code: def test_tokenizer_inference(self):
      tokenizer = BigBirdTokenizer.from_pretrained(
          "google/bigbird-roberta-base"
      )
      model = BigBirdModel.from_pretrained(
          "google/bigbird-roberta-base",
          attention_type="block_sparse",
          num_random_blocks=3,
          block_size=16,
      )
      model.to(torch_device)
  
      text = [
          "Transformer-based models are unable to process long sequences due to their self-attention operation,"
          " which scales quadratically with the sequence length. To address this limitation, we introduce the"
          " Longformer with an attention mechanism that scales linearly with sequence length, making it easy to"
          " process documents of thousands of tokens or longer. Longformer’s attention mechanism is a drop-in"
          " replacement for the standard self-attention and combines a local windowed attention with a task"
          " motivated global attention. Following prior work on long-sequence transformers, we evaluate Longformer"
          " on character-level language modeling and achieve state-of-the-art results on text8 and enwik8. In"
          " contrast to most prior work, we also pretrain Longformer and finetune it on a variety of downstream"
          " tasks. Our pretrained Longformer consistently outperforms RoBERTa on long document tasks and sets new"
          " state-of-the-art results on WikiHop and TriviaQA."
      ]
      inputs = tokenizer(text)
  
      for k in inputs:
          inputs[k] = torch.tensor(
              inputs[k], device=torch_device, dtype=torch.long
          )
  
      prediction = model(**inputs)
      prediction = prediction[0]
  
      self.assertEqual(prediction.shape, torch.Size((1, 199, 768)))
  
      expected_prediction = torch.tensor(
          [
              [-0.0213, -0.2213, -0.0061, 0.0687],
              [0.0977, 0.1858, 0.2374, 0.0483],
              [0.2112, -0.2524, 0.5793, 0.0967],
              [0.2473, -0.5070, -0.0630, 0.2174],
              [0.2885, 0.1139, 0.6071, 0.2991],
              [0.2328, -0.2373, 0.3648, 0.1058],
              [0.2517, -0.0689, 0.0555, 0.0880],
              [0.1021, -0.1495, -0.0635, 0.1891],
              [0.0591, -0.0722, 0.2243, 0.2432],
              [-0.2059, -0.2679, 0.3225, 0.6183],
              [0.2280, -0.2618, 0.1693, 0.0103],
              [0.0183, -0.1375, 0.2284, -0.1707],
          ],
          device=torch_device,
      )
      self.assertTrue(
          torch.allclose(
              prediction[0, 52:64, 320:324],
              expected_prediction,
              atol=1e-4,
          )
      )
  
imports: import unittest
  from transformers import BigBirdConfig, is_torch_available
  from transformers.models.auto import get_values
  from transformers.models.big_bird.tokenization_big_bird import BigBirdTokenizer
  from transformers.testing_utils import require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
label_desc: Use image node_id 6 for calling the BigBirdModelIntegrationTest obj's underlying member method code with example usage: obj.test_tokenizer_inference() without return types