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 _prepare_local(self, var_device, var_dtype, apply_state):
      super()._prepare_local(var_device, var_dtype, apply_state)
      apply_state[(var_device, var_dtype)].update(
          {
              "l1_regularization_strength": tf.identity(
                  self._get_hyper(
                      "l1_regularization_strength", var_dtype
                  )
              ),
              "l2_regularization_strength": tf.identity(
                  self._get_hyper(
                      "l2_regularization_strength", var_dtype
                  )
              ),
          }
      )
  
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 4 for calling the ProximalAdagrad obj's underlying member method code with example usage: obj._prepare_local(var_device, var_dtype, apply_state) without return types