code: def test_preprocessing_with_mvit_encoder(self):
      encoder_config = OmegaConf.create(
          {
              "name": "pytorchvideo",
              "model_name": "mvit_base_32x3",
              "random_init": True,
              "drop_last_n_layers": 0,
              "pooler_name": "cls",
              "spatial_size": 224,
              "temporal_size": 8,
              "head": None,
              "embed_dim_mul": [[1, 2.0], [3, 2.0], [14, 2.0]],
              "atten_head_mul": [[1, 2.0], [3, 2.0], [14, 2.0]],
              "pool_q_stride_size": [
                  [1, 1, 2, 2],
                  [3, 1, 2, 2],
                  [14, 1, 2, 2],
              ],
              "pool_kv_stride_adaptive": [1, 8, 8],
              "pool_kvq_kernel": [3, 3, 3],
          }
      )
      self._image_modality_config = MMFTransformerModalityConfig(
          type="image",
          key="image",
          embedding_dim=768,
          position_dim=1,
          segment_id=0,
          encoder=encoder_config,
      )
      modalities_config = [
          self._image_modality_config,
          self._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, 3, 8, 224, 224))
      sample_list.text = torch.randint(0, 512, (2, 128))
  
      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, 768])
  
      self.assertEqual(input_ids["text"].dim(), 2)
      self.assertEqual(list(input_ids["text"].size()), [2, 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)),
      )
  
      masks = transformer_input["masks"]
      test_utils.compare_tensors(
          masks["image"], torch.tensor([[1], [1]])
      )
      test_utils.compare_tensors(
          masks["text"], torch.ones((2, 128)).long()
      )
  
      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()
      )
  
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 9 for calling the TestMMFTransformer obj's underlying member method code with example usage: obj.test_preprocessing_with_mvit_encoder() without return types