docstr: Parses the metric file of the finished experiments to select the optimal DeepSpeed configuration.
  
  Args:
      finished_experiments (dcit): a dictionary of experiment id and experiment description.
  
  Returns:
      The path to the result folder of the experiment with the optimal configuration.
code: def parse_results(self, metric):
      """Parses the metric file of the finished experiments to select the optimal DeepSpeed configuration.
  
      Args:
          finished_experiments (dcit): a dictionary of experiment id and experiment description.
  
      Returns:
          The path to the result folder of the experiment with the optimal configuration.
      """
      max_throughput = sys.float_info.min
      best_exp_id = -1
      for exp_id, (exp, err) in self.finished_experiments.items():
          if err:
              logger.info(
                  f"The experiment exp_id = {exp_id}, exp_name = {exp['name']}, did not run successfully with error = {err}, thus a metrics.txt does not exist for it. Check the stderr.log in {exp['result_dir']}"
              )
              continue
  
          metric_file = exp["ds_config"][AUTOTUNING][
              AUTOTUNING_METRIC_PATH
          ]
  
          if os.path.exists(metric_file):
              with open(metric_file, "r") as f:
                  results = hjson.load(f)
                  curr_throughput = results[metric]
                  if curr_throughput > max_throughput:
                      max_throughput = curr_throughput
                      best_exp_id = exp_id
                  exp["results"] = results
  
      if best_exp_id != -1:
          best_exp, _ = self.finished_experiments[best_exp_id]
          return best_exp, max_throughput
  
      return exp, None
  
imports: import copy
  from numpy import BUFSIZE
  import json
  import subprocess
  import sys
  import threading
  import time
  import base64
  import os
  import hjson
  from tqdm import tqdm
  from ..utils import logger
  from .constants import AUTOTUNING, AUTOTUNING_METRIC_PATH
  from .utils import get_val_by_key, search_error, was_interruptted
  from deepspeed import comm
label_desc: Use image node_id 9 for calling the ResourceManager obj's underlying member method code with example usage: obj.parse_results(metric) and returns: exp,  None, best_exp,  max_throughput