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 __init__(self, epsilon=1e-9) -> None:
      self.STABILITY_FACTOR = epsilon
  
imports: from abc import ABC, abstractmethod
  import torch
  from ..._utils.common import _format_tensor_into_tuples
label_desc: Use image node_id 1 to create a new EpsilonRule object from inherited base classes: PropagationRule with example: obj = EpsilonRule(epsilon)