code: def eval_example_images(
      model_name, ckpt_dir, image_files, labels_map_file
  ):
      """Eval a list of example images.
  
      Args:
        model_name: str. The name of model to eval.
        ckpt_dir: str. Checkpoint directory path.
        image_files: List[str]. A list of image file paths.
        labels_map_file: str. The labels map file path.
  
      Returns:
        A tuple (pred_idx, and pred_prob), where pred_idx is the top 5 prediction
        index and pred_prob is the top 5 prediction probability.
      """
      eval_ckpt_driver = EvalCkptDriver(model_name)
      classes = json.loads(tf.gfile.Open(labels_map_file).read())
      pred_idx, pred_prob = eval_ckpt_driver.run_inference(
          ckpt_dir, image_files, [0] * len(image_files)
      )
      for i in range(len(image_files)):
          print("predicted class for image {}: ".format(image_files[i]))
          for j, idx in enumerate(pred_idx[i]):
              print(
                  "  -> top_{} ({:4.2f}%): {}  ".format(
                      j, pred_prob[i][j] * 100, classes[str(idx)]
                  )
              )
      return pred_idx, pred_prob
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import json
  import sys
  from absl import app
  from absl import flags
  import numpy
  import tensorflow
  import efficientnet_builder
  import preprocessing
label_desc: Use image node_id 1 for calling a global function with example usage: eval_example_images(model_name, ckpt_dir, image_files, labels_map_file) and returns: pred_idx,  pred_prob