code: def log_throughput(instances, timings):
      item_lengths = [len(item["target"]) for item in instances]
  
      if timings:
          total_time = sum(timings)
          avg_time = total_time / len(timings)
          logger.info(
              "Inference took "
              f"{total_time:.2f}s for {len(timings)} items, "
              f"{avg_time:.2f}s on average."
          )
          for idx, (duration, input_length) in enumerate(
              zip(timings, item_lengths), start=1
          ):
              logger.info(
                  f"\t{idx} took -> {duration:.2f}s"
                  f" (len(target)=={input_length})."
              )
      else:
          logger.info(
              "No items were provided for inference. No throughput to log."
          )
  
imports: import json
  import logging
  import multiprocessing
  import os
  import signal
  import time
  import traceback
  from queue import Empty
  from typing import Callable, Iterable, List, NamedTuple, Set, Tuple
  from typing_extensions import Literal
  from flask import Flask, Response, jsonify, request
  from gluonts.dataset.common import ListDataset
  from gluonts.dataset.jsonl import encode_json
  from gluonts.model.forecast import Forecast, Quantile
  from gluonts.pydantic import BaseModel, Field
  from gluonts.shell.util import forecaster_type_by_name
label_desc: Use image node_id 1 for calling a global function with example usage: log_throughput(instances, timings) without return types