docstr: Draws samples uniformly at random from the training set.
  
  Parameters
  ---------
  coverage_samples
      See :py:meth:`alibi.explainers.anchors.anchor_base.AnchorBaseBeam.anchor_beam` method.
  samplers
      See :py:meth:`alibi.explainers.anchors.anchor_base.AnchorBaseBeam.__init__` method.
  
  Returns
  -------
  coverage_data
      Binarised samples, where 1 indicates the feature has same value/is in same beam as
      instance to be explained. Used to determine, e.g., which samples an result applies to.
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code: def _get_coverage_samples(
      self,
      coverage_samples: int,
      samplers: Optional[List[Callable]] = None,
  ) -> np.ndarray:
      """
      Draws samples uniformly at random from the training set.
  
      Parameters
      ---------
      coverage_samples
          See :py:meth:`alibi.explainers.anchors.anchor_base.AnchorBaseBeam.anchor_beam` method.
      samplers
          See :py:meth:`alibi.explainers.anchors.anchor_base.AnchorBaseBeam.__init__` method.
  
      Returns
      -------
      coverage_data
          Binarised samples, where 1 indicates the feature has same value/is in same beam as
          instance to be explained. Used to determine, e.g., which samples an result applies to.
      """
  
      [coverage_data] = self.sample_fcn(
          (0, ()), coverage_samples, compute_labels=False
      )
  
      return coverage_data
  
imports: import copy
  import logging
  from collections import defaultdict, namedtuple
  from typing import Callable, Dict, List, Optional, Set, Tuple
  import numpy
  from alibi.utils.distributions import kl_bernoulli
label_desc: Use image node_id 7 for calling the AnchorBaseBeam obj's underlying member method code with example usage: obj._get_coverage_samples(coverage_samples, samplers) and returns: coverage_data