code: def run(mini_batch):
      result_list = []
      for file_path in mini_batch:
          test_image = file_to_tensor(file_path)
          out = g_tf_sess.run(test_image)
          result = g_tf_sess.run(
              probabilities, feed_dict={input_images: [out]}
          )
          result_list.append(
              os.path.basename(file_path) + ": " + label_dict[result[0]]
          )
      return result_list
  
file_path: MachineLearningNotebooks/tutorials/machine-learning-pipelines-advanced/scripts/batch_scoring.py
imports: import os
  import argparse
  import datetime
  import time
  import tensorflow.compat.v1
  from math import ceil
  import numpy
  import sys
  import shutil
  import subprocess
  import tf_slim
  from azureml.core import Run
  from azureml.core.model import Model
  from azureml.core.dataset import Dataset
  from tf_slim import nets
label_desc: Use image node_id 4 for calling a global function with example usage: run(mini_batch) and returns: result_list