code: def verification_performance(scores_plda):
      """Computes the Equal Error Rate give the PLDA scores"""
  
      # Create ids, labels, and scoring list for EER evaluation
      ids = []
      labels = []
      positive_scores = []
      negative_scores = []
      for line in open(veri_file_path):
          lab = int(line.split(" ")[0].rstrip().split(".")[0].strip())
          enrol_id = line.split(" ")[1].rstrip().split(".")[0].strip()
          test_id = line.split(" ")[2].rstrip().split(".")[0].strip()
  
          # Assuming enrol_id and test_id are unique
          i = int(numpy.where(scores_plda.modelset == enrol_id)[0][0])
          j = int(numpy.where(scores_plda.segset == test_id)[0][0])
  
          s = float(scores_plda.scoremat[i, j])
          labels.append(lab)
          ids.append(enrol_id + "<>" + test_id)
          if lab == 1:
              positive_scores.append(s)
          else:
              negative_scores.append(s)
  
      # Clean variable
      del scores_plda
  
      # Final EER computation
      eer, th = EER(
          torch.tensor(positive_scores), torch.tensor(negative_scores)
      )
      min_dcf, th = minDCF(
          torch.tensor(positive_scores), torch.tensor(negative_scores)
      )
      return eer, min_dcf
  
imports: import os
  import sys
  import torch
  import torchaudio
  import logging
  import speechbrain
  import numpy
  import pickle
  from tqdm.contrib import tqdm
  from hyperpyyaml import load_hyperpyyaml
  from speechbrain.utils.metric_stats import EER, minDCF
  from speechbrain.processing.PLDA_LDA import StatObject_SB
  from speechbrain.processing.PLDA_LDA import Ndx
  from speechbrain.processing.PLDA_LDA import fast_PLDA_scoring
  from speechbrain.utils.data_utils import download_file
  from speechbrain.utils.distributed import run_on_main
label_desc: Use image node_id 3 for calling a global function with example usage: verification_performance(scores_plda) and returns: eer,  min_dcf