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: Returns a string representation of the meter, used for logging.
  
  The default implementation assumes value is a dict. value is not
  required to be a dict, and in that case you should override this
  method.
docstr_tok: ["Returns","a","string","representation","of","the","meter",",","used","for","logging",".","The","default","implementation","assumes","value","is","a","dict",".","value","is","not","required","to","be","a","dict",",","and","in","that","case","you","should","override","this","method","."]
code: def __repr__(self):
      """Returns a string representation of the meter, used for logging.
  
      The default implementation assumes value is a dict. value is not
      required to be a dict, and in that case you should override this
      method."""
  
      if not isinstance(self.value, dict):
          return super().__repr__()
  
      values = ",".join(
          [f"{key}={value:.6f}" for key, value in self.value.items()]
      )
      return f"{self.name}_meter({values})"
  
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 11 for calling the ClassyMeter obj's underlying member method code with example usage: obj.__repr__() and returns: self, str, values, str, super