class_docstr: Base Class for neural networks written in tensorflow used to abstract across model
  architectures. It defines the computational graph and initialize a session to run the graph.
  
  Make placeholders for input and targets
  self.data = tf.placeholder()
  self.target = tf.placeholder()
  
  Make dictionaries for weights and biases
  self.weights = {
      'conv' : tf.Variable()
      'dense0' : tf.Variable()
  }
  self.biases = {
      'conv' : tf.Variable()
      'dense0' : tf.Variable()
  }
  
  Make the graph
  conv = tf.nn.conv1d(self.data, self.weights['conv'], ..)
  dense = tf.add(tf.matmul() + self.bias())
  ...
  
  Make loss_op with the loss and train_op with the optimizer
  loss_op =
  train_op =
  
  Define Session
  self.sess = tf.Session()
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docstr: Sets the optimizer to learn at the specified learning rate or using a learning rate scheduler.
docstr_tok: ["Sets","the","optimizer","to","learn","at","the","specified","learning","rate","or","using","a","learning","rate","scheduler","."]
code: def export_weights(self):
      """
      Sets the optimizer to learn at the specified learning rate or using a learning rate scheduler.
      """
      raise NotImplementedError
  
file_path: turicreate/src/python/turicreate/toolkits/_tf_model.py
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import abc
  import six
label_desc: Use image node_id 4 for calling the TensorFlowModel obj's underlying member method code with example usage: obj.export_weights() without return types