code: def build_eval_session(module_spec, class_count):
      """Builds an restored eval session without train operations for exporting.
  
      Args:
        module_spec: The hub.ModuleSpec for the image module being used.
        class_count: Number of classes
  
      Returns:
        Eval session containing the restored eval graph.
        The bottleneck input, ground truth, eval step, and prediction tensors.
      """
      # If quantized, we need to create the correct eval graph for exporting.
      (
          eval_graph,
          bottleneck_tensor,
          resized_input_tensor,
          wants_quantization,
      ) = create_module_graph(module_spec)
  
      eval_sess = tf.Session(graph=eval_graph)
      with eval_graph.as_default():
          # Add the new layer for exporting.
          (
              _,
              _,
              bottleneck_input,
              ground_truth_input,
              final_tensor,
          ) = add_final_retrain_ops(
              class_count,
              FLAGS.final_tensor_name,
              bottleneck_tensor,
              wants_quantization,
              is_training=False,
          )
  
          # Now we need to restore the values from the training graph to the eval
          # graph.
          tf.train.Saver().restore(eval_sess, FLAGS.checkpoint_path)
  
          evaluation_step, prediction = add_evaluation_step(
              final_tensor, ground_truth_input
          )
  
      return (
          eval_sess,
          resized_input_tensor,
          bottleneck_input,
          ground_truth_input,
          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 18 for calling a global function with example usage: build_eval_session(module_spec, class_count) and returns: eval_sess,  resized_input_tensor,  bottleneck_input,  ground_truth_input,  evaluation_step,  prediction