code: def add_jpeg_decoding(module_spec):
      """Adds operations that perform JPEG decoding and resizing to the graph..
  
      Args:
        module_spec: The hub.ModuleSpec for the image module being used.
  
      Returns:
        Tensors for the node to feed JPEG data into, and the output of the
          preprocessing steps.
      """
      input_height, input_width = hub.get_expected_image_size(
          module_spec
      )
      input_depth = hub.get_num_image_channels(module_spec)
      jpeg_data = tf.placeholder(tf.string, name="DecodeJPGInput")
      decoded_image = tf.image.decode_jpeg(
          jpeg_data, channels=input_depth
      )
      # Convert from full range of uint8 to range [0,1] of float32.
      decoded_image_as_float = tf.image.convert_image_dtype(
          decoded_image, tf.float32
      )
      decoded_image_4d = tf.expand_dims(decoded_image_as_float, 0)
      resize_shape = tf.stack([input_height, input_width])
      resize_shape_as_int = tf.cast(resize_shape, dtype=tf.int32)
      resized_image = tf.image.resize_bilinear(
          decoded_image_4d, resize_shape_as_int
      )
      return jpeg_data, resized_image
  
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 21 for calling a global function with example usage: add_jpeg_decoding(module_spec) and returns: jpeg_data,  resized_image