code: def prepare_config_and_inputs(self):
      input_ids = ids_tensor(
          [self.batch_size, self.text_seq_length], self.vocab_size
      )
  
      bbox = ids_tensor(
          [self.batch_size, self.text_seq_length, 4], self.range_bbox
      )
      bbox = bbox.numpy()
      # Ensure that bbox is legal
      for i in range(bbox.shape[0]):
          for j in range(bbox.shape[1]):
              if bbox[i, j, 3] < bbox[i, j, 1]:
                  tmp_coordinate = bbox[i, j, 3]
                  bbox[i, j, 3] = bbox[i, j, 1]
                  bbox[i, j, 1] = tmp_coordinate
              if bbox[i, j, 2] < bbox[i, j, 0]:
                  tmp_coordinate = bbox[i, j, 2]
                  bbox[i, j, 2] = bbox[i, j, 0]
                  bbox[i, j, 0] = tmp_coordinate
      bbox = tf.constant(bbox)
  
      pixel_values = floats_tensor(
          [
              self.batch_size,
              self.num_channels,
              self.image_size,
              self.image_size,
          ]
      )
  
      input_mask = None
      if self.use_input_mask:
          input_mask = random_attention_mask(
              [self.batch_size, self.text_seq_length]
          )
  
      token_type_ids = None
      if self.use_token_type_ids:
          token_type_ids = ids_tensor(
              [self.batch_size, self.text_seq_length],
              self.type_vocab_size,
          )
  
      sequence_labels = None
      token_labels = None
      if self.use_labels:
          sequence_labels = ids_tensor(
              [self.batch_size], self.type_sequence_label_size
          )
          token_labels = ids_tensor(
              [self.batch_size, self.text_seq_length], self.num_labels
          )
  
      config = LayoutLMv3Config(
          vocab_size=self.vocab_size,
          hidden_size=self.hidden_size,
          num_hidden_layers=self.num_hidden_layers,
          num_attention_heads=self.num_attention_heads,
          intermediate_size=self.intermediate_size,
          hidden_act=self.hidden_act,
          hidden_dropout_prob=self.hidden_dropout_prob,
          attention_probs_dropout_prob=self.attention_probs_dropout_prob,
          max_position_embeddings=self.max_position_embeddings,
          type_vocab_size=self.type_vocab_size,
          initializer_range=self.initializer_range,
          coordinate_size=self.coordinate_size,
          shape_size=self.shape_size,
          input_size=self.image_size,
          patch_size=self.patch_size,
      )
  
      return (
          config,
          input_ids,
          bbox,
          pixel_values,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_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 2 for calling the TFLayoutLMv3ModelTester obj's underlying member method code with example usage: obj.prepare_config_and_inputs() and returns: config,  input_ids,  bbox,  pixel_values,  token_type_ids,  input_mask,  sequence_labels,  token_labels