class_docstr: Base class to measure various metrics during training and testing phases.
  
  This can include meters like  Accuracy, Precision and Recall, etc.
class_docstr_tok: ["Base","class","to","measure","various","metrics","during","training","and","testing","phases",".","This","can","include","meters","like","Accuracy",",","Precision","and","Recall",",","etc","."]
docstr: Value of meter based on local state, can be any python object.
  
  Note:
      If there are multiple training processes then this
      represents the local state of the meter. If :func:`sync_state` is
      implemented, then value will return the global state since the
      last sync PLUS any local unsynced updates that have occurred
      in the local process.
docstr_tok: ["Value","of","meter","based","on","local","state",",","can","be","any","python","object",".","Note",":","If","there","are","multiple","training","processes","then","this","represents","the","local","state","of","the","meter",".","If",":","func",":","`","sync_state","`","is","implemented",",","then","value","will","return","the","global","state","since","the","last","sync","PLUS","any","local","unsynced","updates","that","have","occurred","in","the","local","process","."]
code: def value(self) -> Any:
      """
      Value of meter based on local state, can be any python object.
  
      Note:
          If there are multiple training processes then this
          represents the local state of the meter. If :func:`sync_state` is
          implemented, then value will return the global state since the
          last sync PLUS any local unsynced updates that have occurred
          in the local process.
      """
      raise NotImplementedError
  
file_path: ClassyVision/classy_vision/meters/classy_meter.py
imports: from typing import Any, Dict, Tuple
  import torch
  from classy_vision.generic.util import log_class_usage
label_desc: Use image node_id 4 for calling the ClassyMeter obj's underlying member method code with example usage: obj.value() without return types