class_docstr: RMSprop.
  
  Maintain a moving (discounted) average of the square of gradients.
  Divide gradient by the root of this average.
  
  Examples:
      ```python
      # With TFLearn estimators.
      rmsprop = RMSProp(learning_rate=0.1, decay=0.999)
      regression = regression(net, optimizer=rmsprop)
  
      # Without TFLearn estimators (returns tf.Optimizer).
      rmsprop = RMSProp(learning_rate=0.01, decay=0.999).get_tensor()
      # or
      rmsprop = RMSProp(learning_rate=0.01, decay=0.999)()
  
      ```
  
  Arguments:
      learning_rate: `float`. Learning rate.
      decay: `float`. Discounting factor for the history/coming gradient.
      momentum: `float`. Momentum.
      epsilon: `float`. Small value to avoid zero denominator.
      use_locking: `bool`. If True use locks for update operation.
      name: `str`. Optional name prefix for the operations created when
          applying gradients. Defaults to "RMSProp".
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code: def __init__(
      self,
      learning_rate=0.001,
      decay=0.9,
      momentum=0.0,
      epsilon=1e-10,
      use_locking=False,
      name="RMSProp",
  ):
      super(RMSProp, self).__init__(learning_rate, use_locking, name)
      self.decay = decay
      self.momentum = momentum
      self.epsilon = epsilon
  
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 RMSProp object from inherited base classes: Optimizer with example: obj = RMSProp(learning_rate, decay, momentum, epsilon, use_locking, name)