class_docstr: AdaGrad.
  
  Examples:
      ```python
      # With TFLearn estimators
      adagrad = AdaGrad(learning_rate=0.01, initial_accumulator_value=0.01)
      regression = regression(net, optimizer=adagrad)
  
      # Without TFLearn estimators (returns tf.Optimizer)
      adagrad = AdaGrad(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:
      Adaptive Subgradient Methods for Online Learning and Stochastic
      Optimization. J. Duchi, E. Hazan & Y. Singer. Journal of Machine
      Learning Research 12 (2011) 2121-2159.
  
  Links:
      [Paper](http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)
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code: def __init__(
      self,
      learning_rate=0.001,
      initial_accumulator_value=0.1,
      use_locking=False,
      name="AdaGrad",
  ):
      super(AdaGrad, self).__init__(learning_rate, use_locking, name)
      self.initial_accumulator_value = initial_accumulator_value
  
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 1 to create a new AdaGrad object from inherited base classes: Optimizer with example: obj = AdaGrad(learning_rate, initial_accumulator_value, use_locking, name)