code: def _predict(self, pairs: TrainingExamples) -> Labels:
      labels: Labels = []
      for record_1, record_2 in pairs:
          for predicate in self.current_predicates:
              keys = predicate(record_2, target=True)
              try:
                  overlap = not keys.isdisjoint(predicate(record_1))
              except AttributeError:
                  warn(
                      f"the predicate {predicate.__name__} is not returning "
                      "a frozen set, this will soon be required behaviour",
                      DeprecationWarning,
                  )
                  overlap = not frozenset(keys).isdisjoint(
                      predicate(record_1)
                  )
              if overlap:
                  labels.append(1)
                  break
          else:
              labels.append(0)
  
      return labels
  
imports: from __future__ import annotations
  import logging
  import random
  from abc import ABC, abstractmethod
  from typing import TYPE_CHECKING, overload
  from warnings import warn
  import numpy
  import numpy.typing
  import sklearn.linear_model
  import dedupe.core
  import dedupe.training
label_desc: Use image node_id 5 for calling the BlockLearner obj's underlying member method code with example usage: obj._predict(pairs) and returns: labels