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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def visualize( logdir, outdir, num_agents, num_episodes, checkpoint=None, env_processes=True):
 """Recover checkpoint and render videos from it. Args: logdir: Logging directory of the trained algorithm. outdir: Directory to store rendered videos in. num_agents: Number of environments to simulate in parallel. num_episodes: Total number of episodes to simulate. checkpoint: Checkpoint name to load; defaults to most recent. env_processes: Whether to step environments in separate processes. """

  config = utility.load_config(logdir)
  with tf.device('/cpu:0'):
    batch_env = utility.define_batch_env(
        lambda: _create_environment(config, outdir),
        num_agents, env_processes)
    graph = utility.define_simulation_graph(
        batch_env, config.algorithm, config)
    total_steps = num_episodes * config.max_length
    loop = _define_loop(graph, total_steps)
  saver = utility.define_saver(
      exclude=(r'.*_temporary.*', r'global_step'))
  sess_config = tf.ConfigProto(allow_soft_placement=True)
  sess_config.gpu_options.allow_growth = True
  with tf.Session(config=sess_config) as sess:
    utility.initialize_variables(
        sess, saver, config.logdir, checkpoint, resume=True)
    for unused_score in loop.run(sess, saver, total_steps):
      pass
  batch_env.close()