class_docstr: The Training Simulator is used to simulate the training process.
  It records computation, communication, and runtime memory during forward and backward passes.
  
  Args:
      region_list (List[Region]): represents the linearized DNN computing graph.
      comp_power (float): the NVIDIA GPU FP16 computing power.
      link_to_bw (Dict[str, Dict[float, float]]): communication links and the corresponding bandwidth.
class_docstr_tok: ["The","Training","Simulator","is","used","to","simulate","the","training","process",".","It","records","computation",",","communication",",","and","runtime","memory","during","forward","and","backward","passes",".","Args",":","region_list","(","List","[","Region","]",")",":","represents","the","linearized","DNN","computing","graph",".","comp_power","(","float",")",":","the","NVIDIA","GPU","FP16","computing","power",".","link_to_bw","(","Dict","[","str",",","Dict","[","float",",","float","]","]",")",":","communication","links","and","the","corresponding","bandwidth","."]
code: def _get_computing_overhead(self, flop: float) -> float:
      return flop / self.comp_power
  
imports: import bisect
  from abc import ABC, abstractmethod
  from collections import OrderedDict
  from typing import Dict, List
  from torch.fx.node import Node
  from .region import Region
  from .util import *
label_desc: Use image node_id 7 for calling the TrainingSimulator obj's underlying member method code with example usage: obj._get_computing_overhead(flop) and returns: unknown