code: def create_and_check_for_pretraining(
      self, config, pixel_values, labels
  ):
      model = VideoMAEForPreTraining(config)
      model.to(torch_device)
      model.eval()
      # important: each video needs to have the same number of masked patches
      # hence we define a single mask, which we then repeat for each example in the batch
      mask = torch.ones((self.num_masks,))
      mask = torch.cat(
          [mask, torch.zeros(self.seq_length - mask.size(0))]
      )
      bool_masked_pos = mask.expand(self.batch_size, -1).bool()
  
      result = model(pixel_values, bool_masked_pos)
      # model only returns predictions for masked patches
      num_masked_patches = mask.sum().item()
      decoder_num_labels = 3 * self.tubelet_size * self.patch_size**2
      self.parent.assertEqual(
          result.logits.shape,
          (self.batch_size, num_masked_patches, decoder_num_labels),
      )
  
imports: import copy
  import unittest
  import numpy
  from huggingface_hub import hf_hub_download
  from transformers import VideoMAEConfig
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, require_vision, slow, torch_device
  from transformers.utils import cached_property, is_torch_available, is_vision_available
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 5 for calling the VideoMAEModelTester obj's underlying member method code with example usage: obj.create_and_check_for_pretraining(config, pixel_values, labels) without return types