code: def test_vilt_image_embedding_pretrained(self):
      config = {
          "random_init": False,
          "patch_size": 32,
          "pretrained_model_name": "google/vit-base-patch32-384",
          "image_size": [384, 384],
      }
      embedding = ViLTImageEmbedding(**config)
      self.assertTrue(isinstance(embedding, nn.Module))
  
      image = torch.rand(32, 3, 384, 384)
      output = embedding(image)
      self.assertEqual(output.shape, torch.Size([32, 145, 768]))
  
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 2 for calling the TestViltEmbeddings obj's underlying member method code with example usage: obj.test_vilt_image_embedding_pretrained() without return types