human
Complete the below
def main(_argv):

    ''''Program entry point.'
    '''
 Write me a the Python code you requested?
assistant
#Here is the Python code you requested.
    if FLAGS.config_path:
        with gfile.GFile(FLAGS.config_path) as config_file:
            config_flags = yaml.load(config_file)
            for (flag_key, flag_value) in config_flags.items():
                setattr(FLAGS, flag_key, flag_value)
    if isinstance(FLAGS.tasks, string_types):
        FLAGS.tasks = _maybe_load_yaml(FLAGS.tasks)
    if isinstance(FLAGS.input_pipeline, string_types):
        FLAGS.input_pipeline = _maybe_load_yaml(FLAGS.input_pipeline)
    input_pipeline_infer = input_pipeline.make_input_pipeline_from_def(FLAGS.input_pipeline, mode=tf.contrib.learn.ModeKeys.INFER, shuffle=False, num_epochs=1)
    train_options = training_utils.TrainOptions.load(FLAGS.model_dir)
    model_cls = (locate(train_options.model_class) or getattr(models, train_options.model_class))
    model_params = train_options.model_params
    model_params = _deep_merge_dict(model_params, _maybe_load_yaml(FLAGS.model_params))
    model = model_cls(params=model_params, mode=tf.contrib.learn.ModeKeys.INFER)
    hooks = []
    for tdict in FLAGS.tasks:
        if (not (u'params' in tdict)):
            tdict[u'params'] = {}
        task_cls = (locate(tdict[u'class']) or getattr(tasks, tdict[u'class']))
        task = task_cls(tdict[u'params'])
        hooks.append(task)
    (predictions, _, _) = create_inference_graph(model=model, input_pipeline=input_pipeline_infer, batch_size=FLAGS.batch_size)
    saver = tf.train.Saver()
    checkpoint_path = FLAGS.checkpoint_path
    if (not checkpoint_path):
        checkpoint_path = tf.train.latest_checkpoint(FLAGS.model_dir)
    def session_init_op(_scaffold, sess):
        saver.restore(sess, checkpoint_path)
        tf.logging.info(u'Restored    model    from    %s', checkpoint_path)
    scaffold = tf.train.Scaffold(init_fn=session_init_op)
    session_creator = tf.train.ChiefSessionCreator(scaffold=scaffold)
    with tf.train.MonitoredSession(session_creator=session_creator, hooks=hooks) as sess:
        while (not sess.should_stop()):
            sess.run([])