class_docstr: dnn MNIST
class_docstr_tok: ["dnn","MNIST"]
code: def make_core_network(network):
      dense1 = tflearn.fully_connected(
          network,
          64,
          activation="tanh",
          regularizer="L2",
          weight_decay=0.001,
          name="dense1",
      )
      dropout1 = tflearn.dropout(dense1, 0.8)
      dense2 = tflearn.fully_connected(
          dropout1,
          64,
          activation="tanh",
          regularizer="L2",
          weight_decay=0.001,
          name="dense2",
      )
      dropout2 = tflearn.dropout(dense2, 0.8)
      softmax = tflearn.fully_connected(
          dropout2, 10, activation="softmax", name="softmax"
      )
      return softmax
  
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 Model2 obj's underlying member method code with example usage: obj.make_core_network(network) and returns: softmax