code: def test_custom_feature_and_mask_preprocessing(self):
      extra_modality = MMFTransformerModalityConfig(
          type="my_random_feature",
          key="my_random_feature",
          embedding_dim=128,
          position_dim=4,
          segment_id=3,
          encoder=EncoderFactory.Config(type="identity"),
      )
  
      modalities_config = [
          self._image_modality_config,
          self._text_modality_config,
          extra_modality,
      ]
      config = MMFTransformer.Config(
          modalities=modalities_config, num_labels=2
      )
      mmft = build_model(config)
  
      sample_list = SampleList()
      sample_list.image = torch.rand(2, 256)
      sample_list.text = torch.randint(0, 512, (2, 128))
      sample_list.text_mask = torch.ones(2, 128)
      sample_list.text_mask[:, 70:] = 0
      sample_list.my_random_feature = torch.rand(2, 4, 128)
      sample_list.my_random_feature_mask = torch.ones(2, 4)
      sample_list.my_random_feature_mask[:, 3:] = 0
  
      transformer_input = mmft.preprocess_sample(sample_list)
      input_ids = transformer_input["input_ids"]
      self.assertEqual(input_ids["image"].dim(), 3)
      self.assertEqual(list(input_ids["image"].size()), [2, 1, 256])
  
      self.assertEqual(input_ids["text"].dim(), 2)
      self.assertEqual(list(input_ids["text"].size()), [2, 128])
  
      self.assertEqual(input_ids["my_random_feature"].dim(), 3)
      self.assertEqual(
          list(input_ids["my_random_feature"].size()), [2, 4, 128]
      )
  
      position_ids = transformer_input["position_ids"]
      test_utils.compare_tensors(
          position_ids["image"], torch.tensor([[0], [0]])
      )
      test_utils.compare_tensors(
          position_ids["text"],
          torch.arange(0, 128).unsqueeze(0).expand((2, 128)),
      )
      test_utils.compare_tensors(
          position_ids["my_random_feature"],
          torch.arange(0, 4).unsqueeze(0).expand((2, 4)),
      )
  
      masks = transformer_input["masks"]
      test_utils.compare_tensors(
          masks["image"], torch.tensor([[1], [1]])
      )
      self.assertEqual(masks["text"].sum().item(), 140)
      self.assertEqual(masks["my_random_feature"].sum().item(), 6)
  
      segment_ids = transformer_input["segment_ids"]
      test_utils.compare_tensors(
          segment_ids["image"], torch.tensor([[0], [0]])
      )
      test_utils.compare_tensors(
          segment_ids["text"], torch.ones((2, 128)).long()
      )
      test_utils.compare_tensors(
          segment_ids["my_random_feature"],
          torch.full((2, 4), dtype=torch.long, fill_value=3).long(),
      )
  
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 7 for calling the TestMMFTransformer obj's underlying member method code with example usage: obj.test_custom_feature_and_mask_preprocessing() without return types