code: def test_inference_for_video_classification(self):
      model = VideoMAEForVideoClassification.from_pretrained(
          "MCG-NJU/videomae-base-finetuned-kinetics"
      ).to(torch_device)
  
      feature_extractor = self.default_feature_extractor
      video = prepare_video()
      inputs = feature_extractor(video, return_tensors="pt").to(
          torch_device
      )
  
      # forward pass
      with torch.no_grad():
          outputs = model(**inputs)
  
      # verify the logits
      expected_shape = torch.Size((1, 400))
      self.assertEqual(outputs.logits.shape, expected_shape)
  
      expected_slice = torch.tensor([0.3669, -0.0688, -0.2421]).to(
          torch_device
      )
  
      self.assertTrue(
          torch.allclose(
              outputs.logits[0, :3], expected_slice, atol=1e-4
          )
      )
  
imports: import copy
  import inspect
  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
label_desc: Use image node_id 2 for calling the VideoMAEModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_for_video_classification() without return types