code: def __init__(
      self,
      candidate_predicates: Iterable[Predicate],
      data: Data,
      index_include: TrainingExamples,
  ):
      super().__init__()
  
      N_SAMPLED_RECORDS = 5000
      N_SAMPLED_RECORD_PAIRS = 10000
  
      index_data = sample_records(data, 50000)
      sampled_records = sample_records(index_data, N_SAMPLED_RECORDS)
  
      preds = _filter_canopy_predicates(
          candidate_predicates, canopies=True
      )
      self.block_learner = training.DedupeBlockLearner(
          preds, sampled_records, index_data
      )
  
      self._candidates = self._sample(
          sampled_records, N_SAMPLED_RECORD_PAIRS
      )
      examples_to_index = self.candidates.copy()
  
      if index_include:
          examples_to_index += index_include
  
      self._index_predicates(examples_to_index)
  
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 DedupeBlockLearner object from inherited base classes: BlockLearner with example: obj = DedupeBlockLearner(candidate_predicates, data, index_include)