code: def _posterior_to_distribution_encoder(
      posterior: Posterior,
  ) -> Union[Type[Distribution], Type[Posterior]]:
      r"""An encoder returning the type of the distribution for `TorchPosterior`
      and the type of the posterior for the rest.
      """
      if isinstance(posterior, TorchPosterior):
          return type(posterior.distribution)
      return type(posterior)
  
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 1 for calling a global function with example usage: _posterior_to_distribution_encoder(posterior) and returns: type, type