code: def test_rgb_with_temporal_jittering(self):
      random_label = np.random.randint(0, 100)
      VIDEO = "/mnt/vol/gfsdataswarm-oregon/users/trandu/sample.avi"
      if not os.path.exists(VIDEO):
          raise unittest.SkipTest("Missing data")
      temp_list = tempfile.NamedTemporaryFile(delete=False).name
      line_str = "{} 0 {}\n".format(VIDEO, random_label)
      self.create_a_list(temp_list, line_str, 16)
      video_db_dir = tempfile.mkdtemp()
  
      self.create_video_db(temp_list, video_db_dir)
      model = model_helper.ModelHelper(name="Video Loader from LMDB")
      reader = model.CreateDB("sample", db=video_db_dir, db_type="lmdb")
  
      # build the model
      model.net.VideoInput(
          reader,
          ["data", "label"],
          name="data",
          batch_size=16,
          clip_per_video=1,
          crop_size=112,
          scale_w=171,
          scale_h=128,
          length_rgb=8,
          sampling_rate_rgb=1,
          decode_type=0,
          video_res_type=0,  # scale by scale_h and scale_w
      )
  
      workspace.RunNetOnce(model.param_init_net)
      workspace.RunNetOnce(model.net)
      data = workspace.FetchBlob("data")
      label = workspace.FetchBlob("label")
  
      np.testing.assert_equal(label, random_label)
      np.testing.assert_equal(data.shape, [16, 3, 8, 112, 112])
      os.remove(temp_list)
      shutil.rmtree(video_db_dir)
  
file_path: pytorch/caffe2/python/operator_test/video_input_op_test.py
imports: import os
  import shutil
  import sys
  import tempfile
  import unittest
  import numpy
  from caffe2.proto import caffe2_pb2
  from caffe2.python import model_helper, workspace
label_desc: Use image node_id 3 for calling the VideoInputOpTest obj's underlying member method code with example usage: obj.test_rgb_with_temporal_jittering() without return types