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: Return the standard deviation of the metric.
docstr_tok: ["Return","the","standard","deviation","of","the","metric","."]
code: def standard_dev(self):
      """Return the standard deviation of the metric."""
      result = torch.FloatTensor(self.calculate_result())
      if result.numel() == result.size(0):
          return result.std(0).cpu().numpy().item()
      else:
          return result.std(0).cpu().numpy()
  
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 7 for calling the Metrics obj's underlying member method code with example usage: obj.standard_dev() and returns: result, result