class_docstr: convnet MNIST
class_docstr_tok: ["convnet","MNIST"]
code: def make_core_network(network):
      network = tflearn.reshape(
          network, [-1, 28, 28, 1], name="reshape"
      )
      network = conv_2d(
          network, 32, 3, activation="relu", regularizer="L2"
      )
      network = max_pool_2d(network, 2)
      network = local_response_normalization(network)
      network = conv_2d(
          network, 64, 3, activation="relu", regularizer="L2"
      )
      network = max_pool_2d(network, 2)
      network = local_response_normalization(network)
      network = fully_connected(network, 128, activation="tanh")
      network = dropout(network, 0.8)
      network = fully_connected(network, 256, activation="tanh")
      network = dropout(network, 0.8)
      network = fully_connected(network, 10, activation="softmax")
      return network
  
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 2 for calling the Model1 obj's underlying member method code with example usage: obj.make_core_network(network) and returns: network