code: def handle_predictions(predictor, instances, configuration):
      # create the forecasts
      forecasts = ThrougputIter(
          predictor.predict(
              ListDataset(instances, configuration.freq),
              num_samples=configuration.num_samples,
          )
      )
  
      predictions = [
          configuration.as_json_dict(forecast) for forecast in forecasts
      ]
  
      log_throughput(instances, forecasts.timings)
      return predictions
  
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 3 for calling a global function with example usage: handle_predictions(predictor, instances, configuration) and returns: predictions