old_contents: #!/usr/bin/env python
  
  import argparse
  
  
  def create_parser():
      parser = argparse.ArgumentParser()
      parser.add_argument("--tile-size", default=64, type=int,
                          help="tile the NAIP and training data into NxN tiles with this dimension")
      parser.add_argument("--training-batches", default=100, type=int,
                          help="the number of batches to train the neural net. 100 is good for a dry "
                          "run, but around 5000 is recommended for statistical significance")
      parser.add_argument("--batch-size", default=96, type=int,
                          help="around 100 is a good choice, defaults to 96 because cifar10 does")
      parser.add_argument("--band-list", default=[1, 1, 1, 1], nargs=4, type=int,
                          help="specify which bands to activate (R  G  B  IR). default is "
                          "--bands 1 1 1 1 (which activates all bands)")
      parser.add_argument("--extract-type", default='highway', choices=['highway', 'tennis'],
                          help="the type of feature to identify")
      parser.add_argument("--cache-way-bmp", action='store_true',
                          help="disable this to regenerate way bitmaps each run")
      parser.add_argument("--clear-way-bmp-cache", action='store_true',
                          help="enable this to bust the ay_bmp_cache from previous runs")
      parser.add_argument("--render-results", action='store_true',
                          help="disable to not print data/predictions to JPEG")
      parser.add_argument("--model", default='mnist', choices=['mnist', 'cifar10'],
                          help="the model to use")
      return parser
  
  
  def run_analysis(*args, **kwargs):  # TODO -- replace this with the actual run_analysis function
      print(kwargs)
  
  
  def main():
      parser = create_parser()
      args = parser.parse_args()
  
      run_analysis(
          cache_way_bmp=args.cache_way_bmp,
          clear_way_bmp_cache=args.clear_way_bmp_cache,
          render_results=args.render_results,
          extract_type=args.extract_type,
          model=args.model,
          band_list=args.band_list,
          training_batches=args.training_batches,
          batch_size=args.batch_size,
          tile_size=args.tile_size)
  
  
  if __name__ == "__main__":
      main()
  
new_contents: #!/usr/bin/env python
  
  import argparse
  from src.run_analysis import run_analysis
  
  
  def create_parser():
      parser = argparse.ArgumentParser()
      parser.add_argument("--tile-size", default=64, type=int,
                          help="tile the NAIP and training data into NxN tiles with this dimension")
      parser.add_argument("--training-batches", default=100, type=int,
                          help="the number of batches to train the neural net. 100 is good for a dry "
                          "run, but around 5000 is recommended for statistical significance")
      parser.add_argument("--batch-size", default=96, type=int,
                          help="around 100 is a good choice, defaults to 96 because cifar10 does")
      parser.add_argument("--band-list", default=[1, 1, 1, 1], nargs=4, type=int,
                          help="specify which bands to activate (R  G  B  IR). default is "
                          "--bands 1 1 1 1 (which activates all bands)")
      parser.add_argument("--extract-type", default='highway', choices=['highway', 'tennis'],
                          help="the type of feature to identify")
      parser.add_argument("--cache-way-bmp", action='store_true',
                          help="disable this to regenerate way bitmaps each run")
      parser.add_argument("--clear-way-bmp-cache", action='store_true',
                          help="enable this to bust the ay_bmp_cache from previous runs")
      parser.add_argument("--render-results", action='store_true',
                          help="disable to not print data/predictions to JPEG")
      parser.add_argument("--model", default='mnist', choices=['mnist', 'cifar10'],
                          help="the model to use")
      return parser
  
  
  def main():
      parser = create_parser()
      args = parser.parse_args()
  
      run_analysis(
          cache_way_bmp=args.cache_way_bmp,
          clear_way_bmp_cache=args.clear_way_bmp_cache,
          render_results=args.render_results,
          extract_type=args.extract_type,
          model=args.model,
          band_list=args.band_list,
          training_batches=args.training_batches,
          batch_size=args.batch_size,
          tile_size=args.tile_size)
  
  
  if __name__ == "__main__":
      main()
  
subject: Replace stubbed run_analysis with a real one