code: def EER(positive_scores, negative_scores):
      """Computes the EER (and its threshold).
  
      Arguments
      ---------
      positive_scores : torch.tensor
          The scores from entries of the same class.
      negative_scores : torch.tensor
          The scores from entries of different classes.
  
      Example
      -------
      >>> positive_scores = torch.tensor([0.6, 0.7, 0.8, 0.5])
      >>> negative_scores = torch.tensor([0.4, 0.3, 0.2, 0.1])
      >>> val_eer, threshold = EER(positive_scores, negative_scores)
      >>> val_eer
      0.0
      """
  
      # Computing candidate thresholds
      thresholds, _ = torch.sort(
          torch.cat([positive_scores, negative_scores])
      )
      thresholds = torch.unique(thresholds)
  
      # Adding intermediate thresholds
      interm_thresholds = (thresholds[0:-1] + thresholds[1:]) / 2
      thresholds, _ = torch.sort(
          torch.cat([thresholds, interm_thresholds])
      )
  
      # Variable to store the min FRR, min FAR and their corresponding index
      min_index = 0
      final_FRR = 0
      final_FAR = 0
  
      for i, cur_thresh in enumerate(thresholds):
          pos_scores_threshold = positive_scores <= cur_thresh
          FRR = (
              pos_scores_threshold.sum(0)
          ).float() / positive_scores.shape[0]
          del pos_scores_threshold
  
          neg_scores_threshold = negative_scores > cur_thresh
          FAR = (
              neg_scores_threshold.sum(0)
          ).float() / negative_scores.shape[0]
          del neg_scores_threshold
  
          # Finding the threshold for EER
          if (FAR - FRR).abs().item() < abs(
              final_FAR - final_FRR
          ) or i == 0:
              min_index = i
              final_FRR = FRR.item()
              final_FAR = FAR.item()
  
      # It is possible that eer != fpr != fnr. We return (FAR  + FRR) / 2 as EER.
      EER = (final_FAR + final_FRR) / 2
  
      return float(EER), float(thresholds[min_index])
  
imports: import torch
  from joblib import Parallel, delayed
  from speechbrain.utils.data_utils import undo_padding
  from speechbrain.utils.edit_distance import wer_summary, wer_details_for_batch
  from speechbrain.dataio.dataio import merge_char, split_word, extract_concepts_values
  from speechbrain.dataio.wer import print_wer_summary, print_alignments
label_desc: Use image node_id 3 for calling a global function with example usage: EER(positive_scores, negative_scores) and returns: float,  float