class_docstr: ProximalAdaGrad.
  
  Examples:
      ```python
      # With TFLearn estimators
      proxi_adagrad = ProximalAdaGrad(learning_rate=0.01,
                                      l2_regularization_strength=0.01,
                                      initial_accumulator_value=0.01)
      regression = regression(net, optimizer=proxi_adagrad)
  
      # Without TFLearn estimators (returns tf.Optimizer)
      adagrad = ProximalAdaGrad(learning_rate=0.01).get_tensor()
      ```
  
  Arguments:
      learning_rate: `float`. Learning rate.
      initial_accumulator_value: `float`. Starting value for the
          accumulators, must be positive
      use_locking: `bool`. If True use locks for update operation.
      name: `str`. Optional name prefix for the operations created when
          applying gradients. Defaults to "AdaGrad".
  
  References:
      Efficient Learning using Forward-Backward Splitting. J. Duchi, Yoram
      Singer, 2009.
  
  Links:
      [Paper](http://papers.nips.cc/paper/3793-efficient-learning-using-forward-backward-splitting.pdf)
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code: def build(self, step_tensor=None):
      self.built = True
      self.tensor = tf.train.AdagradOptimizer(
          self.learning_rate,
          initial_accumulator_value=self.initial_accumulator_value,
          use_locking=self.use_locking,
          name=self.name,
      )
  
imports: from __future__ import division, print_function, absolute_import
  import tensorflow.compat.v1
  from .utils import get_from_module
label_desc: Use image node_id 2 for calling the ProximalAdaGrad obj's underlying member method code with example usage: obj.build(step_tensor) without return types