code: def test_double_meter_update_and_reset(self):
      meter = VideoAccuracyMeter(
          topk=[1, 2], clips_per_video_train=1, clips_per_video_test=2
      )
      # Batchsize = 3, num classes = 3, clips_per_video is 2,
      # score is a value in {1, 2, 3}.
      # Data of two batch is provided
      model_outputs = [
          torch.tensor(
              [
                  [3, 2, 1],
                  [3, 1, 2],
                  [1, 2, 2],
                  [1, 2, 3],
                  [2, 2, 2],
                  [1, 3, 2],
              ],
              dtype=torch.float,
          ),
          torch.tensor(
              [
                  [3, 2, 1],
                  [3, 1, 2],
                  [1, 2, 2],
                  [1, 2, 3],
                  [2, 2, 2],
                  [1, 3, 2],
              ],
              dtype=torch.float,
          ),
      ]
      # Class 0 is the correct class for video 1, class 2 for video 2, and
      # class 1 for video, in both batches
      targets = [
          torch.tensor([0, 0, 1, 1, 2, 2]),
          torch.tensor([0, 0, 1, 1, 2, 2]),
      ]
  
      # First batch has top-1 accuracy of 1/3.0, top-2 accuracy of 2/3.0
      # Second batch has top-1 accuracy of 2/3.0, top-2 accuracy of 3/3.0
      expected_value = {"top_1": 2 / 6.0, "top_2": 6 / 6.0}
  
      self.meter_update_and_reset_test(
          meter, model_outputs, targets, expected_value, is_train=False
      )
  
file_path: ClassyVision/test/meters_video_accuracy_meter_test.py
imports: import torch
  from classy_vision import meters
  from classy_vision.meters import VideoAccuracyMeter
  from test.generic.meter_test_utils import ClassificationMeterTest
label_desc: Use image node_id 3 for calling the TestVideoAccuracyMeter obj's underlying member method code with example usage: obj.test_double_meter_update_and_reset() without return types