class_docstr: Bert BiEncoder that computes classification metrics (F1, precision, recall)
code: def eval_step(self, batch):
      if batch.text_vec is None:
          return
      output = super().eval_step(batch)
      preds = output["text"]
      labels = batch.labels
      if preds is not None and labels is not None:
          self._update_confusion_matrix(preds, labels)
      return output
  
imports: from parlai.agents.bert_ranker.bi_encoder_ranker import BiEncoderRankerAgent
  from projects.style_gen.modules import ClassificationMixin
label_desc: Use image node_id 2 for calling the BertRankerClassifierAgent obj's underlying member method code with example usage: obj.eval_step(batch) and returns: output