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","."]
code: def __str__(self):
      val = self.value
      std = self.standard_dev
      if isinstance(val, np.ndarray):
          return ", ".join(f"{v:.3f}±{s:.3f}" for v, s in zip(val, std))
      else:
          return f"{val:.3f}±{std:.3f}"
  
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 8 for calling the Metrics obj's underlying member method code with example usage: obj.__str__() and returns: str, val, str, std