class_docstr: This class is used to construct the restructure computation graph for
  call_module node with bias addition inside.
class_docstr_tok: ["This","class","is","used","to","construct","the","restructure","computation","graph","for","call_module","node","with","bias","addition","inside","."]
docstr: This method is used to extract the kwargs for non-bias computation.
  
  For example:
      The kwargs for conv2d module is {} because the attributes like 'padding' or 'groups' are
      considered during module initializing. However, we need to consider those attributes as kwargs
      in F.conv2d.
docstr_tok: ["This","method","is","used","to","extract","the","kwargs","for","non-bias","computation",".","For","example",":","The","kwargs","for","conv2d","module","is","{","}","because","the","attributes","like","'padding","'","or","'groups","'","are","considered","during","module","initializing",".","However",",","we","need","to","consider","those","attributes","as","kwargs","in","F.conv2d","."]
code: def extract_kwargs_from_mod(self):
      """
      This method is used to extract the kwargs for non-bias computation.
  
      For example:
          The kwargs for conv2d module is {} because the attributes like 'padding' or 'groups' are
          considered during module initializing. However, we need to consider those attributes as kwargs
          in F.conv2d.
      """
  
file_path: ColossalAI/colossalai/fx/tracer/bias_addition_patch/patched_bias_addition_module/bias_addition_module.py
imports: import operator
  from abc import ABC, abstractmethod
  import torch
  import torch.nn.functional
label_desc: Use image node_id 4 for calling the BiasAdditionModule obj's underlying member method code with example usage: obj.extract_kwargs_from_mod() without return types