code: def test_forward(self):
      img_feature_dim = 2054
      bert_model_name = "bert-base-uncased"
      use_img_layernorm = True
      img_layer_norm_eps = 1e-12
      bert_config = BertConfig.from_pretrained(bert_model_name)
      # augment hf BertConfig for vinvl BertImgModel config
      bert_config.img_feature_dim = img_feature_dim
      bert_config.use_img_layernorm = use_img_layernorm
      bert_config.img_layer_norm_eps = img_layer_norm_eps
      model = VinVLBase(bert_config)
  
      model.eval()
      model = model.to(get_current_device())
  
      bs = 8
      num_feats = 70
      max_sentence_len = 25
      input_ids = torch.ones((bs, max_sentence_len), dtype=torch.long)
      img_feat = torch.rand((bs, num_feats, img_feature_dim))
  
      with torch.no_grad():
          model_output = model(input_ids, img_feat).last_hidden_state
      self.assertEqual(model_output.shape, torch.Size([8, 95, 768]))
  
imports: import unittest
  import tests.test_utils
  import torch
  from mmf.common.sample import SampleList
  from mmf.models.vinvl import VinVLBase, VinVLForClassification, VinVLForPretraining
  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 TestVinVLBase obj's underlying member method code with example usage: obj.test_forward() without return types