code: def result(args, records):
      """
      print benchmark information
      """
  
      print(">>>>>>>>>>>>> test-run information >>>>>>>>>>>>>")
      print(
          "\033[1m\tModel\033[0m: \033[0;31m" + args.model + "\033[0m"
      )
      print(
          "\033[1m\tStage\033[0m: \033[0;31m" + args.stage + "\033[0m"
      )
      print(
          "\033[1m\tDataset\033[0m: \033[0;31m"
          + args.dataset
          + "\033[0m"
      )
      if args.cores:
          print(
              "\033[1m\tCores\033[0m: \033[0;31m"
              + args.core
              + "\033[0m"
          )
      else:
          core_num = psutil.cpu_count(logical=False) * int(
              subprocess.getoutput(
                  "cat /proc/cpuinfo | "
                  'grep "physical id" | sort -u | wc -l'
              )
          )
          print(
              "\033[1m\tCores\033[0m: \033[0;31m" + core_num + "\033[0m"
          )
      print(
          "\033[1m\tLookback\033[0m: \033[0;31m"
          + args.lookback
          + "\033[0m"
      )
      print(
          "\033[1m\tHorizon\033[0m: \033[0;31m"
          + args.horizon
          + "\033[0m"
      )
  
      if args.stage == "train":
          print("\n>>>>>>>>>>>>> train result >>>>>>>>>>>>>")
          print(
              f"\033[1m\tavg throughput\033[0m: \033[0;34m{records['train_throughput']}\033[0m"
          )
          print(">>>>>>>>>>>>> train result >>>>>>>>>>>>>")
      elif args.stage == "latency":
          for framework in args.inference_framework:
              print(
                  "\n>>>>>>>>>>>>> {} latency result >>>>>>>>>>>>>".format(
                      framework
                  )
              )
              print(
                  "avg latency: {}ms".format(
                      records[framework + "_latency"] * 1000
                  )
              )
              print(
                  "p50 latency: {}ms".format(
                      records[framework + "_percentile_latency"][0]
                      * 1000
                  )
              )
              print(
                  "p90 latency: {}ms".format(
                      records[framework + "_percentile_latency"][1]
                      * 1000
                  )
              )
              print(
                  "p95 latency: {}ms".format(
                      records[framework + "_percentile_latency"][2]
                      * 1000
                  )
              )
              print(
                  "p99 latency: {}ms".format(
                      records[framework + "_percentile_latency"][3]
                      * 1000
                  )
              )
              print(
                  ">>>>>>>>>>>>> {} latency result >>>>>>>>>>>>>".format(
                      framework
                  )
              )
      elif args.stage == "throughput":
          for framework in args.inference_framework:
              print(
                  "\n>>>>>>>>>>>>> {} throughput result >>>>>>>>>>>>>".format(
                      framework
                  )
              )
              print(
                  "avg throughput: {}".format(
                      records[framework + "_infer_throughput"]
                  )
              )
              print(
                  ">>>>>>>>>>>>> {} throughput result >>>>>>>>>>>>>".format(
                      framework
                  )
              )
      elif args.stage == "accuracy":
          print("\n>>>>>>>>>>>>> accuracy result >>>>>>>>>>>>>")
          for metric in args.metrics:
              print("{}: {}".format(metric, records[metric]))
          print(">>>>>>>>>>>>> accuracy result >>>>>>>>>>>>>")
  
file_path: BigDL/python/chronos/src/bigdl/chronos/benchmark/benchmark_chronos.py
imports: from bigdl.chronos.benchmark import generate_forecaster, generate_data, get_CPU_info, check_nano_env
  import time
  import numpy
  import argparse
  import os
  from scipy import stats
  import psutil
  import subprocess
  from bigdl.nano.utils.common import invalidInputError
  from bigdl.nano.utils.common import spawn_new_process
label_desc: Use image node_id 5 for calling a global function with example usage: result(args, records) without return types