class_docstr: Optimizer that implements the Adadelta algorithm.
  
  Adadelta optimization is a stochastic gradient descent method that is based
  on adaptive learning rate per dimension to address two drawbacks:
  
  - The continual decay of learning rates throughout training.
  - The need for a manually selected global learning rate.
  
  Adadelta is a more robust extension of Adagrad that adapts learning rates
  based on a moving window of gradient updates, instead of accumulating all
  past gradients. This way, Adadelta continues learning even when many updates
  have been done. Compared to Adagrad, in the original version of Adadelta you
  don't have to set an initial learning rate. In this version, the initial
  learning rate can be set, as in most other Keras optimizers.
  
  Args:
      learning_rate: A float, a
          `keras.optimizers.schedules.LearningRateSchedule` instance, or
          a callable that takes no arguments and returns the actual value to
          use. The learning rate. Defaults to `0.001`. Note that `Adadelta`
          tends to benefit from higher initial learning rate values compared
          to other optimizers. To match the exact form in the original paper,
          use 1.0.
      rho: A floating point value. The decay rate. Defaults to `0.95`.
      epsilon: Small floating point value for maintaining numerical stability.
      {{base_optimizer_keyword_args}}
  
  Reference:
  
  - [Zeiler, 2012](http://arxiv.org/abs/1212.5701)
code: def get_config(self):
      config = super().get_config()
  
      config.update(
          {
              "rho": self.rho,
              "epsilon": self.epsilon,
          }
      )
      return config
  
file_path: keras/keras/optimizers/adadelta.py
imports: from keras import ops
  from keras.api_export import keras_export
  from keras.optimizers import optimizer
label_desc: Use image node_id 4 for calling the Adadelta obj's underlying member method code with example usage: obj.get_config() and returns: config