code: def test_inference_for_pretraining(self):
      model = VideoMAEForPreTraining.from_pretrained(
          "MCG-NJU/videomae-base-short"
      ).to(torch_device)
  
      image_processor = self.default_image_processor
      video = prepare_video()
      inputs = image_processor(video, return_tensors="pt").to(
          torch_device
      )
  
      # add boolean mask, indicating which patches to mask
      local_path = hf_hub_download(
          repo_id="hf-internal-testing/bool-masked-pos",
          filename="bool_masked_pos.pt",
      )
      inputs["bool_masked_pos"] = torch.load(local_path)
  
      # forward pass
      with torch.no_grad():
          outputs = model(**inputs)
  
      # verify the logits
      expected_shape = torch.Size([1, 1408, 1536])
      expected_slice = torch.tensor(
          [
              [0.7994, 0.9612, 0.8508],
              [0.7401, 0.8958, 0.8302],
              [0.5862, 0.7468, 0.7325],
          ],
          device=torch_device,
      )
      self.assertEqual(outputs.logits.shape, expected_shape)
      self.assertTrue(
          torch.allclose(
              outputs.logits[0, :3, :3], expected_slice, atol=1e-4
          )
      )
  
      # verify the loss (`config.norm_pix_loss` = `True`)
      expected_loss = torch.tensor([0.5142], device=torch_device)
      self.assertTrue(
          torch.allclose(outputs.loss, expected_loss, atol=1e-4)
      )
  
      # verify the loss (`config.norm_pix_loss` = `False`)
      model = VideoMAEForPreTraining.from_pretrained(
          "MCG-NJU/videomae-base-short", norm_pix_loss=False
      ).to(torch_device)
  
      with torch.no_grad():
          outputs = model(**inputs)
  
      expected_loss = torch.tensor(
          torch.tensor([0.6469]), device=torch_device
      )
      self.assertTrue(
          torch.allclose(outputs.loss, expected_loss, atol=1e-4)
      )
  
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 3 for calling the VideoMAEModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_for_pretraining() without return types