code: def prepare_config_and_inputs(self):
      input_ids = ids_tensor(
          [self.batch_size, self.seq_length], self.vocab_size
      )
  
      bbox = ids_tensor([self.batch_size, self.seq_length, 8], 1)
      # 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]:
                  t = bbox[i, j, 3]
                  bbox[i, j, 3] = bbox[i, j, 1]
                  bbox[i, j, 1] = t
              if bbox[i, j, 2] < bbox[i, j, 0]:
                  t = bbox[i, j, 2]
                  bbox[i, j, 2] = bbox[i, j, 0]
                  bbox[i, j, 0] = t
  
      input_mask = None
      if self.use_input_mask:
          input_mask = random_attention_mask(
              [self.batch_size, self.seq_length]
          )
  
      bbox_first_token_mask = None
      if self.use_bbox_first_token_mask:
          bbox_first_token_mask = torch.ones(
              [self.batch_size, self.seq_length], dtype=torch.bool
          ).to(torch_device)
  
      token_type_ids = None
      if self.use_token_type_ids:
          token_type_ids = ids_tensor(
              [self.batch_size, self.seq_length], self.type_vocab_size
          )
  
      token_labels = None
      if self.use_labels:
          token_labels = ids_tensor(
              [self.batch_size, self.seq_length], self.num_labels
          )
          initial_token_labels = ids_tensor(
              [self.batch_size, self.seq_length], self.num_labels
          )
          subsequent_token_labels = ids_tensor(
              [self.batch_size, self.seq_length], self.num_labels
          )
  
      config = self.get_config()
  
      return (
          config,
          input_ids,
          bbox,
          token_type_ids,
          input_mask,
          bbox_first_token_mask,
          token_labels,
          initial_token_labels,
          subsequent_token_labels,
      )
  
imports: import copy
  import unittest
  from transformers.testing_utils import require_torch, slow, torch_device
  from transformers.utils import is_torch_available
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 2 for calling the BrosModelTester obj's underlying member method code with example usage: obj.prepare_config_and_inputs() and returns: config,  input_ids,  bbox,  token_type_ids,  input_mask,  bbox_first_token_mask,  token_labels,  initial_token_labels,  subsequent_token_labels