class_docstr: A differentiable version of the Rprop optimizer.
  
  This optimizer creates a gradient tape as it updates parameters.
class_docstr_tok: ["A","differentiable","version","of","the","Rprop","optimizer",".","This","optimizer","creates","a","gradient","tape","as","it","updates","parameters","."]
code: def __init__(self, *args, **kwargs):
      super().__init__(*args, **kwargs)
      _warnings.warn(
          "Differentiable Rprop (correctly) yields zero second order "
          "gradients, as only the sign of the gradient is used in updates. "
          "Future versions will offer higher order gradients based on a "
          "continuous relaxation of the forward pass."
      )
  
imports: import abc
  import collections
  import copy
  import math
  import typing
  import warnings
  import torch
  from .None import patch
  from .None import utils
label_desc: Use image node_id 1 to create a new DifferentiableRprop object from inherited base classes: DifferentiableOptimizer with example: obj = DifferentiableRprop()