code: def build_graph(self, image, mode):
      assert mode == "init_model" or mode == "reuse_model"
      self.conv_1 = self.conv_layer(
          1, image, 16, 3, 1, "Variable:0", "Variable_1:0", mode=mode
      )
      self.pool_2 = self.pooling_layer(2, self.conv_1, 2, 2, mode=mode)
      self.conv_3 = self.conv_layer(
          3,
          self.pool_2,
          32,
          3,
          1,
          "Variable_2:0",
          "Variable_3:0",
          mode=mode,
      )
      self.pool_4 = self.pooling_layer(4, self.conv_3, 2, 2, mode=mode)
      self.conv_5 = self.conv_layer(
          5,
          self.pool_4,
          64,
          3,
          1,
          "Variable_4:0",
          "Variable_5:0",
          mode=mode,
      )
      self.pool_6 = self.pooling_layer(6, self.conv_5, 2, 2, mode=mode)
      self.conv_7 = self.conv_layer(
          7,
          self.pool_6,
          128,
          3,
          1,
          "Variable_6:0",
          "Variable_7:0",
          mode=mode,
      )
      self.pool_8 = self.pooling_layer(8, self.conv_7, 2, 2, mode=mode)
      self.conv_9 = self.conv_layer(
          9,
          self.pool_8,
          256,
          3,
          1,
          "Variable_8:0",
          "Variable_9:0",
          mode=mode,
      )
      self.pool_10 = self.pooling_layer(
          10, self.conv_9, 2, 2, mode=mode
      )
      self.conv_11 = self.conv_layer(
          11,
          self.pool_10,
          512,
          3,
          1,
          "Variable_10:0",
          "Variable_11:0",
          mode=mode,
      )
      self.pool_12 = self.pooling_layer(
          12, self.conv_11, 2, 2, mode=mode
      )
      self.conv_13 = self.conv_layer(
          13,
          self.pool_12,
          1024,
          3,
          1,
          "Variable_12:0",
          "Variable_13:0",
          mode=mode,
      )
      self.conv_14 = self.conv_layer(
          14,
          self.conv_13,
          1024,
          3,
          1,
          "Variable_14:0",
          "Variable_15:0",
          mode=mode,
      )
      self.conv_15 = self.conv_layer(
          15,
          self.conv_14,
          1024,
          3,
          1,
          "Variable_16:0",
          "Variable_17:0",
          mode=mode,
      )
      self.fc_16 = self.fc_layer(
          16,
          self.conv_15,
          256,
          "Variable_18:0",
          "Variable_19:0",
          flat=True,
          linear=False,
          mode=mode,
      )
      self.fc_17 = self.fc_layer(
          17,
          self.fc_16,
          4096,
          "Variable_20:0",
          "Variable_21:0",
          flat=False,
          linear=False,
          mode=mode,
      )
      # skip dropout_18
      self.fc_19 = self.fc_layer(
          19,
          self.fc_17,
          1470,
          "Variable_22:0",
          "Variable_23:0",
          flat=False,
          linear=True,
          mode=mode,
      )
  
      self.c = tf.reshape(
          self.fc_19[:, 0:980],
          (self.fc_19[:, 0:980].shape.as_list()[0], 7, 7, 20),
      )
      self.s = tf.reshape(
          self.fc_19[:, 980:1078],
          (self.fc_19[:, 980:1078].shape.as_list()[0], 7, 7, 2),
      )
  
      self.p1 = tf.multiply(self.c[:, :, :, 14], self.s[:, :, :, 0])
      self.p2 = tf.multiply(self.c[:, :, :, 14], self.s[:, :, :, 1])
      self.p = tf.stack([self.p1, self.p2], axis=0)
      self.batch_p = tf.reduce_max(self.p, 2)
      self.Ctarget = tf.reduce_sum(self.batch_p)
      # self.Ctarget = tf.reduce_sum(self.p)
  
      return self.Ctarget
  
imports: from __future__ import division
  from __future__ import print_function
  from builtins import str
  from builtins import range
  from past.utils import old_div
  from builtins import object
  import tensorflow
  import cv2
  import numpy
  import os
  import sys
  import argparse
  import pdb
  from tqdm import tqdm
label_desc: Use image node_id 2 for calling the YOLO_tiny_model_updated obj's underlying member method code with example usage: obj.build_graph(image, mode) and returns: self