code: def test_vilt_image_processor(self):
      from torchvision.transforms import ToPILImage
  
      size = 384
      config = OmegaConf.create({"size": [size, size]})
      image_processor = VILTImageProcessor(config)
  
      expected_size = torch.Size([3, size, size])
  
      image = ToPILImage()(torch.ones(3, 300, 500))
      processed_image = image_processor(image)
      self.assertEqual(processed_image.size(), expected_size)
  
      image = ToPILImage()(torch.ones(1, 224, 224))
      processed_image = image_processor(image)
      self.assertEqual(processed_image.size(), expected_size)
  
imports: import os
  import tempfile
  import unittest
  import torch
  from mmf.datasets.processors.image_processors import VILTImageProcessor
  from mmf.datasets.processors.processors import CaptionProcessor, EvalAIAnswerProcessor, MultiClassFromFile, MultiHotAnswerFromVocabProcessor, Processor, TransformerBboxProcessor
  from mmf.datasets.processors.video_processors import VideoTransforms
  from mmf.utils.configuration import load_yaml
  from omegaconf import OmegaConf
  from tests.test_utils import compare_tensors, skip_if_no_pytorchvideo
label_desc: Use image node_id 7 for calling the TestDatasetProcessors obj's underlying member method code with example usage: obj.test_vilt_image_processor() without return types