
          trigger=(frequency, "epoch"),
      )
  
      # Write a log of evaluation statistics for each epoch
      trainer.extend(extensions.LogReport(), call_before_training=True)
  
      # Save two plot images to the result dir
      trainer.extend(
          extensions.PlotReport(
              ["main/loss", "validation/main/loss"],
              "epoch",
              file_name="loss.png",
          ),
          call_before_training=True,
      )
      trainer.extend(
          extensions.PlotReport(
              ["main/accuracy", "validation/main/accuracy"],
              "epoch",
              file_name="accuracy.png",
          ),
          call_before_training=True,
      )
  
      # Print selected entries of the log to stdout
      # Here "main" refers to the target link of the "main" optimizer again, and
      # "validation" refers to the default name of the Evaluator extension.
      # Entries other than 'epoch' are reported by the Classifier link, called by
      # either the updater or the evaluator.
      trainer.extend(
          extensions.PrintReport(
              [
                  "epoch",
                  "main/loss",
                  "validation/main/loss",
                  "main/accuracy",
                  "validation/main/accuracy",
                  "elapsed_time",
              ]
          ),
          call_before_training=True,
      )
  
      # Print a progress bar to stdout
      trainer.extend(extensions.ProgressBar())
  
      if args.resume is not None:
          # Resume from a snapshot (Note: this loaded model is to be
          # overwritten by --autoload option, autoloading snapshots, if
          # any snapshots exist in output directory)
          chainer.serializers.load_npz(args.resume, trainer)
  
      # Run the training
      trainer.run()
  
file_path: chainer/examples/mnist/train_mnist.py
imports: import argparse
  import chainer
  import chainer.functions
  import chainer.links
  from chainer import training
  from chainer.training import extensions
  import chainerx
  import matplotlib
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types