class_docstr: Root Mean Squared Propagation (RMSProp) is an unpublished, adaptive learning rate method.
  Args:
      learning_rate (float|Variable) - The learning rate used to update parameters.
          Can be a float value or a Variable with one float value as data element.
      momentum (float) - Momentum factor.
      rho (float) - rho value in equation.
      epsilon (float) - avoid division by zero, default is 1e-6.
      regularization (WeightDecayRegularizer, optional) - The strategy of regularization.
class_docstr_tok: ["Root","Mean","Squared","Propagation","(","RMSProp",")","is","an","unpublished",",","adaptive","learning","rate","method",".","Args",":","learning_rate","(","float|Variable",")","-","The","learning","rate","used","to","update","parameters",".","Can","be","a","float","value","or","a","Variable","with","one","float","value","as","data","element",".","momentum","(","float",")","-","Momentum","factor",".","rho","(","float",")","-","rho","value","in","equation",".","epsilon","(","float",")","-","avoid","division","by","zero",",","default","is","1e-6",".","regularization","(","WeightDecayRegularizer",",","optional",")","-","The","strategy","of","regularization","."]
code: def __init__(
      self,
      learning_rate,
      momentum,
      rho=0.95,
      epsilon=1e-6,
      parameter_list=None,
      regularization=None,
      **args,
  ):
      super(RMSProp, self).__init__()
      self.learning_rate = learning_rate
      self.momentum = momentum
      self.rho = rho
      self.epsilon = epsilon
      self.parameter_list = parameter_list
      self.regularization = regularization
  
imports: import sys
  import math
  from paddle.optimizer.lr import LinearWarmup
  from paddle.optimizer.lr import PiecewiseDecay
  from paddle.optimizer.lr import CosineAnnealingDecay
  from paddle.optimizer.lr import ExponentialDecay
  import paddle
  import paddle.regularizer
  from copy import deepcopy
label_desc: Use image node_id 1 to create a new RMSProp object from inherited base classes: object with example: obj = RMSProp(learning_rate, momentum, rho, epsilon, parameter_list, regularization)