docstr: Evaluate the profit of a strategy choice.
  
  Returns:
      AsynTrainingSimulator: the training simulator corresponding to the current strategy.
      tuple: contains memory saving and cost information of the current strategy.
docstr_tok: ["Evaluate","the","profit","of","a","strategy","choice",".","Returns",":","AsynTrainingSimulator",":","the","training","simulator","corresponding","to","the","current","strategy",".","tuple",":","contains","memory","saving","and","cost","information","of","the","current","strategy","."]
code: def _eval_one_choice(self):
      """
      Evaluate the profit of a strategy choice.
  
      Returns:
          AsynTrainingSimulator: the training simulator corresponding to the current strategy.
          tuple: contains memory saving and cost information of the current strategy.
      """
  
      ts = AsynTrainingSimulator(
          self.region_list, self.comp_power, self.link_to_bandwidth
      )
      ts.execute()
  
      extra_comm_cost = max(
          ts.iter_end_time - self.best_ts.iter_end_time, 0
      )
      profit = self._compute_offload_profit(
          ts.total_mem_saving,
          self.best_ts.peak_mem - ts.peak_mem,
          extra_comm_cost,
      )
  
      return ts, profit
  
imports: import time
  from abc import ABC, abstractmethod
  from typing import Dict, List, Type
  import torch
  from torch.fx.node import Node
  from colossalai.utils.device import get_current_device
  from .region import Region
  from .training_simulator import AsynTrainingSimulator, SynTrainingSimulator, TrainingSimulator
  from .util import NodeInfo, NvDevicePower
label_desc: Use image node_id 7 for calling the AsynGreedySolver obj's underlying member method code with example usage: obj._eval_one_choice() and returns: ts,  profit