class_docstr: Rule for relevance propagation using a small value of epsilon
  to avoid numerical instabilities and remove noise.
  
  Use for middle layers.
  
  Args:
      epsilon (integer, float): Value by which is added to the
      discriminator during propagation.
class_docstr_tok: ["Rule","for","relevance","propagation","using","a","small","value","of","epsilon","to","avoid","numerical","instabilities","and","remove","noise",".","Use","for","middle","layers",".","Args",":","epsilon","(","integer",",","float",")",":","Value","by","which","is","added","to","the","discriminator","during","propagation","."]
code: def _manipulate_weights(self, module, inputs, outputs):
      pass
  
imports: from abc import ABC, abstractmethod
  import torch
  from ..._utils.common import _format_tensor_into_tuples
label_desc: Use image node_id 2 for calling the EpsilonRule obj's underlying member method code with example usage: obj._manipulate_weights(module, inputs, outputs) without return types