code: def test_forward_pass_sequence_classification(self):
      # initialize model with randomly initialized sequence classification head
      model = TFLayoutLMForSequenceClassification.from_pretrained(
          "microsoft/layoutlm-base-uncased", num_labels=2
      )
  
      (
          input_ids,
          attention_mask,
          bbox,
          token_type_ids,
          _,
      ) = 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=tf.convert_to_tensor([1, 1]),
      )
  
      # test whether we get a loss as a scalar
      loss = outputs.loss
      expected_shape = (2,)
      self.assertEqual(loss.shape, expected_shape)
  
      # test the shape of the logits
      logits = outputs.logits
      expected_shape = (2, 2)
      self.assertEqual(logits.shape, expected_shape)
  
imports: from __future__ import annotations
  import unittest
  import numpy
  from transformers import LayoutLMConfig, is_tf_available
  from transformers.testing_utils import require_tf, slow
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 2 for calling the TFLayoutLMModelIntegrationTest obj's underlying member method code with example usage: obj.test_forward_pass_sequence_classification() without return types