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".
class_docstr_tok: ["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","''","."]
code: def build(self, step_tensor=None):
      self.built = True
      self.tensor = tf.train.RMSPropOptimizer(
          learning_rate=self.learning_rate,
          decay=self.decay,
          momentum=self.momentum,
          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 RMSProp obj's underlying member method code with example usage: obj.build(step_tensor) without return types