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 build_graph(self, input_img, target_img):
      target_img = tf.cast(target_img, tf.float32)
      target_img = tf.image.rgb_to_grayscale(target_img)
  
      self.prediction_img = tf.identity(
          self.make_prediction(input_img), name="prediction_img"
      )
  
      cost = tf.losses.mean_squared_error(
          target_img,
          self.prediction_img,
          reduction=tf.losses.Reduction.MEAN,
      )
      return tf.identity(cost, name="total_costs")
  
imports: import argparse
  import cv2
  from tensorpack import tfv1
  from tensorpack import *
  from tensorpack.tfutils.export import ModelExporter
label_desc: Use image node_id 3 for calling the Model obj's underlying member method code with example usage: obj.build_graph(input_img, target_img) and returns: tf