code: def test_pretrained_model(self):
      img_dim = 1024
      model = UNITERModelBase(img_dim=img_dim)
  
      device = get_current_device()
      model.eval()
      model = model.to(device)
  
      bs = 8
      num_feats = 100
      max_sentence_len = 25
      pos_dim = 7
      input_ids = torch.ones(
          (bs, max_sentence_len), dtype=torch.long
      ).to(device)
      img_feat = torch.rand((bs, num_feats, img_dim)).to(device)
      img_pos_feat = torch.rand((bs, num_feats, pos_dim)).to(device)
      position_ids = torch.arange(
          0, input_ids.size(1), dtype=torch.long, device=device
      ).unsqueeze(0)
      attention_mask = torch.ones(
          (bs, max_sentence_len + num_feats)
      ).to(device)
  
      with torch.no_grad():
          model_output = model(
              input_ids,
              position_ids,
              img_feat,
              img_pos_feat,
              attention_mask,
          ).final_layer
  
      self.assertEqual(model_output.shape, torch.Size([8, 125, 768]))
  
imports: import gc
  import unittest
  import tests.test_utils
  import torch
  from mmf.common.sample import SampleList
  from mmf.models.uniter import UNITERForClassification, UNITERForPretraining, UNITERImageEmbeddings, UNITERModelBase
  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 omegaconf import OmegaConf
label_desc: Use image node_id 1 for calling the TestUNITERModelBase obj's underlying member method code with example usage: obj.test_pretrained_model() without return types