class_docstr: *** RMSProp *** 
  
  Computes adaptive learning rates for each parameter. Has an EWMA of squared gradients.
code: def update(self, gradient):
      self.variance = self.ewma * self.variance + (
          1 - self.ewma
      ) * np.power(gradient, 2)
      if self.t > 5:
          self.parameters += (
              self.learning_rate
              + (self.learning_rate * 15.0 * (0.990**self.t))
          ) * (gradient / np.sqrt(self.variance + self.epsilon))
      self.t += 1
      return self.parameters
  
file_path: pyflux/pyflux/inference/stoch_optim.py
imports: import numpy
label_desc: Use image node_id 2 for calling the RMSProp obj's underlying member method code with example usage: obj.update(gradient) and returns: self