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", torch.rand((1, 3, 224, 224)).float()
      )
      sample_list.add_field("targets", torch.rand((1, 3129)).float())
  
      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 = "test"
      sample_list.dataset_type = "test"
      with torch.no_grad():
          model_output = self.pretrain_model(sample_list)
  
      self.assertTrue("losses" in model_output)
      self.assertTrue("test/test/logit_bce" in model_output["losses"])
  
imports: import gc
  import unittest
  import tests.test_utils
  import torch
  from mmf.common.sample import SampleList
  from mmf.models.vilt import ViLTImageEmbedding, ViLTTextEmbedding
  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
  from tests.test_utils import skip_if_old_transformers
  from torch import nn
label_desc: Use image node_id 3 for calling the TestViltPretrained obj's underlying member method code with example usage: obj.test_pretrained_model() without return types