class_docstr: metric is an abstract class.
  Args:
      average (bool): a way to output one single value for metrics
                      that are calculated in several trials.
class_docstr_tok: ["metric","is","an","abstract","class",".","Args",":","average","(","bool",")",":","a","way","to","output","one","single","value","for","metrics","that","are","calculated","in","several","trials","."]
docstr: Main calculation of the metric which updated the private values respectively.
  
  Args:
      output (tensor): predictions of model
      target (tensor): labels
docstr_tok: ["Main","calculation","of","the","metric","which","updated","the","private","values","respectively",".","Args",":","output","(","tensor",")",":","predictions","of","model","target","(","tensor",")",":","labels"]
code: def update(self, output=None, target=None):
      """
      Main calculation of the metric which updated the private values respectively.
  
      Args:
          output (tensor): predictions of model
          target (tensor): labels
      """
      raise NotImplementedError
  
imports: import dataclasses
  import math
  import warnings
  from collections import defaultdict
  import numpy
  import torch
  from sklearn.metrics import confusion_matrix, auc
  from baal.utils.array_utils import to_prob
label_desc: Use image node_id 3 for calling the Metrics obj's underlying member method code with example usage: obj.update(output, target) without return types