old_contents: #!/usr/bin/env python3
  
  """Test/benchmark deep_dream.py."""
  
  from pathlib import Path
  import time
  
  import click
  from PIL import Image
  
  import deep_dream as dd
  import utils
  
  utils.setup_traceback()
  
  
  @click.command()
  @click.option('--cpu-workers', default=0, help='The number of CPU workers to start.')
  @click.option('--gpus', type=utils.List(int, 'integer'), default='',
                help='The CUDA device IDs to use.')
  @click.option('--max-tile-size', default=512, help='The maximum dimension of a tile.')
  def main(cpu_workers=None, gpus=None, max_tile_size=None):
      pwd = Path(__file__).parent
      cnn = dd.CNN(dd.GOOGLENET_BVLC, cpu_workers=cpu_workers, gpus=gpus)
      input_img = Image.open(str(pwd/'kodim/img0022.jpg')).resize((1536, 1024), Image.LANCZOS)
      print('Input image classes:')
      for category in cnn.classify(input_img, 5):
          print('%.3f %s' % category)
      cnn.dream(input_img, 'inception_3a/3x3', min_size=129, n=1, max_tile_size=max_tile_size)
      time_0 = time.perf_counter()
      img = cnn.dream(input_img, 'inception_3a/3x3', min_size=129, n=10, step_size=1,
                      max_tile_size=max_tile_size)
      time_1 = time.perf_counter()
      print('Input image classes:')
      for category in cnn.classify(img, 5):
          print('%.3f %s' % category)
      print('Time taken: %.3f s' % (time_1-time_0))
      dd.to_image(img).save('test_output.png')
      print('Saved to test_output.png.')
  
  if __name__ == '__main__':
      main()
  
new_contents: #!/usr/bin/env python3
  
  """Test/benchmark deep_dream.py."""
  
  from pathlib import Path
  import time
  
  import click
  from PIL import Image
  
  import deep_dream as dd
  import utils
  
  utils.setup_traceback()
  
  
  @click.command()
  @click.option('--cpu-workers', default=0, help='The number of CPU workers to start.')
  @click.option('--gpus', type=utils.List(int, 'integer'), default='',
                help='The CUDA device IDs to use.')
  @click.option('--max-tile-size', default=512, help='The maximum dimension of a tile.')
  def main(cpu_workers=None, gpus=None, max_tile_size=None):
      """Test/benchmark deep_dream.py."""
      pwd = Path(__file__).parent
      cnn = dd.CNN(dd.GOOGLENET_BVLC, cpu_workers=cpu_workers, gpus=gpus)
      input_img = Image.open(str(pwd/'kodim/img0022.jpg')).resize((1536, 1024), Image.LANCZOS)
      print('Input image classes:')
      for category in cnn.classify(input_img, 5):
          print('%.3f %s' % category)
      cnn.dream(input_img, 'inception_3a/3x3', min_size=129, n=1, max_tile_size=max_tile_size)
      time_0 = time.perf_counter()
      img = cnn.dream(input_img, 'inception_3a/3x3', min_size=129, n=10, step_size=1,
                      max_tile_size=max_tile_size)
      time_1 = time.perf_counter()
      print('Input image classes:')
      for category in cnn.classify(img, 5):
          print('%.3f %s' % category)
      print('Time taken: %.3f s' % (time_1-time_0))
      dd.to_image(img).save('test_output.png')
      print('Saved to test_output.png.')
  
  if __name__ == '__main__':
      main()
  
subject: Add main() docstring in deep_dream_test.py.