code: def add_answer_info(self, sample_info, current_sample):
      length = min(len(current_sample.text), current_sample.text_len)
      index = random.randint(0, length - 1)
  
      word = self.text_processor.vocab.get_itos()[
          current_sample.text[index].item()
      ]
      current_sample.text[index] = self.text_processor.vocab.get_stoi()[
          "<mask>"
      ]
      answer_processor_arg = {"answers": [word]}
  
      processed_soft_copy_answers = self.answer_processor(
          answer_processor_arg
      )
  
      current_sample.answers = processed_soft_copy_answers["answers"]
      current_sample.targets = processed_soft_copy_answers[
          "answers_scores"
      ]
  
      if self.answer_processor.word2idx(
          word
      ) == self.answer_processor.word2idx("<unk>"):
          current_sample.targets.zero_()
      return current_sample
  
imports: import random
  from mmf.datasets.builders.vqa2.dataset import VQA2Dataset
label_desc: Use image node_id 2 for calling the MaskedQVQA2Dataset obj's underlying member method code with example usage: obj.add_answer_info(sample_info, current_sample) and returns: current_sample