code: def test_inference_for_pretraining(self):
      # make random mask reproducible across the PT and TF model
      np.random.seed(2)
  
      model = ViTMAEForPreTraining.from_pretrained(
          "facebook/vit-mae-base"
      ).to(torch_device)
  
      image_processor = self.default_image_processor
      image = prepare_img()
      inputs = image_processor(images=image, return_tensors="pt").to(
          torch_device
      )
  
      # prepare a noise vector that will be also used for testing the TF model
      # (this way we can ensure that the PT and TF models operate on the same inputs)
      vit_mae_config = ViTMAEConfig()
      num_patches = int(
          (vit_mae_config.image_size // vit_mae_config.patch_size) ** 2
      )
      noise = np.random.uniform(size=(1, num_patches))
  
      # forward pass
      with torch.no_grad():
          outputs = model(
              **inputs,
              noise=torch.from_numpy(noise).to(device=torch_device),
          )
  
      # verify the logits
      expected_shape = torch.Size((1, 196, 768))
      self.assertEqual(outputs.logits.shape, expected_shape)
  
      expected_slice = torch.tensor(
          [
              [-0.0548, -1.7023, -0.9325],
              [0.3721, -0.5670, -0.2233],
              [0.8235, -1.3878, -0.3524],
          ]
      )
  
      self.assertTrue(
          torch.allclose(
              outputs.logits[0, :3, :3],
              expected_slice.to(torch_device),
              atol=1e-4,
          )
      )
  
imports: import math
  import tempfile
  import unittest
  import numpy
  from transformers import ViTMAEConfig
  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 2 for calling the ViTMAEModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_for_pretraining() without return types