class_docstr: Base class for all propagation rule classes, also called Z-Rule.
  STABILITY_FACTOR is used to assure that no zero divison occurs.
class_docstr_tok: ["Base","class","for","all","propagation","rule","classes",",","also","called","Z-Rule",".","STABILITY_FACTOR","is","used","to","assure","that","no","zero","divison","occurs","."]
docstr: Pass initial activations to graph generation pass
docstr_tok: ["Pass","initial","activations","to","graph","generation","pass"]
code: def forward_pre_hook_activations(self, module, inputs):
      """Pass initial activations to graph generation pass"""
      device = (
          inputs[0].device
          if isinstance(inputs, tuple)
          else inputs.device
      )
      for input, activation in zip(inputs, module.activations[device]):
          input.data = activation
      return inputs
  
imports: from abc import ABC, abstractmethod
  import torch
  from ..._utils.common import _format_tensor_into_tuples
label_desc: Use image node_id 7 for calling the PropagationRule obj's underlying member method code with example usage: obj.forward_pre_hook_activations(module, inputs) and returns: inputs