class_docstr: A mix-in for adding sampler functionality into an acquisition function class.
  
  Attributes:
      _default_sample_shape: The `sample_shape` for the default sampler.
  
  :meta private:
docstr: Sample from the posterior using the sampler.
  
  Args:
      posterior: The posterior to sample from.
docstr_tok: ["Sample","from","the","posterior","using","the","sampler",".","Args",":","posterior",":","The","posterior","to","sample","from","."]
code: def get_posterior_samples(self, posterior: Posterior) -> Tensor:
      r"""Sample from the posterior using the sampler.
  
      Args:
          posterior: The posterior to sample from.
      """
      if self.sampler is None:
          self.sampler = get_sampler(
              posterior=posterior,
              sample_shape=self._default_sample_shape,
          )
      return self.sampler(posterior=posterior)
  
imports: from __future__ import annotations
  import warnings
  from abc import ABC, abstractmethod
  from typing import Optional
  import torch
  from botorch.exceptions import BotorchWarning
  from botorch.models.model import Model, ModelDict
  from botorch.posteriors.posterior import Posterior
  from botorch.sampling.base import MCSampler
  from botorch.sampling.get_sampler import get_sampler
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 2 for calling the MCSamplerMixin obj's underlying member method code with example usage: obj.get_posterior_samples(posterior) and returns: self