class_docstr: Root Mean Squared Propagation (RMSProp) is an unpublished, adaptive learning rate method.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class_docstr_tok: ["Root","Mean","Squared","Propagation","(","RMSProp",")","is","an","unpublished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 __call__(self, model):
      train_params = [
          param
          for param in model.parameters()
          if param.trainable is True
      ]
      opt = optim.RMSProp(
          learning_rate=self.learning_rate,
          momentum=self.momentum,
          rho=self.rho,
          epsilon=self.epsilon,
          weight_decay=self.weight_decay,
          grad_clip=self.grad_clip,
          parameters=train_params,
      )
      return opt
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  from paddle import optimizer
label_desc: Use image node_id 2 for calling the RMSProp obj's underlying member method code with example usage: obj.__call__(model) and returns: opt