code: def _sample_indices(
      self, sample_size: int, max_cover: int
  ) -> Iterable[RecordIDPair]:
      weights: Dict[RecordIDPair, float] = {}
      for (
          predicate,
          covered,
      ) in self.block_learner.comparison_cover.items():
          # each predicate gets to vote for every record pair it covers. the
          # strength of that vote is in inverse proportion to the number of
          # records the predicate covers.
          #
          # if a predicate only covers a few record pairs, the value of
          # the vote it puts on those few pairs will be worth more than
          # a predicate that covers almost all the record pairs
          if len(covered) < max_cover:
              weight = 1 / len(covered)
              for pair in covered:
                  weights[pair] = weights.get(pair, 0.0) + weight
  
      sample_ids: Iterable[RecordIDPair]
      if sample_size < len(weights):
          # consider using a reservoir sampling strategy, which would
          # be more memory efficient and probably about as fast
          normalized_weights = numpy.fromiter(
              weights.values(), dtype=float, count=len(weights)
          ) / sum(weights.values())
          rng = numpy.random.default_rng()
          sample_indices = rng.choice(
              len(weights),
              size=sample_size,
              replace=False,
              p=normalized_weights,
          )
          keys = list(weights.keys())
          sample_ids = (
              (keys[i][0], keys[i][1]) for i in sample_indices
          )
      else:
          sample_ids = weights.keys()
  
      return sample_ids
  
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 7 for calling the BlockLearner obj's underlying member method code with example usage: obj._sample_indices(sample_size, max_cover) and returns: sample_ids