code: def main(args):
      scorefile = args[0]
      out_dir = args[1]
  
      # get scores and labels
      scores, labels = load_scorefile(scorefile)
  
      # calculate statistics in target and nontarget classes.
      n_trials = len(scores)
      scores_trg = []
      scores_nontrg = []
      for _s, _l in zip(scores, labels):
          if _l == 1:
              scores_trg.append(_s)
          elif _l == 0:
              scores_nontrg.append(_s)
          else:
              raise ValueError(f"{_l}, {type(_l)}")
      trg_mean = float(np.mean(scores_trg))
      trg_std = float(np.std(scores_trg))
      nontrg_mean = float(np.std(scores_nontrg))
      nontrg_std = float(np.std(scores_nontrg))
  
      # predictions, ground truth, and the false acceptance rates to calculate
      results = tuneThresholdfromScore(scores, labels, [1, 0.1])
      eer = results[1]
      fnrs, fprs, thresholds = ComputeErrorRates(scores, labels)
  
      # p_target, c_miss, and c_falsealarm in NIST minDCF calculation
      p_trg, c_miss, c_fa = 0.05, 1, 1
      mindcf, _ = ComputeMinDcf(
          fnrs, fprs, thresholds, p_trg, c_miss, c_fa
      )
  
      with open(out_dir, "w") as f:
          f.write(f"trg_mean: {trg_mean}, trg_std: {trg_std}\n")
          f.write(
              f"nontrg_mean: {nontrg_mean}, nontrg_std: {nontrg_std}\n"
          )
          f.write(f"eer: {eer}, mindcf: {mindcf}\n")
  
imports: import sys
  from typing import List, Tuple
  import numpy
  from espnet2.utils.eer import ComputeErrorRates, ComputeMinDcf, tuneThresholdfromScore
label_desc: Use image node_id 2 for calling a global function with example usage: main(args) without return types