code: def demonstrate_combined_model1_and_model2_network():
      print(
          "=" * 60
          + " Demonstrate loading weights from model1 and model2 into new mashup network model12"
      )
      print("-" * 40 + " Creating mashup of model1 and model2 networks")
      tf.reset_default_graph()
      m12 = Model12()
      print("-" * 60 + " Loading model1 and model2 weights into mashup")
      m12.load_from_two("model1.tfl", "model2.tfl")
      print("-" * 60 + " Training mashup")
      m12.train(X, Y, testX, testY, 1)
      print("-" * 60 + " Saving mashup weights")
      m12.model.save("model12.tfl")
      print("-" * 60 + " Done")
  
imports: from __future__ import division, print_function, absolute_import
  import re
  import tflearn
  import tensorflow.compat.v1
  import tflearn.datasets.mnist
  from tflearn.layers.core import input_data, dropout, fully_connected
  from tflearn.layers.conv import conv_2d, max_pool_2d
  from tflearn.layers.normalization import local_response_normalization
  from tflearn.layers.estimator import regression
label_desc: Use image node_id 7 for calling a global function with example usage: demonstrate_combined_model1_and_model2_network() without return types