old_contents: import argparse
  import matplotlib.pyplot as plot
  import numpy as np
  
  import chainer
  
  from chainercv.datasets.pascal_voc import voc_utils
  from chainercv.links import FasterRCNNVGG16
  from chainercv import utils
  from chainercv.visualizations import vis_bbox
  
  
  def main():
      parser = argparse.ArgumentParser()
      parser.add_argument('gpu', type=int)
      parser.add_argument('image')
      args = parser.parse_args()
  
      model = FasterRCNNVGG16(n_class=21, pretrained_model='voc07')
      if args.gpu >= 0:
          model.to_gpu(args.gpu)
          chainer.cuda.get_device(args.gpu).use()
  
      img = utils.read_image(args.image, color=True)
      bboxes, labels, scores = model.predict(img[np.newaxis])
      bbox, label, score = bboxes[0], labels[0], scores[0]
  
      vis_bbox(
          img, bbox, label, score, label_names=voc_utils.pascal_voc_labels)
      plot.show()
  
  
  if __name__ == '__main__':
      main()
  
new_contents: import argparse
  import matplotlib.pyplot as plot
  import numpy as np
  
  import chainer
  
  from chainercv.datasets.pascal_voc import voc_utils
  from chainercv.links import FasterRCNNVGG16
  from chainercv import utils
  from chainercv.visualizations import vis_bbox
  
  
  def main():
      parser = argparse.ArgumentParser()
      parser.add_argument('gpu', type=int)
      parser.add_argument('image')
      args = parser.parse_args()
  
      model = FasterRCNNVGG16(pretrained_model='voc07')
      if args.gpu >= 0:
          model.to_gpu(args.gpu)
          chainer.cuda.get_device(args.gpu).use()
  
      img = utils.read_image(args.image, color=True)
      bboxes, labels, scores = model.predict(img[np.newaxis])
      bbox, label, score = bboxes[0], labels[0], scores[0]
  
      vis_bbox(
          img, bbox, label, score, label_names=voc_utils.pascal_voc_labels)
      plot.show()
  
  
  if __name__ == '__main__':
      main()
  
subject: fix argument of demo