class_docstr: convnet MNIST
class_docstr_tok: ["convnet","MNIST"]
code: def __init__(self):
      network = tflearn.input_data(shape=[None, 784], name="input")
      network = self.make_core_network(network)
      network = regression(
          network,
          optimizer="adam",
          learning_rate=0.01,
          loss="categorical_crossentropy",
          name="target",
      )
  
      model = tflearn.DNN(network, tensorboard_verbose=0)
      self.model = model
  
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 Model1 object from inherited base classes: object with example: obj = Model1()