class_docstr: Implements a mash-up of the Adagrad algorithm and RMSProp. For the precise
  update equation see equations 10 and 11 in reference [1].
  
  References:
  [1] 'Automatic Differentiation Variational Inference', Alp Kucukelbir,
  Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei
  URL: https://arxiv.org/abs/1603.00788
  [2] 'Lecture 6.5 RmsProp: Divide the gradient by a running average
  of its recent magnitude', Tieleman, T. and Hinton, G.,
  COURSERA: Neural Networks for Machine Learning.
  [3] 'Adaptive subgradient methods for online learning and stochastic optimization',
  Duchi, John, Hazan, E and Singer, Y.
  
  Arguments:
  
  :param params: iterable of parameters to optimize or dicts defining parameter groups
  :param eta: sets the step size scale (optional; default: 1.0)
  :type eta: float
  :param t:  t, optional): momentum parameter (optional; default: 0.1)
  :type t: float
  :param delta: modulates the exponent that controls how the step size scales (optional: default: 1e-16)
  :type delta: float
code: def share_memory(self) -> None:
      for group in self.param_groups:
          for p in group["params"]:
              state = self.state[p]
              state["sum"].share_memory_()
  
file_path: pyro/pyro/optim/adagrad_rmsprop.py
imports: from typing import Any, Callable, Optional
  import torch
  from torch.optim.optimizer import Optimizer
label_desc: Use image node_id 2 for calling the AdagradRMSProp obj's underlying member method code with example usage: obj.share_memory() without return types