class_docstr: This class is used to construct the restructure computation graph for
  call_func node with bias addition inside.
class_docstr_tok: ["This","class","is","used","to","construct","the","restructure","computation","graph","for","call_func","node","with","bias","addition","inside","."]
docstr: This method is used to extract the kwargs for further graph transform.
  
  For example:
      The formula for torch.addmm is out = beta * input + alpha * (m1 @ m2)
      The kwargs for addmm function is {beta=1, alpha=1, output=None}, then we need
      to insert two more operator.mul nodes for the computation graph to compute the
      final result.
docstr_tok: ["This","method","is","used","to","extract","the","kwargs","for","further","graph","transform",".","For","example",":","The","formula","for","torch.addmm","is","out","=","beta","*","input","+","alpha","*","(","m1","@","m2",")","The","kwargs","for","addmm","function","is","{","beta=1",",","alpha=1",",","output=None","}",",","then","we","need","to","insert","two","more","operator.mul","nodes","for","the","computation","graph","to","compute","the","final","result","."]
code: def extract_kwargs_from_origin_func(self):
      """
      This method is used to extract the kwargs for further graph transform.
  
      For example:
          The formula for torch.addmm is out = beta * input + alpha * (m1 @ m2)
          The kwargs for addmm function is {beta=1, alpha=1, output=None}, then we need
          to insert two more operator.mul nodes for the computation graph to compute the
          final result.
      """
  
file_path: ColossalAI/colossalai/fx/tracer/bias_addition_patch/patched_bias_addition_function/bias_addition_function.py
imports: import operator
  from abc import ABC, abstractmethod
  import torch
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the BiasAdditionFunc obj's underlying member method code with example usage: obj.extract_kwargs_from_origin_func() without return types