code: def test_forward_pass_token_classification(self):
      # initialize model with randomly initialized token classification head
      model = LayoutLMForTokenClassification.from_pretrained(
          "microsoft/layoutlm-base-uncased", num_labels=13
      ).to(torch_device)
  
      (
          input_ids,
          attention_mask,
          bbox,
          token_type_ids,
          labels,
      ) = prepare_layoutlm_batch_inputs()
  
      # forward pass
      outputs = model(
          input_ids=input_ids,
          bbox=bbox,
          attention_mask=attention_mask,
          token_type_ids=token_type_ids,
          labels=labels,
      )
  
      # test the loss calculation to be around 2.65
      # expected_loss = torch.tensor(2.65, device=torch_device)
  
      # The loss is currently somewhat random and can vary between 0.1-0.3 atol.
      # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))
  
      # test the shape of the logits
      logits = outputs.logits
      expected_shape = torch.Size((2, 25, 13))
      self.assertEqual(logits.shape, expected_shape)
  
imports: import unittest
  from transformers import LayoutLMConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor
label_desc: Use image node_id 3 for calling the LayoutLMModelIntegrationTest obj's underlying member method code with example usage: obj.test_forward_pass_token_classification() without return types