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`.
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docstr: The mean of the posterior as a `(b) x n x m`-dim Tensor.
  
  This is only supported if all posteriors provide a mean.
docstr_tok: ["The","mean","of","the","posterior","as","a","`","(","b",")","x","n","x","m","`","-dim","Tensor",".","This","is","only","supported","if","all","posteriors","provide","a","mean","."]
code: def mean(self) -> Tensor:
      r"""The mean of the posterior as a `(b) x n x m`-dim Tensor.
  
      This is only supported if all posteriors provide a mean.
      """
      return self._reshape_and_cat(
          tensors=[p.mean 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 9 for calling the PosteriorList obj's underlying member method code with example usage: obj.mean() and returns: self