class_docstr: Combination of two networks
class_docstr_tok: ["Combination","of","two","networks"]
code: def __init__(self):
      inputs = tflearn.input_data(shape=[None, 784], name="input")
  
      with tf.variable_scope("scope1") as scope:
          net_conv = Model1.make_core_network(inputs)  # shape (?, 10)
      with tf.variable_scope("scope2") as scope:
          net_dnn = Model2.make_core_network(inputs)  # shape (?, 10)
  
      network = tf.concat(
          [net_conv, net_dnn], 1, name="concat"
      )  # shape (?, 20)
      network = tflearn.fully_connected(
          network, 10, activation="softmax"
      )
      network = regression(
          network,
          optimizer="adam",
          learning_rate=0.01,
          loss="categorical_crossentropy",
          name="target",
      )
  
      self.model = tflearn.DNN(network, tensorboard_verbose=0)
  
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 1 to create a new Model12 object from inherited base classes: object with example: obj = Model12()