code: def get_result(self):
      captions = []
      max_len = 0
      for ex_idx in range(len(self._complete_seqs_scores)):
          if len(self._complete_seqs_scores[ex_idx]) == 0:
              captions.append([0] * 5)
              max_len = max(5, max_len)
          else:
              max_score = max(self._complete_seqs_scores[ex_idx])
              max_idx = self._complete_seqs_scores[ex_idx].index(
                  max_score
              )
              captions.append(self._complete_seqs[ex_idx][max_idx])
              max_len = max(max_len, len(captions[-1]))
      for ex_idx in range(len(captions)):
          padded_tokens = [self._vocab.PAD_INDEX] * (
              max_len - len(captions[ex_idx])
          )
          captions[ex_idx].extend(padded_tokens)
      return torch.FloatTensor(captions)
  
imports: import os
  import re
  from collections import Counter
  from itertools import chain
  import torch
  from mmf.common.registry import registry
  from mmf.utils.file_io import PathManager
  from mmf.utils.general import get_absolute_path
label_desc: Use image node_id 4 for calling the BeamSearch obj's underlying member method code with example usage: obj.get_result() and returns: torch