code: def batch_inference_invocations(
      predictor_factory, configuration, settings
  ) -> Callable[[], Response]:
      predictor = predictor_factory(
          {"configuration": configuration.dict()}
      )
  
      scored_instances: List[ScoredInstanceStat] = []
      last_scored = [time.time()]
  
      def log_scored(when):
          N = 60
          diff = when - last_scored[0]
          if diff > N:
              scored_amount = sum(
                  info.amount for info in scored_instances
              )
              time_used = sum(
                  info.duration for info in scored_instances
              )
  
              logger.info(
                  f"Worker pid={os.getpid()}: scored {scored_amount} using on "
                  f"avg {round(time_used / scored_amount, 1)} s/ts over the "
                  f"last {round(diff)} seconds."
              )
              scored_instances.clear()
              last_scored[0] = time.time()
  
      def invocations() -> Response:
          request_data = request.data.decode("utf8").strip()
  
          # request_data can be empty, but .split() will produce a non-empty
          # list, which then means we try to decode an empty string, which
          # causes an error: `''.split() == ['']`
          if request_data:
              instances = list(
                  map(json.loads, request_data.split("\n"))
              )
          else:
              instances = []
  
          dataset = ListDataset(instances, configuration.freq)
  
          start_time = time.time()
  
          if settings.gluonts_batch_timeout > 0:
              predictions = with_timeout(
                  make_predictions,
                  args=(predictor, dataset, configuration),
                  timeout=settings.gluonts_batch_timeout,
              )
  
              # predictions are None, when predictor timed out
              if predictions is None:
                  logger.warning(
                      f"predictor timed out for: {request_data}"
                  )
                  FallbackPredictor = forecaster_type_by_name(
                      settings.gluonts_batch_fallback_predictor
                  )
                  fallback_predictor = FallbackPredictor(
                      prediction_length=predictor.prediction_length,
                  )
  
                  predictions = make_predictions(
                      fallback_predictor, dataset, configuration
                  )
          else:
              predictions = make_predictions(
                  predictor, dataset, configuration
              )
  
          end_time = time.time()
  
          scored_instances.append(
              ScoredInstanceStat(
                  amount=len(predictions),
                  duration=end_time - start_time,
              )
          )
  
          log_scored(when=end_time)
  
          for forward_field in settings.gluonts_forward_fields:
              for input_item, prediction in zip(dataset, predictions):
                  prediction[forward_field] = input_item.get(
                      forward_field
                  )
  
          lines = list(map(json.dumps, map(encode_json, predictions)))
          return Response(
              "\n".join(lines), mimetype="application/jsonlines"
          )
  
      def invocations_error_wrapper() -> Response:
          try:
              return invocations()
          except Exception:
              return Response(
                  json.dumps({"error": traceback.format_exc()}),
                  mimetype="application/jsonlines",
              )
  
      if settings.gluonts_batch_suppress_errors:
          return invocations_error_wrapper
      else:
          return invocations
  
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 8 for calling a global function with example usage: batch_inference_invocations(predictor_factory, configuration, settings) and returns: Response, invocations_error_wrapper, invocations, invocations, Response