class_docstr: A Posterior represented by a list of independent Posteriors.
  
  When at least one of the posteriors is a `GaussianMixturePosterior`, the other
  posteriors are expanded to match the size of the `GaussianMixturePosterior`.
class_docstr_tok: ["A","Posterior","represented","by","a","list","of","independent","Posteriors",".","When","at","least","one","of","the","posteriors","is","a","`","GaussianMixturePosterior","`",",","the","other","posteriors","are","expanded","to","match","the","size","of","the","`","GaussianMixturePosterior","`","."]
docstr: Check if any of the posteriors is a `GaussianMixturePosterior`.
docstr_tok: ["Check","if","any","of","the","posteriors","is","a","`","GaussianMixturePosterior","`","."]
code: def _is_gaussian_mixture(self) -> bool:
      r"""Check if any of the posteriors is a `GaussianMixturePosterior`."""
      return any(
          isinstance(p, GaussianMixturePosterior)
          for p in self.posteriors
      )
  
imports: from __future__ import annotations
  from functools import cached_property
  from typing import Any, List, Optional
  import torch
  from botorch.posteriors.fully_bayesian import GaussianMixturePosterior, MCMC_DIM
  from botorch.posteriors.posterior import Posterior
  from torch import Tensor
label_desc: Use image node_id 2 for calling the PosteriorList obj's underlying member method code with example usage: obj._is_gaussian_mixture() and returns: any