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 _resource_apply_sparse(
      self, grad, var, indices, apply_state=None
  ):
      var_device, var_dtype = var.device, var.dtype.base_dtype
      coefficients = (apply_state or {}).get(
          (var_device, var_dtype)
      ) or self._fallback_apply_state(var_device, var_dtype)
  
      acc = self.get_slot(var, "accumulator")
      return tf.raw_ops.ResourceSparseApplyProximalAdagrad(
          var=var.handle,
          accum=acc.handle,
          lr=coefficients["lr_t"],
          l1=coefficients["l1_regularization_strength"],
          l2=coefficients["l2_regularization_strength"],
          grad=grad,
          indices=indices,
          use_locking=self._use_locking,
      )
  
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 5 for calling the ProximalAdagrad obj's underlying member method code with example usage: obj._resource_apply_sparse(grad, var, indices, apply_state) and returns: tf