class_docstr: An abstract class representing a metric which accumulates TPs, FPs, and FNs.
  
  :param average: This determines the type of averaging performed            on the data: 'micro' (calculate metrics globally by
  counting the total true positives, false negatives and false positives)            , 'macro' (calculate metrics for each label,
  and find their unweighted mean).
class_docstr_tok: ["An","abstract","class","representing","a","metric","which","accumulates","TPs",",","FPs",",","and","FNs",".",":","param","average",":","This","determines","the","type","of","averaging","performed","on","the","data",":","'micro","'","(","calculate","metrics","globally","by","counting","the","total","true","positives",",","false","negatives","and","false","positives",")",",","'macro","'","(","calculate","metrics","for","each","label",",","and","find","their","unweighted","mean",")","."]
docstr: Reset any accumulators or internal state.
docstr_tok: ["Reset","any","accumulators","or","internal","state","."]
code: def reset(self) -> None:
      """
      Reset any accumulators or internal state.
      """
      self._true_positives: Dict[str, float] = defaultdict(float)
      self._false_positives: Dict[str, float] = defaultdict(float)
      self._false_negatives: Dict[str, float] = defaultdict(float)
  
imports: from collections import defaultdict
  from copy import copy
  from typing import Dict, List, Set, Tuple
  from .distance import Distance
label_desc: Use image node_id 4 for calling the ErrorMetric obj's underlying member method code with example usage: obj.reset() without return types