code: def prepare_bros_batch_inputs():
      attention_mask = torch.tensor(
          [
              [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
              [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
          ]
      )
  
      bbox = torch.tensor(
          [
              [
                  [0.0000, 0.0000, 0.0000, 0.0000],
                  [0.5223, 0.5590, 0.5787, 0.5720],
                  [0.5853, 0.5590, 0.6864, 0.5720],
                  [0.5853, 0.5590, 0.6864, 0.5720],
                  [0.1234, 0.5700, 0.2192, 0.5840],
                  [0.2231, 0.5680, 0.2782, 0.5780],
                  [0.2874, 0.5670, 0.3333, 0.5780],
                  [0.3425, 0.5640, 0.4344, 0.5750],
                  [0.0866, 0.7770, 0.1181, 0.7870],
                  [0.1168, 0.7770, 0.1522, 0.7850],
                  [0.1535, 0.7750, 0.1864, 0.7850],
                  [0.1890, 0.7750, 0.2572, 0.7850],
                  [1.0000, 1.0000, 1.0000, 1.0000],
              ],
              [
                  [0.0000, 0.0000, 0.0000, 0.0000],
                  [0.4396, 0.6720, 0.4659, 0.6850],
                  [0.4698, 0.6720, 0.4843, 0.6850],
                  [0.1575, 0.6870, 0.2021, 0.6980],
                  [0.2047, 0.6870, 0.2730, 0.7000],
                  [0.1299, 0.7010, 0.1430, 0.7140],
                  [0.1299, 0.7010, 0.1430, 0.7140],
                  [0.1562, 0.7010, 0.2441, 0.7120],
                  [0.1562, 0.7010, 0.2441, 0.7120],
                  [0.2454, 0.7010, 0.3150, 0.7120],
                  [0.3176, 0.7010, 0.3320, 0.7110],
                  [0.3333, 0.7000, 0.4029, 0.7140],
                  [1.0000, 1.0000, 1.0000, 1.0000],
              ],
          ]
      )
      input_ids = torch.tensor(
          [
              [
                  101,
                  1055,
                  8910,
                  1012,
                  5719,
                  3296,
                  5366,
                  3378,
                  2146,
                  2846,
                  10807,
                  13494,
                  102,
              ],
              [
                  101,
                  2112,
                  1997,
                  3671,
                  6364,
                  1019,
                  1012,
                  5057,
                  1011,
                  4646,
                  2030,
                  2974,
                  102,
              ],
          ]
      )
  
      return input_ids, bbox, attention_mask
  
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 1 for calling a global function with example usage: prepare_bros_batch_inputs() and returns: input_ids,  bbox,  attention_mask