docstr: Updates hyperparameters.
docstr_tok: ["Updates","hyperparameters","."]
code: def update_hyperparameters(
      self,
      proximity_weight=0.5,
      diversity_weight=0.5,
      categorical_penalty=0.1,
  ):
      """Updates hyperparameters."""
  
      self.hyperparameters = [
          proximity_weight,
          diversity_weight,
          categorical_penalty,
      ]
      self.dice_sess.run(
          self.weights_assign[0],
          feed_dict={self.weights_inits: proximity_weight},
      )
      if self.total_random_inits > 0:
          self.dice_sess.run(
              self.weights_assign[1],
              feed_dict={self.weights_inits: 0.0},
          )
      else:
          self.dice_sess.run(
              self.weights_assign[1],
              feed_dict={self.weights_inits: diversity_weight},
          )
      # just sanity check. anyways, the loss part is made to 0.
      self.dice_sess.run(
          self.weights_assign[2],
          feed_dict={self.weights_inits: categorical_penalty},
      )
  
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 17 for calling the DiceTensorFlow1 obj's underlying member method code with example usage: obj.update_hyperparameters(proximity_weight, diversity_weight, categorical_penalty) without return types