docstr: Initializes gradient-based TF optimizers.
docstr_tok: ["Initializes","gradient-based","TF","optimizers","."]
code: def do_optimizer_initializations(self, optimizer):
      """Initializes gradient-based TF optimizers."""
  
      self.optimizer_weights = [optimizer]
  
      opt_library = optimizer.split(":")[0]
      opt_method = optimizer.split(":")[1]
  
      # optimization step
      self.opt, self.optim_step = self.optimizers_options[opt_library](
          opt_method
      )
  
      self.opt_vars = self.opt.variables()
      self.reset_optimizer_op = tf.variables_initializer(self.opt_vars)
      self.dice_sess.run(self.reset_optimizer_op)
  
imports: import collections
  import copy
  import random
  import timeit
  import numpy
  import tensorflow
  from dice_ml import diverse_counterfactuals
  from dice_ml.counterfactual_explanations import CounterfactualExplanations
  from dice_ml.explainer_interfaces.explainer_base import ExplainerBase
label_desc: Use image node_id 16 for calling the DiceTensorFlow1 obj's underlying member method code with example usage: obj.do_optimizer_initializations(optimizer) without return types