class_docstr: Adagrad Dual Averaging algorithm for sparse linear models.
  
  This optimizer takes care of regularization of unseen features in a mini batch
  by updating them when they are seen with a closed form update rule that is
  equivalent to having updated them on every mini-batch.
  
  AdagradDA is typically used when there is a need for large sparsity in the
  trained model. This optimizer only guarantees sparsity for linear models. Be
  careful when using AdagradDA for deep networks as it will require careful
  initialization of the gradient accumulators for it to train.
  
  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))
code: def _apply_sparse(self, grad, var):
      g_acc = self.get_slot(var, "gradient_accumulator")
      gg_acc = self.get_slot(var, "gradient_squared_accumulator")
      with ops.device(var.device):
          global_step = array_ops.identity(self._global_step_on_worker)
      return gen_training_ops.sparse_apply_adagrad_da(
          var,
          g_acc,
          gg_acc,
          grad.values,
          grad.indices,
          math_ops.cast(
              self._learning_rate_tensor, var.dtype.base_dtype
          ),
          math_ops.cast(
              self._l1_regularization_strength, var.dtype.base_dtype
          ),
          math_ops.cast(
              self._l2_regularization_strength, var.dtype.base_dtype
          ),
          global_step,
          use_locking=self._use_locking,
      )
  
file_path: tensorflow-upstream/tensorflow/python/training/adagrad_da.py
imports: from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_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 AdagradDAOptimizer obj's underlying member method code with example usage: obj._apply_sparse(grad, var) and returns: gen_training_ops