code: def valid_step(self, model, samples):
      answers = model.predict_answers(
          samples=samples,
          answer_list=self.answer_list,
          inference_method=self.inference_method,
          num_beams=self.num_beams,
          max_len=self.max_len,
          min_len=self.min_len,
          num_ans_candidates=self.num_ans_candidates,
          prompt=self.prompt,
      )
  
      if answers == None:  # corrupt videos
          return []
  
      pred_qa_pairs = []
  
      question_id = samples["question_id"]
      gt_answers = samples["answer"]
  
      for answer, ques_id, gt_answer in zip(
          answers, question_id, gt_answers
      ):
          ques_id = (
              int(ques_id.item())
              if isinstance(ques_id, torch.Tensor)
              else ques_id
          )
          pred_qa_pairs.append(
              {
                  "question_id": ques_id,
                  "pred_ans": answer,
                  "gt_ans": gt_answer,
              }
          )
  
      return pred_qa_pairs
  
imports: import logging
  import json
  import os
  import torch
  from tqdm import tqdm
  from lavis.common.utils import is_convertible_to_int
  import lavis.common.dist_utils
  from lavis.common.registry import registry
  from lavis.common.vqa_tools.vqa import VQA
  from lavis.common.vqa_tools.vqa_eval import VQAEval
  from lavis.tasks.base_task import BaseTask
label_desc: Use image node_id 1 for calling the DisCRNTask obj's underlying member method code with example usage: obj.valid_step(model, samples) and returns: pred_qa_pairs, list