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.
docstr: Contains the states of the meter.
docstr_tok: ["Contains","the","states","of","the","meter","."]
code: def get_classy_state(self):
      """Contains the states of the meter."""
      state = {}
      state["meter_state"] = self.meter.get_classy_state()
      state["name"] = self.name
      state["clips_per_video_train"] = self._clips_per_video_train
      state["clips_per_video_test"] = self._clips_per_video_test
      return 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 5 for calling the VideoMeter obj's underlying member method code with example usage: obj.get_classy_state() and returns: state