code: def test_stacked_feature_preprocessing(self):
      self._text_modality_config.key = "body"
      second_text_modality_config = MMFTransformerModalityConfig(
          type="text",
          key="ocr",
          embedding_dim=756,
          position_dim=128,
          segment_id=2,
          encoder=TextEncoderFactory.Config(
              type=TextEncoderTypes.identity
          ),
      )
  
      modalities_config = [
          self._image_modality_config,
          self._text_modality_config,
          second_text_modality_config,
      ]
      config = MMFTransformer.Config(
          modalities=modalities_config, num_labels=2
      )
      mmft = build_model(config)
  
      sample_list = SampleList()
      sample_list.image = torch.rand(2, 256)
      # In stacked case, input_ids should represent all texts
      sample_list.input_ids = torch.randint(0, 512, (2, 2, 128))
      sample_list.lm_label_ids = torch.randint(-1, 30522, (2, 2, 128))
      lm_labels_sum = sample_list.lm_label_ids.sum().item()
  
      transformer_input = mmft.preprocess_sample(sample_list)
      self._compare_processed_for_multimodality(
          transformer_input, lm_labels_sum
      )
  
imports: import gc
  import unittest
  import tests.test_utils
  import torch
  from mmf.common.sample import SampleList
  from mmf.models.mmf_transformer import MMFTransformer, MMFTransformerModalityConfig
  from mmf.models.transformers.heads.mlm import MLM
  from mmf.modules.encoders import EncoderFactory, IdentityEncoder, ImageEncoderFactory, ImageEncoderTypes, ResNet152ImageEncoder, TextEncoderFactory, TextEncoderTypes
  from mmf.utils.build import build_model
  from mmf.utils.configuration import Configuration
  from mmf.utils.env import setup_imports, teardown_imports
  from omegaconf import OmegaConf
  from tests.test_utils import skip_if_no_pytorchvideo
label_desc: Use image node_id 4 for calling the TestMMFTransformer obj's underlying member method code with example usage: obj.test_stacked_feature_preprocessing() without return types