code: def test_for_warning_if_padding_and_no_attention_mask(self):
      (
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
      ) = self.model_tester.prepare_config_and_inputs()
  
      # Set pad tokens in the input_ids
      input_ids[0, 0] = config.pad_token_id
  
      # Check for warnings if the attention_mask is missing.
      logger = logging.get_logger("transformers.modeling_utils")
      # clear cache so we can test the warning is emitted (from `warning_once`).
      logger.warning_once.cache_clear()
  
      with CaptureLogger(logger) as cl:
          model = BertModel(config=config)
          model.to(torch_device)
          model.eval()
          model(
              input_ids,
              attention_mask=None,
              token_type_ids=token_type_ids,
          )
      self.assertIn(
          "We strongly recommend passing in an `attention_mask`", cl.out
      )
  
imports: import os
  import tempfile
  import unittest
  from transformers import BertConfig, is_torch_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import CaptureLogger, require_torch, require_torch_accelerator, slow, torch_device
  from ...generation.test_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 19 for calling the BertModelTest obj's underlying member method code with example usage: obj.test_for_warning_if_padding_and_no_attention_mask() without return types