code: def _concurrent_benchmark(
      predictor: PredictorBase, sample_input: object
  ) -> tuple:
      """Placeholder docstring"""
      logger.info(
          "============= Running Concurrent Benchmark.... =============="
      )
      concurrent_users = [
          (predictor.predict, sample_input)
      ] * CONCURRENCY
      latencies = []
      with ThreadPool(CONCURRENCY) as pool:
          t = 0
          for latency, _ in pool.starmap(
              _timed_invoke, concurrent_users
          ):
              latencies.append(latency)
              logger.info("User: %s => latency: %s seconds", t, latency)
              t += 1
  
      throughput_per_second = CONCURRENCY / sum(latencies)
      standard_deviation = std(latencies)
  
      logger.info("")
      logger.info("Model Latencies for Queued Requests:")
      _pretty_print_ordered_histogram(latencies, "queued request")
      logger.info("")
  
      logger.info(
          "Concurrent Benchmark Metrics => throughput/s: %s, standard deviation: %s",
          throughput_per_second,
          standard_deviation,
      )
      logger.info(
          "============== Completed Concurrent Benchmark ===============\n"
      )
  
      return (throughput_per_second, standard_deviation)
  
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 7 for calling a global function with example usage: _concurrent_benchmark(predictor, sample_input) and returns: throughput_per_second,  standard_deviation