class_docstr: Gamma rule for relevance propagation, gives more importance to
  positive relevance.
  
  Use for lower layers.
  
  Args:
      gamma (float): The gamma parameter determines by how much
      the positive relevance is increased.
class_docstr_tok: ["Gamma","rule","for","relevance","propagation",",","gives","more","importance","to","positive","relevance",".","Use","for","lower","layers",".","Args",":","gamma","(","float",")",":","The","gamma","parameter","determines","by","how","much","the","positive","relevance","is","increased","."]
code: def _manipulate_weights(self, module, inputs, outputs):
      if hasattr(module, "weight"):
          module.weight.data = (
              module.weight.data
              + self.gamma * 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 GammaRule obj's underlying member method code with example usage: obj._manipulate_weights(module, inputs, outputs) without return types