code: def get_sampler(
      posterior: TorchPosterior, sample_shape: torch.Size, **kwargs: Any
  ) -> MCSampler:
      r"""Get the sampler for the given posterior.
  
      The sampler can be used as `sampler(posterior)` to produce samples
      suitable for use in acquisition function optimization via SAA.
  
      Args:
          posterior: A `Posterior` to get the sampler for.
          sample_shape: The sample shape of the samples produced by the
              given sampler. The full shape of the resulting samples is
              given by `posterior._extended_shape(sample_shape)`.
          kwargs: Optional kwargs, passed down to the samplers during construction.
  
      Returns:
          The `MCSampler` object for the given posterior.
      """
      kwargs["sample_shape"] = sample_shape
      return GetSampler(posterior, **kwargs)
  
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 2 for calling a global function with example usage: get_sampler(posterior, sample_shape) and returns: GetSampler