docstr: Uses RMSProp to compute step from gradients.
  
  Args:
      grads: numpy array of gradients.
      cache: numpy array of same shape as `grads` as RMSProp cache
      decay_rate: How fast to decay cache
  
  Returns:
      A tuple of
          step: numpy array of the same shape as `grads` giving the step.
              Note that this does not yet take the learning rate into account.
          cache: Updated RMSProp cache.
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code: def _rmsprop(self, grads, cache=None, decay_rate=0.95):
      """Uses RMSProp to compute step from gradients.
  
      Args:
          grads: numpy array of gradients.
          cache: numpy array of same shape as `grads` as RMSProp cache
          decay_rate: How fast to decay cache
  
      Returns:
          A tuple of
              step: numpy array of the same shape as `grads` giving the step.
                  Note that this does not yet take the learning rate into account.
              cache: Updated RMSProp cache.
      """
      if cache is None:
          cache = np.zeros_like(grads)
      cache = decay_rate * cache + (1 - decay_rate) * grads**2
      step = -grads / np.sqrt(cache + K.epsilon())
      return step, cache
  
file_path: keras-vis/vis/optimizer.py
imports: from __future__ import absolute_import
  import numpy
  from keras import backend
  from .callbacks import Print
  from .grad_modifiers import get
  from .utils import utils
label_desc: Use image node_id 2 for calling the Optimizer obj's underlying member method code with example usage: obj._rmsprop(grads, cache, decay_rate) and returns: step,  cache