code: def update(self, predicted, gold):
      self.tp += len(set(predicted) & set(gold))
      self.fn += len(set(gold) - set(predicted))
      self.fp += len(set(predicted) - set(gold))
  
      return
  
imports: import logging
  import numpy
  from collections import Counter, defaultdict
  from scipy.optimize import linear_sum_assignment
  from .blanc import blanc, tuple_to_metric
label_desc: Use image node_id 2 for calling the NEREvaluator obj's underlying member method code with example usage: obj.update(predicted, gold) without return types