code: def _get_sampler_list(
      posterior: PosteriorList, sample_shape: torch.Size, **kwargs: Any
  ) -> MCSampler:
      r"""Get the `ListSampler` with the appropriate list of samplers."""
      samplers = [
          get_sampler(posterior=p, sample_shape=sample_shape, **kwargs)
          for p in posterior.posteriors
      ]
      return ListSampler(*samplers)
  
imports: from typing import Any, Type, Union
  import torch
  from botorch.logging import logger
  from botorch.posteriors.deterministic import DeterministicPosterior
  from botorch.posteriors.ensemble import EnsemblePosterior
  from botorch.posteriors.gpytorch import GPyTorchPosterior
  from botorch.posteriors.posterior import Posterior
  from botorch.posteriors.posterior_list import PosteriorList
  from botorch.posteriors.torch import TorchPosterior
  from botorch.posteriors.transformed import TransformedPosterior
  from botorch.sampling.base import MCSampler
  from botorch.sampling.deterministic import DeterministicSampler
  from botorch.sampling.index_sampler import IndexSampler
  from botorch.sampling.list_sampler import ListSampler
  from botorch.sampling.normal import IIDNormalSampler, NormalMCSampler, SobolQMCNormalSampler
  from botorch.utils.dispatcher import Dispatcher
  from gpytorch.distributions import MultivariateNormal
  from torch.distributions import Distribution
  from torch.quasirandom import SobolEngine
label_desc: Use image node_id 5 for calling a global function with example usage: _get_sampler_list(posterior, sample_shape) and returns: ListSampler