code: def create_and_check_for_sequence_classification(
      self,
      config,
      input_ids,
      bbox,
      pixel_values,
      token_type_ids,
      input_mask,
      sequence_labels,
  ):
      config.num_labels = self.num_labels
      model = TFLayoutLMv3ForSequenceClassification(config=config)
      result = model(
          input_ids,
          bbox=bbox,
          pixel_values=pixel_values,
          attention_mask=input_mask,
          token_type_ids=token_type_ids,
          labels=sequence_labels,
          training=False,
      )
      self.parent.assertEqual(
          result.logits.shape, (self.batch_size, self.num_labels)
      )
  
imports: from __future__ import annotations
  import copy
  import inspect
  import unittest
  import numpy
  from transformers import is_tf_available, is_vision_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_tf, slow
  from transformers.utils import cached_property
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 4 for calling the TFLayoutLMv3ModelTester obj's underlying member method code with example usage: obj.create_and_check_for_sequence_classification(config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels) without return types