code: def test_splinter_pretraining_prepare_question_positions(self):
      model = SplinterForPreTraining.from_pretrained(
          "tau/splinter-base-qass"
      )
  
      input_ids = torch.tensor(
          [
              [101, 104, 1, 2, 104, 3, 4, 102],
              [101, 1, 104, 2, 104, 3, 104, 102],
              [101, 1, 2, 104, 104, 3, 4, 102],
              [101, 1, 2, 3, 4, 5, 104, 102],
          ]
      )
      question_positions = torch.tensor(
          [[1, 4, 0], [2, 4, 6], [3, 4, 0], [6, 0, 0]], dtype=torch.long
      )
      output_without_positions = model(input_ids)
      output_with_positions = model(
          input_ids, question_positions=question_positions
      )
      self.assertTrue(
          (
              output_without_positions.start_logits
              == output_with_positions.start_logits
          ).all()
      )
      self.assertTrue(
          (
              output_without_positions.end_logits
              == output_with_positions.end_logits
          ).all()
      )
  
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 6 for calling the SplinterModelIntegrationTest obj's underlying member method code with example usage: obj.test_splinter_pretraining_prepare_question_positions() without return types