class_docstr: Just a simple model, which applies the Laplacian-operation to images to showcase
  the usage of variables, and alternating the inference-graph later.
class_docstr_tok: ["Just","a","simple","model",",","which","applies","the","Laplacian-operation","to","images","to","showcase","the","usage","of","variables",",","and","alternating","the","inference-graph","later","."]
code: def make_prediction(self, img):
      img = tf.cast(img, tf.float32)
      img = tf.image.rgb_to_grayscale(img)
  
      k = tf.get_variable(
          "filter",
          dtype=tf.float32,
          initializer=[
              [[[0.0]], [[1.0]], [[0.0]]],
              [[[1.0]], [[-4.0]], [[1.0]]],
              [[[0.0]], [[1.0]], [[0.0]]],
          ],
      )
      prediction_img = tf.nn.conv2d(
          img, k, strides=[1, 1, 1, 1], padding="SAME"
      )
      return prediction_img
  
imports: import argparse
  import cv2
  from tensorpack import tfv1
  from tensorpack import *
  from tensorpack.tfutils.export import ModelExporter
label_desc: Use image node_id 2 for calling the Model obj's underlying member method code with example usage: obj.make_prediction(img) and returns: prediction_img