class_docstr: RMSProp optimizer.
  
  Uses optimizer weights ("slots") to maintain a root-mean-square exponentially
  decaying average of gradients from prior training batches.
code: def __init__(
      self,
      learning_rate=0.001,
      gamma=0.9,
      eps=1e-8,
      clip_grad_norm=None,
  ):  # pylint: disable=useless-super-delegation
      super().__init__(
          learning_rate=learning_rate,
          gamma=gamma,
          eps=eps,
          clip_grad_norm=clip_grad_norm,
      )
  
imports: from trax.fastmath import numpy
  from trax.optimizers import base
label_desc: Use image node_id 1 to create a new RMSProp object from inherited base classes: opt_base with example: obj = RMSProp(learning_rate, gamma, eps, clip_grad_norm)