class_docstr: An abstraction of meter for evaluating video models.
  
  Video-level metric is computed by averaging clip-level predictions and
  compare the result with video-level groundtruth label.
  
  This meter abstraction can wrap conventional classy meters by passing
  averaged clip-level predictions to the meter needed for video level metrics.
code: def set_classy_state(self, state):
      assert (
          self.name == state["name"]
      ), "State name {state_name} does not match meter name {obj_name}".format(
          state_name=state["name"], obj_name=self.name
      )
      assert (
          self._clips_per_video_train == state["clips_per_video_train"]
      ), "incompatible clips_per_video_train for video accuracy"
      assert (
          self._clips_per_video_test == state["clips_per_video_test"]
      ), "incompatible clips_per_video_test for video accuracy"
      # Restore the state -- correct_predictions and sample_count.
      self.reset()
      self.meter.set_classy_state(state["meter_state"])
  
file_path: ClassyVision/classy_vision/meters/video_meter.py
imports: import logging
  import torch
  from classy_vision.meters import ClassyMeter
label_desc: Use image node_id 6 for calling the VideoMeter obj's underlying member method code with example usage: obj.set_classy_state(state) without return types