code: def main():
      parser = argparse.ArgumentParser(
          description="Chainer example: End-to-end memory networks"
      )
      parser.add_argument(
          "TRAIN_DATA",
          help="Path to training data in bAbI dataset "
          '(e.g. "qa1_single-supporting-fact_train.txt")',
      )
      parser.add_argument(
          "TEST_DATA",
          help="Path to test data in bAbI dataset "
          '(e.g. "qa1_single-supporting-fact_test.txt")',
      )
      parser.add_argument(
          "--model",
          "-m",
          default="model",
          help="Model directory where it stores trained model",
      )
      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=100,
          help="Number of sweeps over the dataset to train",
      )
      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(
          "--unit", "-u", type=int, default=20, help="Number of units"
      )
      parser.add_argument(
          "--hop", "-H", type=int, default=3, help="Number of hops"
      )
      parser.add_argument(
          "--max-memory",
          type=int,
          default=50,
          help="Maximum number of memory",
      )
      parser.add_argument(
          "--sentence-repr",
          choices=["bow", "pe"],
          default="bow",
          help="Sentence representation. "
          'Select from BoW ("bow") or position encoding ("pe")',
      )
      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
          )
  
      train(args.TRAIN_DATA, args.TEST_DATA, args)
  
file_path: chainer/examples/memnn/train_memnn.py
imports: import argparse
  import collections
  import warnings
  import chainer
  from chainer.training import extensions
  import numpy
  import babi
  import memnn
label_desc: Use image node_id 2 for calling a global function with example usage: main() without return types