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: Updates any internal state of meter.
  
  Should be called after each batch processing of each phase.
  
  Args:
      model_output: Output of a :class:`ClassyModel`.
      target: Target provided by a dataloader from :class:`ClassyDataset`.
docstr_tok: ["Updates","any","internal","state","of","meter",".","Should","be","called","after","each","batch","processing","of","each","phase",".","Args",":","model_output",":","Output","of","a",":","class",":","`","ClassyModel","`",".","target",":","Target","provided","by","a","dataloader","from",":","class",":","`","ClassyDataset","`","."]
code: def update(
      self, model_output: torch.Tensor, target: torch.Tensor, **kwargs
  ) -> None:
      """
      Updates any internal state of meter.
  
      Should be called after each batch processing of each phase.
  
      Args:
          model_output: Output of a :class:`ClassyModel`.
          target: Target provided by a dataloader from :class:`ClassyDataset`.
      """
      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 7 for calling the ClassyMeter obj's underlying member method code with example usage: obj.update(model_output, target) without return types