class_docstr: Optimizer that implements the Proximal Adagrad algorithm.
  
  References:
    Adaptive Subgradient Methods for Online Learning and Stochastic Optimization:
      [Duchi et al., 2011](http://jmlr.org/papers/v12/duchi11a.html)
      ([pdf](http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf))
    Efficient Learning using Forward-Backward Splitting:
      [Duchi et al., 2009](http://papers.nips.cc/paper/3793-efficient-learning-using-forward-backward-splitting)
      ([pdf](http://papers.nips.cc/paper/3793-efficient-learning-using-forward-backward-splitting.pdf))
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code: def _apply_sparse(self, grad, var):
      acc = self.get_slot(var, "accumulator")
      return gen_training_ops.sparse_apply_proximal_adagrad(
          var,
          acc,
          self._learning_rate_tensor,
          self._l1_regularization_strength_tensor,
          self._l2_regularization_strength_tensor,
          grad.values,
          grad.indices,
          use_locking=self._use_locking,
      )
  
file_path: tensorflow/tensorflow/python/training/proximal_adagrad.py
imports: from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import gen_training_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.training import optimizer
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 6 for calling the ProximalAdagradOptimizer obj's underlying member method code with example usage: obj._apply_sparse(grad, var) and returns: gen_training_ops