code: def main():
      archs = {
          "alex": alex.Alex,
          "googlenet": googlenet.GoogLeNet,
          "googlenetbn": googlenetbn.GoogLeNetBN,
          "nin": nin.NIN,
          "resnet50": resnet50.ResNet50,
          "resnext50": resnext50.ResNeXt50,
      }
  
      parser = argparse.ArgumentParser(
          description="Learning convnet from ILSVRC2012 dataset"
      )
      parser.add_argument(
          "train", help="Path to training image-label list file"
      )
      parser.add_argument(
          "val", help="Path to validation image-label list file"
      )
      parser.add_argument(
          "--arch",
          "-a",
          choices=archs.keys(),
          default="nin",
          help="Convnet architecture",
      )
      parser.add_argument(
          "--batchsize",
          "-B",
          type=int,
          default=32,
          help="Learning minibatch size",
      )
      parser.add_argument(
          "--epoch",
          "-E",
          type=int,
          default=10,
          help="Number of epochs to train",
      )
      parser.add_argument(
          "--devices",
          "-d",
          type=str,
          nargs="*",
          default=["0", "1", "2", "3"],
          help="Device specifiers. Either ChainerX device "
          "specifiers or integers. If non-negative integer, "
          "CuPy arrays with specified device id are used. If "
          "negative integer, NumPy arrays are used",
      )
      parser.add_argument(
          "--initmodel", help="Initialize the model from given file"
      )
      parser.add_argument(
          "--loaderjob",
          "-j",
          type=int,
          help="Number of parallel data loading processes",
      )
      parser.add_argument(
          "--mean",
          "-m",
          default="mean.npy",
          help="Mean file (computed by compute_mean.py)",
      )
      parser.add_argument(
          "--resume",
          "-r",
          default="",
          help="Initialize the trainer from given file",
      )
      parser.add_argument(
          "--out", "-o", default="result", help="Output directory"
      )
      parser.add_argument(
          "--root",
          "-R",
          default=".",
          help="Root directory path of image files",
      )
      parser.add_argument(
          "--val_batchsize",
          "-b",
          type=int,
          default=250,
          help="Validation minibatch size",
      )
      parser.add_argument("--test", action="store_true")
      parser.set_defaults(test=False)
      group = parser.add_argument_group("deprecated arguments")
      group.add_argument(
          "--gpus",
          "-g",
          dest="devices",
          type=int,
          nargs="?",
          const=0,
          help="GPU IDs (negative value indicates CPU)",
      )
      args = parser.parse_args()
  
      devices = tuple([chainer.get_device(d) for d in args.devices])
      if any(device.xp is chainerx for device in devices):
          sys.stderr.write(
              "This example does not support ChainerX devices.\n"
          )
          sys.exit(1)
  
      # Initialize the model to train
      model = archs[args.arch]()
      if args.initmodel:
          print("Load model from {}".format(args.initmodel))
          chainer.serializers.load_npz(args.initmodel, model)
  
      # Load the datasets and mean file
      mean = np.load(args.mean)
      train = PreprocessedDataset(
          args.train, args.root, mean, model.insize
      )
      val = PreprocessedDataset(
          args.val, args.root, mean, model.insize, False
      )
      # These iterators load the images with subprocesses running in parallel to
      # the training/validation.
      train_iters = [
          chainer.iterators.MultiprocessIterator(
              i, args.batchsize, n_processes=args.loaderjob
          )
          for i in chainer.datasets.split_dataset_n_random(
              train, len(devices)
          )
      ]
      val_iter = chainer.iterators.MultiprocessIterator(
          val,
          args.val_batchsize,
          repeat=False,
          n_processes=args.loaderjob,
      )
  
      # Set up an optimizer
      optimizer = chainer.optimizers.MomentumSGD(lr=0.01, momentum=0.9)
      optimizer.setup(model)
  
      # Set up a trainer
      updater = updaters.MultiprocessParallelUpdater(
          train_iters, optimizer, devices=devices
      )
      trainer = training.Trainer(
          updater, (args.epoch, "epoch"), args.out
      )
  
      if args.test:
          val_interval = 5, "epoch"
          log_interval = 1, "epoch"
      else:
          val_interval = 100000, "iteration"
          log_interval = 1000, "iteration"
  
      trainer.extend(
          extensions.Evaluator(val_iter, model, device=devices[0]),
          trigger=val_interval,
      )
      trainer.extend(extensions.DumpGraph("main/loss"))
      trainer.extend(extensions.snapshot(), trigger=val_interval)
      trainer.extend(
          extensions.snapshot_object(
              model, "model_iter_{.updater.iteration}"
          ),
          trigger=val_interval,
      )
      # Be careful to pass the interval directly to LogReport
      # (it determines when to emit log rather than when to read observations)
      trainer.extend(extensions.LogReport(trigger=log_interval))
      trainer.extend(extensions.observe_lr(), trigger=log_interval)
      trainer.extend(
          extensions.PrintReport(
              [
                  "epoch",
                  "iteration",
                  "main/loss",
                  "validation/main/loss",
                  "main/accuracy",
                  "validation/main/accuracy",
                  "lr",
              ]
          ),
          trigger=log_interval,
      )
      trainer.extend(extensions.ProgressBar(update_interval=2))
  
      if args.resume:
          chainer.serializers.load_npz(args.resume, trainer)
  
      trainer.run()
  
file_path: chainer/examples/imagenet/train_imagenet_data_parallel.py
imports: import argparse
  import sys
  import numpy
  import chainer
  from chainer import training
  from chainer.training import extensions
  from chainer.training import updaters
  import chainerx
  from dataset_util import PreprocessedDataset
  import alex
  import googlenet
  import googlenetbn
  import nin
  import resnet50
  import resnext50
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types