class_docstr: Optimizer that implements the Proximal Adagrad algorithm.
  
  References:
      - [Efficient Learning using Forward-Backward Splitting](
        http://papers.nips.cc/paper/3793-efficient-learning-using-forward-backward-splitting.pdf).
class_docstr_tok: ["Optimizer","that","implements","the","Proximal","Adagrad","algorithm",".","References",":","-","[","Efficient","Learning","using","Forward-Backward","Splitting","]","(","http",":","\/\/papers.nips.cc\/paper\/3793-efficient-learning-using-forward-backward-splitting.pdf",")","."]
code: def get_config(self):
      config = super().get_config()
      config.update(
          {
              "learning_rate": self._serialize_hyperparameter(
                  "learning_rate"
              ),
              "initial_accumulator_value": self._initial_accumulator_value,
              "l1_regularization_strength": self._serialize_hyperparameter(
                  "l1_regularization_strength"
              ),
              "l2_regularization_strength": self._serialize_hyperparameter(
                  "l2_regularization_strength"
              ),
          }
      )
      return config
  
file_path: addons/tensorflow_addons/optimizers/proximal_adagrad.py
imports: from typing import Callable, Union
  import tensorflow
  from typeguard import typechecked
  from tensorflow_addons.optimizers import KerasLegacyOptimizer
  from tensorflow_addons.utils.types import FloatTensorLike
label_desc: Use image node_id 6 for calling the ProximalAdagrad obj's underlying member method code with example usage: obj.get_config() and returns: config