code: def add_jpeg_decoding(module_spec):
    
    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
docs: 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.