code: def _timed_invoke(predict: callable, sample_input: object) -> tuple:
      """Placeholder docstring"""
      start_timer = perf_counter()
      response = predict(sample_input)
      stop_timer = perf_counter()
  
      elapsed_time = stop_timer - start_timer
  
      if isinstance(response, list):
          generated_text = response[0]["generated_text"]
      else:
          generated_text = response["generated_text"]
  
      tokens_per_second = _tokens_per_second(
          generated_text,
          sample_input["parameters"]["max_new_tokens"],
          elapsed_time,
      )
  
      return (elapsed_time, tokens_per_second)
  
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 4 for calling a global function with example usage: _timed_invoke(predict, sample_input) and returns: elapsed_time,  tokens_per_second