code: def test_inference_for_next_sentence_prediction(self):
      model = FNetForNextSentencePrediction.from_pretrained(
          "google/fnet-base"
      )
      model.to(torch_device)
  
      input_ids = torch.tensor(
          [[0, 1, 2, 3, 4, 5]], device=torch_device
      )
      with torch.no_grad():
          output = model(input_ids)[0]
  
      expected_shape = torch.Size((1, 2))
      self.assertEqual(output.shape, expected_shape)
  
      expected_slice = torch.tensor(
          [[-0.2234, -0.0226]], device=torch_device
      )
  
      self.assertTrue(torch.allclose(output, expected_slice, atol=1e-4))
  
imports: import unittest
  from typing import Dict, List, Tuple
  from transformers import FNetConfig, is_torch_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_tokenizers, require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 3 for calling the FNetModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_for_next_sentence_prediction() without return types