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 _manipulate_weights(self, module, inputs, outputs):
      if hasattr(module, "weight"):
          module.weight.data = module.weight.data.clamp(min=0)
      if self.set_bias_to_zero and hasattr(module, "bias"):
          if module.bias is not None:
              module.bias.data = torch.zeros_like(module.bias.data)
  
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 Alpha1_Beta0_Rule obj's underlying member method code with example usage: obj._manipulate_weights(module, inputs, outputs) without return types