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)
  
          predictions.append(
              {"questionId": question_id.item(), "prediction": answer}
          )
  
      return predictions
  
file_path: mmf/mmf/datasets/builders/gqa/dataset.py
imports: import torch
  from mmf.common.sample import Sample
  from mmf.datasets.mmf_dataset import MMFDataset
label_desc: Use image node_id 4 for calling the GQADataset obj's underlying member method code with example usage: obj.format_for_prediction(report) and returns: predictions