class_docstr: An implementation of RMSprop
  :param learningrate learning rate
  :param learningrate_decay learning rate decay
  :param decayrate decay rate, also called rho
  :param epsilon for numerical stability
  >>> adagrad = RMSprop()
  creating: createRMSprop
class_docstr_tok: ["An","implementation","of","RMSprop",":","param","learningrate","learning","rate",":","param","learningrate_decay","learning","rate","decay",":","param","decayrate","decay","rate",",","also","called","rho",":","param","epsilon","for","numerical","stability",">",">",">","adagrad","=","RMSprop","(",")","creating",":","createRMSprop"]
code: def __init__(
      self,
      learningrate: float = 1e-2,
      learningrate_decay: float = 0.0,
      decayrate: float = 0.99,
      epsilon: float = 1e-8,
  ) -> None:
      from bigdl.dllib.optim.optimizer import RMSprop as BRMSprop
  
      self.optimizer = BRMSprop(
          learningrate,
          learningrate_decay,
          decayrate,
          epsilon,
          bigdl_type="float",
      )
  
imports: from abc import ABC, abstractmethod
  from bigdl.dllib.utils.common import DOUBLEMAX
  from bigdl.orca.learn.optimizers.schedule import Scheduler
  from bigdl.dllib.utils.log4Error import invalidInputError
  from typing import Any, Optional, Dict, TYPE_CHECKING
label_desc: Use image node_id 1 to create a new RMSprop object from inherited base classes: Optimizer with example: obj = RMSprop(learningrate, learningrate_decay, decayrate, epsilon)