class_docstr: Root mean squared propagation optimizer.
  
  The root mean square progation optimizer is a modified
  :class:`Adagrad optimizer <pennylane.optmimize.AdagradOptimizer>`,
  with a decay of learning rate adaptation.
  
  Extensions of the Adagrad optimization method generally
  start the sum :math:`a` over past gradients in the denominator
  of the learning rate at a finite :math:`t'` with :math:`0 < t' < t`,
  or decay past gradients to avoid an ever-decreasing learning rate.
  
  Root Mean Square propagation is such an adaptation, where
  
  .. math::
      a_i^{(t+1)} = \gamma a_i^{(t)} + (1-\gamma) (\partial_{x_i} f(x^{(t)}))^2.
  
  Args:
      stepsize (float): the user-defined hyperparameter :math:`\eta`
          used in the Adagrad optmization
      decay (float): the learning rate decay :math:`\gamma`
      eps (float): offset :math:`\epsilon` added for numerical stability
          (see :class:`Adagrad <pennylane.optmimize.AdagradOptimizer>`)
code: def __init__(self, stepsize=0.01, decay=0.9, eps=1e-8):
      super().__init__(stepsize)
      self.decay = decay
      self.eps = eps
  
imports: from numpy import sqrt
  from .adagrad import AdagradOptimizer
label_desc: Use image node_id 1 to create a new RMSPropOptimizer object from inherited base classes: AdagradOptimizer with example: obj = RMSPropOptimizer(stepsize, decay, eps)