code: def read_files(directory, seed=None, n_jobs=1):
      seed_pattern = "*" if seed is None else str(seed)
      glob_pattern = os.path.join(
          directory, "predictions_*_%s_*.npy" % seed_pattern
      )
      files = sorted(glob.glob(glob_pattern))
      files = joblib.Parallel(n_jobs=n_jobs, verbose=10)(
          joblib.delayed(_load_file)(f=f) for f in files
      )
  
      return files
  
file_path: auto-sklearn/scripts/2015_nips_paper/run/score_ensemble.py
imports: from argparse import ArgumentParser
  import csv
  import glob
  import os
  import time
  import joblib
  import numpy
  from autosklearn.ensembles.ensemble_selection import EnsembleSelection
  from autosklearn.metrics import balanced_accuracy
  from autosklearn.automl_common.common.utils.backend import create
label_desc: Use image node_id 2 for calling a global function with example usage: read_files(directory, seed, n_jobs) and returns: files