class_docstr: Alpha1_Beta0 rule for relevance backpropagation, also known
  as Deep-Taylor. Only positive relevance is propagated, resulting
  in stable results, therefore recommended as the initial choice.
  
  Warning: Does not work for BatchNorm modules because weight and bias
  are defined differently.
  
  Use for lower layers.
class_docstr_tok: ["Alpha1_Beta0","rule","for","relevance","backpropagation",",","also","known","as","Deep-Taylor",".","Only","positive","relevance","is","propagated",",","resulting","in","stable","results",",","therefore","recommended","as","the","initial","choice",".","Warning",":","Does","not","work","for","BatchNorm","modules","because","weight","and","bias","are","defined","differently",".","Use","for","lower","layers","."]
code: def __init__(self, set_bias_to_zero=False) -> None:
      self.set_bias_to_zero = set_bias_to_zero
  
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 Alpha1_Beta0_Rule object from inherited base classes: PropagationRule with example: obj = Alpha1_Beta0_Rule(set_bias_to_zero)