code: def random_forest_candidates(
      self,
      match_cover: Cover,
      comparison_cover: ComparisonCover,
      K: int | None = None,
  ) -> Cover:
      predicates = list(match_cover)
      matches = list(frozenset.union(*match_cover.values()))
      pred_sample_size = max(int(math.sqrt(len(predicates))), 5)
      candidates = {}
      if K is None:
          K = max(math.floor(math.log10(len(matches))), 1)
  
      n_samples = 5000
      for _ in range(n_samples):
          sample_predicates = random.sample(
              predicates, pred_sample_size
          )
          resampler = Resampler(matches)
          sample_match_cover = {
              pred: resampler(pairs)
              for pred, pairs in match_cover.items()
          }
  
          # initialize variables that will be
          # the base for the constructing k-conjunctions
          candidate = None
          covered_comparisons: frozenset[
              RecordIDPair
          ] | InfiniteSet = InfiniteSet()
          covered_matches: frozenset[int] | InfiniteSet = InfiniteSet()
          covered_sample_matches = InfiniteSet()
  
          def score(predicate: Predicate) -> float:
              try:
                  return len(
                      covered_sample_matches
                      & sample_match_cover[predicate]
                  ) / len(
                      covered_comparisons & comparison_cover[predicate]
                  )
              except ZeroDivisionError:
                  return 0.0
  
          for _ in range(K):
              next_predicate = max(sample_predicates, key=score)
              if candidate:
                  candidate += next_predicate
              else:
                  candidate = next_predicate
  
              covered_comparisons &= comparison_cover[next_predicate]
              candidate.cover_count = len(covered_comparisons)
  
              covered_matches &= match_cover[next_predicate]
              candidates[candidate] = covered_matches
  
              covered_sample_matches &= sample_match_cover[
                  next_predicate
              ]
  
              sample_predicates.remove(next_predicate)
  
      return candidates
  
imports: from __future__ import annotations
  import collections
  import functools
  import itertools
  import logging
  import math
  import random
  from abc import ABC
  from typing import TYPE_CHECKING, overload
  from warnings import warn
  from .None import blocking, branch_and_bound
label_desc: Use image node_id 3 for calling the BlockLearner obj's underlying member method code with example usage: obj.random_forest_candidates(match_cover, comparison_cover, K) and returns: candidates, unknown, int