code: def test_video_transforms(self):
      config = OmegaConf.create(
          {
              "transforms": [
                  "permute_and_rescale",
                  {"type": "Resize", "params": {"size": [140, 100]}},
                  {
                      "type": "UniformTemporalSubsample",
                      "params": {"num_samples": 7},
                  },
              ]
          }
      )
      video_transforms = VideoTransforms(config)
  
      video = torch.ones(10, 200, 200, 3)
      processed_video = video_transforms(video)
      torch.testing.assert_close(
          processed_video, torch.ones(3, 7, 140, 100) / 255
      )
  
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 8 for calling the TestDatasetProcessors obj's underlying member method code with example usage: obj.test_video_transforms() without return types