code: def test_splinter_pretraining(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,
              ]
          ]
      )
      question_positions = torch.tensor([[1, 5]], dtype=torch.long)
      output = model(input_ids, question_positions=question_positions)
  
      expected_shape = torch.Size((1, 2, 16))
      self.assertEqual(output.start_logits.shape, expected_shape)
      self.assertEqual(output.end_logits.shape, expected_shape)
  
      self.assertEqual(torch.argmax(output.start_logits[0, 0]), 7)
      self.assertEqual(torch.argmax(output.end_logits[0, 0]), 7)
      self.assertEqual(torch.argmax(output.start_logits[0, 1]), 10)
      self.assertEqual(torch.argmax(output.end_logits[0, 1]), 12)
  
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 2 for calling the SplinterModelIntegrationTest obj's underlying member method code with example usage: obj.test_splinter_pretraining() without return types