code: def __init__(
      self,
      candidate_predicates: Iterable[Predicate],
      featurizer: FeaturizerFunction,
      data_1: Data,
      data_2: Data,
      index_include: TrainingExamples,
  ):
      super().__init__()
      data_1 = core.index(data_1)
  
      offset = len(data_1)
      data_2 = core.index(data_2, offset)
  
      random_pair = (
          random.choice(list(data_1.values())),
          random.choice(list(data_2.values())),
      )
      exact_match = (random_pair[0], random_pair[0])
  
      index_include = index_include.copy()
      index_include.append(exact_match)
  
      self.blocker = RecordLinkBlockLearner(
          candidate_predicates, data_1, data_2, index_include
      )
      self._candidates = self.blocker.candidates.copy()
  
      self.matcher = MatchLearner(featurizer, self.candidates)
  
      examples = [exact_match] * 4 + [random_pair]
      labels: Labels = [1] * 4 + [0]  # type: ignore[assignment]
      self.mark(examples, 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 1 to create a new RecordLinkDisagreementLearner object from inherited base classes: DisagreementLearner with example: obj = RecordLinkDisagreementLearner(candidate_predicates, featurizer, data_1, data_2, index_include)