code: def _more_performant(
      best_tuned_configuration: list, tuned_configuration: list
  ) -> bool:
      """Placeholder docstring"""
      best_avg_latency = best_tuned_configuration[0]
      tuned_avg_latency = tuned_configuration[0]
      best_standard_deviation = best_tuned_configuration[6]
      tuned_standard_deviation = tuned_configuration[6]
  
      if _within_margins(
          MARGIN, 5, tuned_avg_latency, best_avg_latency
      ):
          if tuned_standard_deviation <= best_standard_deviation:
              return True
          return False
      return tuned_avg_latency <= best_avg_latency
  
file_path: sagemaker-python-sdk/src/sagemaker/serve/utils/tuning.py
imports: from __future__ import absolute_import
  import logging
  from time import perf_counter
  import collections
  from multiprocessing.pool import ThreadPool
  from math import ceil
  import pandas
  from numpy import percentile, std
  from sagemaker.serve.model_server.djl_serving.utils import _tokens_from_chars, _tokens_from_words
  from sagemaker.base_predictor import PredictorBase
label_desc: Use image node_id 9 for calling a global function with example usage: _more_performant(best_tuned_configuration, tuned_configuration) and returns: unknown, False, True