code: def create_and_check_for_pretraining(
      self,
      config,
      pixel_values,
      audio_values,
      pixel_mask,
      audio_mask,
      pixel_values_mixed,
      pixel_mask_mixed,
      labels,
  ):
      model = TvltForPreTraining(config=config)
      model.to(torch_device)
      model.train()
      result = model(
          pixel_values,
          audio_values,
          pixel_mask,
          audio_mask,
          pixel_values_mixed=pixel_values_mixed,
          pixel_mask_mixed=pixel_mask_mixed,
          labels=labels,
      )
      self.parent.assertEqual(
          result.pixel_logits.shape,
          (
              self.batch_size,
              self.expected_pixel_seq_len,
              self.image_mae_output_dim,
          ),
      )
      self.parent.assertEqual(
          result.audio_logits.shape,
          (
              self.batch_size,
              self.expected_audio_seq_len,
              self.audio_mae_output_dim,
          ),
      )
      self.parent.assertEqual(
          result.matching_logits.shape,
          (self.batch_size, self.num_labels),
      )
  
imports: import copy
  import inspect
  import unittest
  import numpy
  from huggingface_hub import hf_hub_download
  from transformers import TvltConfig, is_datasets_available, is_speech_available, is_torch_available, is_vision_available
  from transformers.testing_utils import require_torch, require_vision, slow, torch_device
  from transformers.utils import cached_property
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 7 for calling the TvltModelTester obj's underlying member method code with example usage: obj.create_and_check_for_pretraining(config, pixel_values, audio_values, pixel_mask, audio_mask, pixel_values_mixed, pixel_mask_mixed, labels) without return types