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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 evaluate_repeatedly(self, accuracy, num_steps, feed_vars=(), feed_data=None, summary_tag=None, evaluation_times=-1):
 """Runs the evaluation in a loop for `evaluation_times`. On each iteration, `evaluate_model` is called with the supplied arguments. This manages the queue threads itself. Args: accuracy: The metric that is being evaluated. num_steps: The number of steps to run in the evaluator. feed_vars: A list or tuple of the variables that will be fed. feed_data: A generator that produces tuples of the same length as feed_vars. summary_tag: If provided, the final result of each evaluation will be published to this tag. evaluation_times: Run this loop for this many times or forever if it is `-1`. Returns: The final evaluation result from `evaluate_model` if `evaluation_times` ever ends. """

    current_checkpoint = None
    try:
      for i in itertools.count(0):
        # New session each time to reset queues - Yay.
        with self.session() as sess:
          current_checkpoint = self.load_new_checkpoint_when_available(
              sess, current_checkpoint)
          # Create relevant ops before starting queue runners.
          self._run_init_test_vars_op()

          accuracy_result = self.evaluate_model(accuracy,
                                                num_steps,
                                                summary_tag=summary_tag,
                                                print_every=0,
                                                feed_vars=feed_vars,
                                                feed_data=feed_data)
          if not summary_tag:
            print('[%d] %s' % (sess.run(bookkeeper.global_step()),
                               accuracy_result))
          if (i + 1) == evaluation_times:
            return accuracy_result
    finally:
      print('Shutting down')
      sys.stdout.flush()
      self.stop_queues()