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>`)
docstr: Update the accumulation with the gradient.
  
  Args:
      index (int): index of argument to update.
      grad (ndarray): gradient at the index.
docstr_tok: ["Update","the","accumulation","with","the","gradient",".","Args",":","index","(","int",")",":","index","of","argument","to","update",".","grad","(","ndarray",")",":","gradient","at","the","index","."]
code: def _update_accumulation(self, index, grad):
      r"""Update the accumulation with the gradient.
  
      Args:
          index (int): index of argument to update.
          grad (ndarray): gradient at the index.
      """
      self.accumulation[index] = (
          self.decay * self.accumulation[index]
          + (1 - self.decay) * grad**2
      )
  
imports: from numpy import sqrt
  from .adagrad import AdagradOptimizer
label_desc: Use image node_id 3 for calling the RMSPropOptimizer obj's underlying member method code with example usage: obj._update_accumulation(index, grad) without return types