class_docstr: AdaDelta.
  
  Construct a new Adadelta optimizer.
  
  Arguments:
      learning_rate: A `Tensor` or a floating point value. The learning rate.
      rho: A `Tensor` or a floating point value. The decay rate.
      epsilon: A `Tensor` or a floating point value.  A constant epsilon used
          to better conditioning the grad update.
      use_locking: If `True` use locks for update operations.
      name: Optional name prefix for the operations created when applying
          gradients.  Defaults to "Adadelta".
  
  References:
      ADADELTA: An Adaptive Learning Rate Method, Matthew D. Zeiler, 2012.
  
  Links:
      [http://arxiv.org/abs/1212.5701](http://arxiv.org/abs/1212.5701)
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code: def build(self, step_tensor=None):
      self.built = True
      self.tensor = tf.train.AdadeltaOptimizer(
          self.learning_rate,
          rho=self.rho,
          epsilon=self.epsilon,
          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 AdaDelta obj's underlying member method code with example usage: obj.build(step_tensor) without return types