code: def test_inference_encoder(self):
      num_candidates = 2
      vocab_size = 30522
  
      model = RealmKnowledgeAugEncoder.from_pretrained(
          "google/realm-cc-news-pretrained-encoder",
          num_candidates=num_candidates,
      )
      input_ids = torch.tensor(
          [[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]]
      )
      relevance_score = torch.tensor([[0.3, 0.7]], dtype=torch.float32)
      output = model(input_ids, relevance_score=relevance_score)[0]
  
      expected_shape = torch.Size((2, 6, vocab_size))
      self.assertEqual(output.shape, expected_shape)
  
      expected_slice = torch.tensor(
          [[[-11.0888, -11.2544], [-10.2170, -10.3874]]]
      )
  
      self.assertTrue(
          torch.allclose(output[1, :2, :2], expected_slice, atol=1e-4)
      )
  
imports: import copy
  import unittest
  import numpy
  from transformers import RealmConfig, is_torch_available
  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
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 2 for calling the RealmModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_encoder() without return types