code: def format_for_prediction(self, report):
      answers = report.scores.argmax(dim=1)
  
      predictions = []
      answer_space_size = self.answer_processor.get_true_vocab_size()
  
      for idx, question_id in enumerate(report.question_id):
          answer_id = answers[idx].item()
  
          if answer_id >= answer_space_size:
              answer_id -= answer_space_size
              answer = report.context_tokens[idx][answer_id]
              if answer == self.context_processor.PAD_TOKEN:
                  answer = "unanswerable"
          else:
              answer = self.answer_processor.idx2word(answer_id)
          # actual_answer = report.answers[idx]
  
          predictions.append(
              {
                  "question_id": question_id.item(),
                  "answer": answer,
                  # "actual_answers": actual_answer,
                  # "question_tokens": report.question_tokens[idx],
                  # "image_id": report.image_id[idx].item()
              }
          )
  
      return predictions
  
file_path: mmf/mmf/datasets/builders/vqa2/dataset.py
imports: import logging
  import torch
  import tqdm
  from mmf.common.sample import Sample
  from mmf.datasets.mmf_dataset import MMFDataset
  from mmf.utils.distributed import is_main
label_desc: Use image node_id 9 for calling the VQA2Dataset obj's underlying member method code with example usage: obj.format_for_prediction(report) and returns: predictions