docstr: Parameters
  ----------
  anchors
      Anchors on which samples are conditioned.
  batch_size
      The number of samples drawn for each result.
  
  Returns
  -------
  A tuple of positive samples (for which prediction matches desired label) and a tuple of         total number of samples drawn.
docstr_tok: ["Parameters","--","--","--","--","--","anchors","Anchors","on","which","samples","are","conditioned",".","batch_size","The","number","of","samples","drawn","for","each","result",".","Returns","--","--","--","-","A","tuple","of","positive","samples","(","for","which","prediction","matches","desired","label",")","and","a","tuple","of","total","number","of","samples","drawn","."]
code: def draw_samples(
      self, anchors: list, batch_size: int
  ) -> Tuple[tuple, tuple]:
      """
          Parameters
          ----------
          anchors
              Anchors on which samples are conditioned.
          batch_size
              The number of samples drawn for each result.
  
          Returns
          -------
          A tuple of positive samples (for which prediction matches desired label) and a tuple of \
          total number of samples drawn.
          """
  
      for anchor in anchors:
          if anchor not in self.state["t_order"]:
              self.state["t_order"][anchor] = list(anchor)
  
      sample_stats: List = []
      pos: Tuple = tuple()
      total: Tuple = tuple()
      samples_iter = [
          self.sample_fcn(
              (i, tuple(self.state["t_order"][anchor])),
              num_samples=batch_size,
          )
          for i, anchor in enumerate(anchors)
      ]
      for samples, anchor in zip(samples_iter, anchors):
          covered_true, covered_false, labels, *additionals, _ = samples
          sample_stats.append(
              self.update_state(
                  covered_true,
                  covered_false,
                  labels,
                  additionals,
                  anchor,
              )
          )
          pos, total = list(zip(*sample_stats))
  
      return pos, total
  
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 10 for calling the AnchorBaseBeam obj's underlying member method code with example usage: obj.draw_samples(anchors, batch_size) and returns: pos,  total