code: def test_inference_masked_lm(self):
      tokenizer = PerceiverTokenizer.from_pretrained(
          "deepmind/language-perceiver"
      )
      model = PerceiverForMaskedLM.from_pretrained(
          "deepmind/language-perceiver"
      )
      model.to(torch_device)
  
      # prepare inputs
      text = (
          "This is an incomplete sentence where some words are missing."
      )
      encoding = tokenizer(
          text, padding="max_length", return_tensors="pt"
      )
  
      # mask " missing.".
      encoding.input_ids[0, 52:61] = tokenizer.mask_token_id
      inputs, input_mask = encoding.input_ids.to(
          torch_device
      ), encoding.attention_mask.to(torch_device)
  
      # forward pass
      with torch.no_grad():
          outputs = model(inputs=inputs, attention_mask=input_mask)
      logits = outputs.logits
  
      # verify logits
      expected_shape = torch.Size(
          (1, tokenizer.model_max_length, tokenizer.vocab_size)
      )
      self.assertEqual(logits.shape, expected_shape)
  
      expected_slice = torch.tensor(
          [
              [-10.8609, -10.7651, -10.9187],
              [-12.1689, -11.9389, -12.1479],
              [-12.1518, -11.9707, -12.2073],
          ],
          device=torch_device,
      )
  
      self.assertTrue(
          torch.allclose(logits[0, :3, :3], expected_slice, atol=1e-4)
      )
  
      expected_greedy_predictions = [
          38,
          115,
          111,
          121,
          121,
          111,
          116,
          109,
          52,
      ]
      masked_tokens_predictions = (
          logits[0, 52:61].argmax(dim=-1).tolist()
      )
      self.assertListEqual(
          expected_greedy_predictions, masked_tokens_predictions
      )
  
imports: import copy
  import inspect
  import math
  import tempfile
  import unittest
  import warnings
  from typing import Dict, List, Tuple
  import numpy
  from datasets import load_dataset
  from transformers import PerceiverConfig
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
  from transformers.utils import is_torch_available, is_vision_available
  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 1 for calling the PerceiverModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_masked_lm() without return types