old_contents: from __future__ import print_function
  import numpy as np
  import tensorflow as tf
  
  import argparse
  import time
  import os
  from six.moves import cPickle
  
  from utils import TextLoader
  from model import Model
  
  def main():
      parser = argparse.ArgumentParser()
      parser.add_argument('--save_dir', type=str, default='save',
                         help='model directory to load stored checkpointed models from')
      parser.add_argument('-n', type=int, default=200,
                         help='number of words to sample')
      parser.add_argument('--prime', type=str, default=' ',
                         help='prime text')
      parser.add_argument('--pick', type=int, default=1,
                         help='1 = weighted pick, 2 = beam search pick')
      parser.add_argument('--sample', type=int, default=1,
                         help='0 to use max at each timestep, 1 to sample at each timestep, 2 to sample on spaces')
      parser.add_argument('--width', type=int, default=4,
                         help='width of the beam search')
  
      args = parser.parse_args()
      sample(args)
  
  def sample(args):
      with open(os.path.join(args.save_dir, 'config.pkl'), 'rb') as f:
          saved_args = cPickle.load(f)
      with open(os.path.join(args.save_dir, 'words_vocab.pkl'), 'rb') as f:
          words, vocab = cPickle.load(f)
      model = Model(saved_args, True)
      with tf.Session() as sess:
          tf.global_variables_initializer().run()
          saver = tf.train.Saver(tf.global_variables())
          ckpt = tf.train.get_checkpoint_state(args.save_dir)
          if ckpt and ckpt.model_checkpoint_path:
              saver.restore(sess, ckpt.model_checkpoint_path)
              print(model.sample(sess, words, vocab, args.n, args.prime, args.sample, args.pick, args.width))
  
  if __name__ == '__main__':
      main()
  
new_contents: from __future__ import print_function
  import numpy as np
  import tensorflow as tf
  
  import argparse
  import time
  import os
  from six.moves import cPickle
  
  from utils import TextLoader
  from model import Model
  
  def main():
      parser = argparse.ArgumentParser()
      parser.add_argument('--save_dir', type=str, default='save',
                         help='model directory to load stored checkpointed models from')
      parser.add_argument('-n', type=int, default=200,
                         help='number of words to sample')
      parser.add_argument('--prime', type=str, default=' ',
                         help='prime text')
      parser.add_argument('--pick', type=int, default=1,
                         help='1 = weighted pick, 2 = beam search pick')
      parser.add_argument('--width', type=int, default=4,
                         help='width of the beam search')
      parser.add_argument('--sample', type=int, default=1,
                         help='0 to use max at each timestep, 1 to sample at each timestep, 2 to sample on spaces')
  
      args = parser.parse_args()
      sample(args)
  
  def sample(args):
      with open(os.path.join(args.save_dir, 'config.pkl'), 'rb') as f:
          saved_args = cPickle.load(f)
      with open(os.path.join(args.save_dir, 'words_vocab.pkl'), 'rb') as f:
          words, vocab = cPickle.load(f)
      model = Model(saved_args, True)
      with tf.Session() as sess:
          tf.global_variables_initializer().run()
          saver = tf.train.Saver(tf.global_variables())
          ckpt = tf.train.get_checkpoint_state(args.save_dir)
          if ckpt and ckpt.model_checkpoint_path:
              saver.restore(sess, ckpt.model_checkpoint_path)
              print(model.sample(sess, words, vocab, args.n, args.prime, args.sample, args.pick, args.width))
  
  if __name__ == '__main__':
      main()
  
subject: Adjust placement of lines