code: def test_splinter_pretraining_loss_with_padding(self):
      model = SplinterForPreTraining.from_pretrained(
          "tau/splinter-base-qass"
      )
  
      # Input: "[CLS] [QUESTION] was born in [QUESTION] . Brad returned to the United Kingdom later . [SEP]"
      # Output should be the spans "Brad" and "the United Kingdom"
      input_ids = torch.tensor(
          [
              [
                  101,
                  104,
                  1108,
                  1255,
                  1107,
                  104,
                  119,
                  7796,
                  1608,
                  1106,
                  1103,
                  1244,
                  2325,
                  1224,
                  119,
                  102,
              ],
          ]
      )
      start_positions = torch.tensor([[7, 10]], dtype=torch.long)
      end_positions = torch.tensor([7, 12], dtype=torch.long)
      question_positions = torch.tensor([[1, 5]], dtype=torch.long)
      start_positions_with_padding = torch.tensor(
          [[7, 10, 0]], dtype=torch.long
      )
      end_positions_with_padding = torch.tensor(
          [7, 12, 0], dtype=torch.long
      )
      question_positions_with_padding = torch.tensor(
          [[1, 5, 0]], dtype=torch.long
      )
      output = model(
          input_ids,
          start_positions=start_positions,
          end_positions=end_positions,
          question_positions=question_positions,
      )
      output_with_padding = model(
          input_ids,
          start_positions=start_positions_with_padding,
          end_positions=end_positions_with_padding,
          question_positions=question_positions_with_padding,
      )
  
      self.assertAlmostEqual(
          output.loss.item(), output_with_padding.loss.item(), 4
      )
  
      # Note that the original code uses 0 to denote padded question tokens
      # and their start and end positions. As the pad_token_id of the model's
      # config is used for the losse's ignore_index in SplinterForPreTraining,
      # we add this test to ensure anybody making changes to the default
      # value of the config, will be aware of the implication.
      self.assertEqual(model.config.pad_token_id, 0)
  
imports: import copy
  import unittest
  from transformers import is_torch_available
  from transformers.testing_utils import require_torch, require_torch_multi_gpu, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
label_desc: Use image node_id 5 for calling the SplinterModelIntegrationTest obj's underlying member method code with example usage: obj.test_splinter_pretraining_loss_with_padding() without return types