code: def main():
      args = parse_args()
      print(args.gpu)
      print_sys_info(args)
      results = {}
      set_signal()
      skipped = 0
      total = 0
  
      if not os.path.exists(args.pipedir):
          raise Exception(args.pipedir + " directory not found")
  
      tasks = os.listdir(args.pipedir)
      tasks = list(map(lambda x: int(x), tasks))
      tasks.sort()
      for task in list(map(lambda x: str(x), tasks)):
          print("Task-{}".format(task))
          task_dir = os.path.join(args.pipedir, task)
          task_pip_dir = os.path.join(task_dir, "pipelines")
  
          X = np.load(os.path.join(task_dir, "data", "X.dat.npy"))
          results[task] = {
              "dataset_size": X.shape[0],
              "num_features": X.shape[1],
          }
          pipelines = os.listdir(os.path.join(task_pip_dir))
          pipelines = list(map(lambda x: int(x[:-4]), pipelines))
          pipelines.sort()
          res = []
          for pipeline in list(map(lambda x: str(x), pipelines)):
              total += 1
              with open(
                  os.path.join(task_pip_dir, pipeline + ".pkl"), "rb"
              ) as f:
                  model = joblib.load(f)
  
                  assert model is not None
  
                  results[task][pipeline] = {}
                  times = []
                  mean = 0
                  print("Pipeline-{}".format(pipeline))
  
                  try:
                      for i in range(args.niters):
                          set_alarm(3600)
                          with Timer() as t:
                              res = model.predict(X)
                          times.append(t.interval)
                          set_alarm(0)
                      mean = (
                          stats.trim_mean(times, 1 / len(times))
                          if args.niters > 1
                          else times[0]
                      )
                      gc.collect()
                  except Exception as e:
                      print(e)
                      pass
                  results[task][pipeline] = {"prediction_time": mean}
  
                  for backend in args.backend.split(","):
                      print("Running '%s' ..." % backend)
                      scorer = score.ScoreBackend.create(backend)
                      with scorer:
                          try:
                              conversion_time = scorer.convert(
                                  model, X, args
                              )
                          except Exception as e:
                              skipped += 1
                              print(e)
                              continue
  
                          times = []
                          prediction_time = 0
                          try:
                              for i in range(args.niters):
                                  set_alarm(3600)
                                  times.append(scorer.predict(X))
                                  set_alarm(0)
                              prediction_time = (
                                  times[0]
                                  if args.niters == 1
                                  else stats.trim_mean(
                                      times, 1 / len(times)
                                  )
                              )
                              gc.collect()
                          except Exception as e:
                              skipped += 1
                              print(e)
                              pass
  
                          results[task][pipeline][backend] = {
                              "conversion_time": str(conversion_time),
                              "prediction_time": str(prediction_time),
                              "speedup": "0"
                              if mean == 0 or prediction_time == 0
                              else str(mean / prediction_time)
                              if prediction_time < mean
                              else str(-prediction_time / mean),
                          }
  
                          print(results[task][pipeline][backend])
  
                          if args.validate:
                              np.testing.assert_allclose(
                                  scorer.predictions,
                                  res,
                                  rtol=1e-5,
                                  atol=1e-6,
                              )
  
      output = json.dumps(results, indent=2)
      output_file = open(args.output, "w")
      output_file.write(output + "\n")
      output_file.close()
  
      print("All results written to file '%s'" % args.output)
      print(
          "Total num of pipelines: {}; skipped: {}".format(
              total, skipped
          )
      )
  
imports: import os
  import sys
  import argparse
  import json
  import ast
  import psutil
  from pathlib import Path
  import signal
  import time
  import numpy
  import sklearn
  import joblib
  import torch
  from scipy import stats
  import gc
  import benchmarks.pipelines.score
  from benchmarks.timer import Timer
label_desc: Use image node_id 6 for calling a global function with example usage: main() without return types