class_docstr: factor type parameter
      
class_docstr_tok: ["factor","type","parameter"]
code: def step(self, action):
      if self.partition[action[0]] % action[2] == 0:
          self.partition[action[0]] /= action[2]
          self.partition[action[1]] *= action[2]
          status = True
      else:
          status = False
  
      return status
  
imports: import math
  import random
  import logging
  import copy
  import torch
  from torch import optim
  from torch import nn
  import torch.nn.functional
  import numpy
  import nni
  from nni.tuner import Tuner
label_desc: Use image node_id 3 for calling the Factor obj's underlying member method code with example usage: obj.step(action) and returns: status