code: def _compute_metrics(
      true_positives: int, false_positives: int, false_negatives: int
  ):
      precision = float(true_positives) / float(
          true_positives + false_positives + 1e-13
      )
      recall = float(true_positives) / float(
          true_positives + false_negatives + 1e-13
      )
      f1_measure = 2.0 * (
          (precision * recall) / (precision + recall + 1e-13)
      )
      return precision, recall, f1_measure
  
file_path: scispacy/scispacy/per_class_scorer.py
imports: from typing import Dict, List, Tuple, Set
  from collections import defaultdict
  import copy
label_desc: Use image node_id 4 for calling the PerClassScorer obj's underlying member method code with example usage: obj._compute_metrics(true_positives, false_positives, false_negatives) and returns: precision,  recall,  f1_measure