code: def test_meter_invalid_target(self):
      meter = VideoAccuracyMeter(
          topk=[1, 2], clips_per_video_train=1, clips_per_video_test=2
      )
      model_output = torch.tensor(
          [
              [3, 2, 1],
              [3, 1, 2],
              [1, 2, 2],
              [1, 2, 3],
              [2, 2, 2],
              [1, 3, 2],
          ],
          dtype=torch.float,
      )
      # Target has 3 dimensions instead of expected 1 or 2
      target = torch.tensor([[[0, 1, 2], [0, 1, 2]]])
  
      self.meter_invalid_meter_input_test(meter, model_output, target)
      # Target of clips from the same video is not consistent
      target = torch.tensor([0, 2, 1, 1, 2, 2])
  
      self.meter_invalid_update_test(
          meter, model_output, target, 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 5 for calling the TestVideoAccuracyMeter obj's underlying member method code with example usage: obj.test_meter_invalid_target() without return types