code: def make_predictions(predictor, dataset, configuration):
      DEBUG = configuration.dict().get("DEBUG")
  
      # we have to take this as the initial start-time since the first
      # forecast is produced before the loop in predictor.predict
      start = time.time()
  
      predictions = []
  
      forecast_iter = predictor.predict(
          dataset,
          num_samples=configuration.num_samples,
      )
  
      for forecast in forecast_iter:
          end = time.time()
          prediction = configuration.as_json_dict(forecast)
  
          if DEBUG:
              prediction["debug"] = {"timing": end - start}
  
          predictions.append(prediction)
  
          start = time.time()
  
      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 7 for calling a global function with example usage: make_predictions(predictor, dataset, configuration) and returns: predictions