class_docstr: Tracks binary metrics, such as precision, recall, F1, EER, etc.
      
docstr: Appends scores and labels to internal lists.
  
  Does not compute metrics until time of summary, since
  automatic thresholds (e.g., EER) need full set of scores.
  
  Arguments
  ---------
  ids : list
      The string ids for the samples
docstr_tok: ["Appends","scores","and","labels","to","internal","lists",".","Does","not","compute","metrics","until","time","of","summary",",","since","automatic","thresholds","(","e.g.",",","EER",")","need","full","set","of","scores",".","Arguments","--","--","--","--","-","ids",":","list","The","string","ids","for","the","samples"]
code: def append(self, ids, scores, labels):
      """Appends scores and labels to internal lists.
  
      Does not compute metrics until time of summary, since
      automatic thresholds (e.g., EER) need full set of scores.
  
      Arguments
      ---------
      ids : list
          The string ids for the samples
  
      """
      self.ids.extend(ids)
      self.scores.extend(scores.detach())
      self.labels.extend(labels.detach())
  
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 the BinaryMetricStats obj's underlying member method code with example usage: obj.append(ids, scores, labels) without return types