code: def test_multi_hot_answer_from_vocab_processor(self):
      config = self._get_config(
          "../../../mmf/configs/datasets/clevr/defaults.yaml"
      )
      clevr_config = config.dataset_config.clevr
      answer_processor_config = clevr_config.processors.answer_processor
  
      # Test num_answers==1 case
      vocab_path = os.path.join(
          os.path.abspath(__file__), "..", "..", "data", "vocab.txt"
      )
      answer_processor_config.params.vocab_file = os.path.abspath(
          vocab_path
      )
      answer_processor = MultiHotAnswerFromVocabProcessor(
          answer_processor_config.params
      )
      processed = answer_processor({"answers": ["helmet"]})
      answers_indices = processed["answers_indices"]
      answers_scores = processed["answers_scores"]
  
      self.assertTrue(
          compare_tensors(
              answers_indices, torch.tensor([5] * 10, dtype=torch.long)
          )
      )
      expected_answers_scores = torch.zeros(19, dtype=torch.float)
      expected_answers_scores[5] = 1.0
      self.assertTrue(
          compare_tensors(answers_scores, expected_answers_scores)
      )
  
      # Test multihot when num answers greater than 1
      answer_processor_config.params.vocab_file = os.path.abspath(
          vocab_path
      )
      answer_processor_config.params.num_answers = 3
      answer_processor = MultiHotAnswerFromVocabProcessor(
          answer_processor_config.params
      )
      processed = answer_processor(
          {"answers": ["man", "with", "countryside"]}
      )
      answers_indices = processed["answers_indices"]
      answers_scores = processed["answers_scores"]
      self.assertTrue(
          compare_tensors(
              answers_indices,
              torch.tensor(
                  [2, 3, 15, 2, 3, 15, 2, 3, 15, 2], dtype=torch.long
              ),
          )
      )
      expected_answers_scores = torch.zeros(19, dtype=torch.float)
      expected_answers_scores[2] = 1.0
      expected_answers_scores[3] = 1.0
      expected_answers_scores[15] = 1.0
      self.assertTrue(
          compare_tensors(answers_scores, expected_answers_scores)
      )
  
      # Test unk
      processed = answer_processor(
          {"answers": ["test", "answer", "man"]}
      )
      answers_indices = processed["answers_indices"]
      answers_scores = processed["answers_scores"]
      self.assertTrue(
          compare_tensors(
              answers_indices,
              torch.tensor(
                  [0, 0, 2, 0, 0, 2, 0, 0, 2, 0], dtype=torch.long
              ),
          )
      )
      expected_answers_scores = torch.zeros(19, dtype=torch.float)
      expected_answers_scores[2] = 1.0
      self.assertTrue(
          compare_tensors(answers_scores, expected_answers_scores)
      )
  
imports: import os
  import tempfile
  import unittest
  import torch
  from mmf.datasets.processors.image_processors import VILTImageProcessor
  from mmf.datasets.processors.processors import CaptionProcessor, EvalAIAnswerProcessor, MultiClassFromFile, MultiHotAnswerFromVocabProcessor, Processor, TransformerBboxProcessor
  from mmf.datasets.processors.video_processors import VideoTransforms
  from mmf.utils.configuration import load_yaml
  from omegaconf import OmegaConf
  from tests.test_utils import compare_tensors, skip_if_no_pytorchvideo
label_desc: Use image node_id 3 for calling the TestDatasetProcessors obj's underlying member method code with example usage: obj.test_multi_hot_answer_from_vocab_processor() without return types