code: def file_to_tensor(file_path):
      image_string = tf.read_file(file_path)
      image = tf.image.decode_image(image_string, channels=3)
  
      image.set_shape([None, None, None])
      image = tf.image.resize_images(image, [image_size, image_size])
      image = tf.divide(tf.subtract(image, [0]), [255])
      image.set_shape([image_size, image_size, num_channel])
      return image
  
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 3 for calling a global function with example usage: file_to_tensor(file_path) and returns: image