docstr: Update the sampler to use the original base samples for X_baseline.
  
  This is used in CachedCholeskyAcquisitionFunctions to ensure consistency.
  
  Args:
      posterior: The posterior for which the base samples are constructed.
      base_sampler: The base sampler to retrieve the base samples from.
docstr_tok: ["Update","the","sampler","to","use","the","original","base","samples","for","X_baseline",".","This","is","used","in","CachedCholeskyAcquisitionFunctions","to","ensure","consistency",".","Args",":","posterior",":","The","posterior","for","which","the","base","samples","are","constructed",".","base_sampler",":","The","base","sampler","to","retrieve","the","base","samples","from","."]
code: def _update_base_samples(
      self, posterior: PosteriorList, base_sampler: ListSampler
  ) -> None:
      r"""Update the sampler to use the original base samples for X_baseline.
  
      This is used in CachedCholeskyAcquisitionFunctions to ensure consistency.
  
      Args:
          posterior: The posterior for which the base samples are constructed.
          base_sampler: The base sampler to retrieve the base samples from.
      """
      self._instance_check(base_sampler=base_sampler)
      for s, p, bs in zip(
          self.samplers, posterior.posteriors, base_sampler.samplers
      ):
          s._update_base_samples(posterior=p, base_sampler=bs)
  
file_path: botorch/botorch/sampling/list_sampler.py
imports: from __future__ import annotations
  import torch
  from botorch.exceptions.errors import UnsupportedError
  from botorch.posteriors.posterior_list import PosteriorList
  from botorch.sampling.base import MCSampler
  from torch import Tensor
  from torch.nn import ModuleList
label_desc: Use image node_id 5 for calling the ListSampler obj's underlying member method code with example usage: obj._update_base_samples(posterior, base_sampler) without return types