code: def add_evaluation_step(result_tensor, ground_truth_tensor):
    
    with tf.name_scope('accuracy'):
      with tf.name_scope('correct_prediction'):
        prediction = tf.argmax(result_tensor, 1)
        correct_prediction = tf.equal(prediction, ground_truth_tensor)
      with tf.name_scope('accuracy'):
        evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    tf.summary.scalar('accuracy', evaluation_step)
    return evaluation_step, prediction
docs: Inserts the operations we need to evaluate the accuracy of our results.
  
    Args:
      result_tensor: The new final node that produces results.
      ground_truth_tensor: The node we feed ground truth data
      into.
  
    Returns:
      Tuple of (evaluation step, prediction).