code: def create_and_check_lxmert_for_pretraining(
      self,
      config,
      input_ids,
      visual_feats,
      bounding_boxes,
      token_type_ids,
      input_mask,
      obj_labels,
      masked_lm_labels,
      matched_label,
      ans,
      output_attentions,
  ):
      model = TFLxmertForPreTraining(config=config)
      result = model(
          input_ids,
          visual_feats,
          bounding_boxes,
          token_type_ids=token_type_ids,
          attention_mask=input_mask,
          masked_lm_labels=masked_lm_labels,
          obj_labels=obj_labels,
          matched_label=matched_label,
          ans=ans,
          output_attentions=output_attentions,
      )
      result = model(
          input_ids,
          visual_feats,
          bounding_boxes,
          token_type_ids=token_type_ids,
          attention_mask=input_mask,
          masked_lm_labels=masked_lm_labels,
          output_attentions=not output_attentions,
          return_dict=False,
      )
      result = model(
          input_ids,
          visual_feats,
          bounding_boxes,
          token_type_ids=token_type_ids,
          attention_mask=input_mask,
          masked_lm_labels=masked_lm_labels,
      )
      result = model(
          input_ids,
          visual_feats,
          bounding_boxes,
          token_type_ids=token_type_ids,
          attention_mask=input_mask,
          obj_labels=obj_labels,
      )
      result = model(
          input_ids,
          visual_feats,
          bounding_boxes,
          token_type_ids=token_type_ids,
          attention_mask=input_mask,
          matched_label=matched_label,
      )
      result = model(
          input_ids,
          visual_feats,
          bounding_boxes,
          token_type_ids=token_type_ids,
          attention_mask=input_mask,
          ans=ans,
      )
      result = model(
          input_ids,
          visual_feats,
          bounding_boxes,
          token_type_ids=token_type_ids,
          attention_mask=input_mask,
          masked_lm_labels=masked_lm_labels,
          obj_labels=obj_labels,
          matched_label=matched_label,
          ans=ans,
          output_attentions=not output_attentions,
      )
  
      self.parent.assertEqual(
          result.prediction_logits.shape,
          (self.batch_size, self.seq_length, self.vocab_size),
      )
  
imports: from __future__ import annotations
  import tempfile
  import unittest
  import numpy
  from transformers import LxmertConfig, 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 5 for calling the TFLxmertModelTester obj's underlying member method code with example usage: obj.create_and_check_lxmert_for_pretraining(config, input_ids, visual_feats, bounding_boxes, token_type_ids, input_mask, obj_labels, masked_lm_labels, matched_label, ans, output_attentions) without return types