class_docstr: Subclasses AttributionPriorExplainer to avoid pre-specified batch size. Will adapt batch
  size based on shape of input tensor.
docstr: Arguments:
  refs_needed: int - number of references to provide
docstr_tok: ["Arguments",":","refs_needed",":","int","-","number","of","references","to","provide"]
code: def _get_ref_batch(self, refs_needed=None):
      """
      Arguments:
      refs_needed: int - number of references to provide
      """
      if refs_needed != self.refs_needed:
          self.ref_sampler = DataLoader(
              dataset=self.ref_set,
              batch_size=refs_needed,
              shuffle=True,
              drop_last=True,
          )
          self.refs_needed = refs_needed
      return next(iter(self.ref_sampler))[0].float()
  
imports: import functools
  import operator
  import torch
  from torch.autograd import grad
  from torch.utils.data import DataLoader
label_desc: Use image node_id 2 for calling the VariableBatchExplainer obj's underlying member method code with example usage: obj._get_ref_batch(refs_needed) and returns: next