code: def add_evaluation_step(result_tensor, ground_truth_tensor):
      """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).
      """
      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
  
imports: from absl import logging
  import argparse
  import collections
  from datetime import datetime
  import hashlib
  import os.path
  import random
  import re
  import sys
  import numpy
  import tensorflow
  import tensorflow_hub
  from tensorflow.contrib import quantize
label_desc: Use image node_id 16 for calling a global function with example usage: add_evaluation_step(result_tensor, ground_truth_tensor) and returns: evaluation_step,  prediction