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: Every video meter should implement to have its own internal meter.
  
  It consumes the video level predictions and ground truth label, and compute
  the actual metrics.
  
  Returns:
      An instance of ClassyMeter.
docstr_tok: ["Every","video","meter","should","implement","to","have","its","own","internal","meter",".","It","consumes","the","video","level","predictions","and","ground","truth","label",",","and","compute","the","actual","metrics",".","Returns",":","An","instance","of","ClassyMeter","."]
code: def meter(self) -> "ClassyMeter":
      """Every video meter should implement to have its own internal meter.
  
      It consumes the video level predictions and ground truth label, and compute
      the actual metrics.
  
      Returns:
          An instance of ClassyMeter.
      """
      raise NotImplementedError
  
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 4 for calling the VideoMeter obj's underlying member method code with example usage: obj.meter() without return types