code: def add_answer_info(self, sample_info, sample):
      # Load real answers from sample_info
      answers = sample_info.get("answers", [])
      answer_processor_arg = {"answers": answers}
  
      answer_processor_arg["tokens"] = sample.pop("ocr_tokens", [])
  
      processed_answers = self.answer_processor(answer_processor_arg)
  
      assert not self.config.fast_read, (
          "In TextVQADataset, online OCR sampling is incompatible "
          "with fast_read, so fast_read is currently not supported."
      )
  
      sample.update(processed_answers)
      sample.answers = object_to_byte_tensor(answers)
  
      if "answers_scores" in sample:
          sample.targets = sample.pop("answers_scores")
  
      return sample
  
file_path: mmf/mmf/datasets/builders/textvqa/dataset.py
imports: import numpy
  import torch
  from mmf.common.sample import Sample
  from mmf.datasets.mmf_dataset import MMFDataset
  from mmf.utils.distributed import byte_tensor_to_object, object_to_byte_tensor
  from mmf.utils.text import word_tokenize
label_desc: Use image node_id 7 for calling the TextVQADataset obj's underlying member method code with example usage: obj.add_answer_info(sample_info, sample) and returns: sample