class_docstr: Similar to ZRule.
  The only difference is the addition of a numerical stabilizer term
  epsilon to the decomposition function's denominator.
  the sign of epsilon depends on the sign of the output activation
  0 is considered to be positive, ie sign(0) = 1
class_docstr_tok: ["Similar","to","ZRule",".","The","only","difference","is","the","addition","of","a","numerical","stabilizer","term","epsilon","to","the","decomposition","function","'s","denominator",".","the","sign","of","epsilon","depends","on","the","sign","of","the","output","activation","0","is","considered","to","be","positive",",","ie","sign","(","0",")","=","1"]
code: def __init__(
      self, layer: Layer, _state, epsilon=1e-7, bias: bool = True
  ):
      self._epsilon = rutils.assert_lrp_epsilon_param(epsilon, self)
      self._layer_wo_act = igraph.copy_layer_wo_activation(
          layer, keep_bias=bias, name_template="reversed_kernel_%s"
      )
  
imports: from __future__ import annotations
  import numpy
  import tensorflow.keras.backend
  import tensorflow.keras.layers
  import innvestigate.analyzer.relevance_based.utils
  import innvestigate.backend
  import innvestigate.backend.graph
  import innvestigate.layers
  from innvestigate.backend.types import Layer, OptionalList, Tensor
label_desc: Use image node_id 1 to create a new EpsilonRule object from inherited base classes: igraph with example: obj = EpsilonRule(layer, _state, epsilon, bias)