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 _create_slots(self, var_list):
      for var in var_list:
          init = tf.keras.initializers.constant(
              self._initial_accumulator_value
          )
          self.add_slot(var, "accumulator", init)
  
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 2 for calling the ProximalAdagrad obj's underlying member method code with example usage: obj._create_slots(var_list) without return types