class_docstr: This class is used to construct the restructure computation graph for
  call_func node with bias addition inside.
docstr: This method is used to create a coefficent node for the numerical correctness.
  The formula for torch.addmm is out = beta * input + alpha * (m1 @ m2)
  Therefore, we need to use this method insert two more operator.mul nodes for
  the computation graph to compute the final result.
docstr_tok: ["This","method","is","used","to","create","a","coefficent","node","for","the","numerical","correctness",".","The","formula","for","torch.addmm","is","out","=","beta","*","input","+","alpha","*","(","m1","@","m2",")","Therefore",",","we","need","to","use","this","method","insert","two","more","operator.mul","nodes","for","the","computation","graph","to","compute","the","final","result","."]
code: def create_mul_node(self, input_proxy, coefficent):
      """
      This method is used to create a coefficent node for the numerical correctness.
      The formula for torch.addmm is out = beta * input + alpha * (m1 @ m2)
      Therefore, we need to use this method insert two more operator.mul nodes for
      the computation graph to compute the final result.
      """
      node_kind = "call_function"
      node_target = operator.mul
      node_args = (
          input_proxy,
          coefficent,
      )
      node_kwargs = {}
      mul_proxy = self.tracer.create_proxy(
          node_kind, node_target, node_args, node_kwargs
      )
      return mul_proxy
  
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 4 for calling the BiasAdditionFunc obj's underlying member method code with example usage: obj.create_mul_node(input_proxy, coefficent) and returns: mul_proxy