code: def test_pretrained_model(self):
      sample_list = SampleList()
  
      sample_list.add_field(
          "input_ids",
          torch.randint(
              low=0, high=BERT_VOCAB_SIZE, size=(1, 128)
          ).long(),
      )
      sample_list.add_field("input_mask", torch.ones((1, 128)).long())
      sample_list.add_field("segment_ids", torch.zeros(1, 128).long())
      sample_list.add_field(
          "image_feature_0", torch.rand((1, 100, 2048)).float()
      )
      sample_list.add_field(
          "lm_label_ids",
          torch.zeros((1, 128), dtype=torch.long).fill_(-1),
      )
  
      self.pretrain_model.eval()
      self.pretrain_model = self.pretrain_model.to(get_current_device())
      sample_list = sample_list.to(get_current_device())
  
      sample_list.dataset_name = "random"
      sample_list.dataset_type = "test"
      with torch.no_grad():
          model_output = self.pretrain_model(sample_list)
  
      self.assertTrue("losses" in model_output)
      self.assertTrue(
          "random/test/masked_lm_loss" in model_output["losses"]
      )
      self.assertTrue(
          torch.isnan(
              model_output["losses"]["random/test/masked_lm_loss"]
          )
      )
  
imports: import gc
  import unittest
  import tests.test_utils
  import torch
  from mmf.common.sample import SampleList
  from mmf.modules.hf_layers import replace_with_jit, undo_replace_with_jit
  from mmf.utils.build import build_model
  from mmf.utils.configuration import Configuration
  from mmf.utils.env import setup_imports, teardown_imports
  from mmf.utils.general import get_current_device
label_desc: Use image node_id 3 for calling the TestVisualBertPretraining obj's underlying member method code with example usage: obj.test_pretrained_model() without return types