class_docstr: Ensemble posterior, that should be used for ensemble models that compute
  eagerly a finite number of samples per X value as for example a deep ensemble
  or a random forest.
class_docstr_tok: ["Ensemble","posterior",",","that","should","be","used","for","ensemble","models","that","compute","eagerly","a","finite","number","of","samples","per","X","value","as","for","example","a","deep","ensemble","or","a","random","forest","."]
docstr: The torch dtype of the posterior.
docstr_tok: ["The","torch","dtype","of","the","posterior","."]
code: def dtype(self) -> torch.dtype:
      r"""The torch dtype of the posterior."""
      return self.values.dtype
  
file_path: botorch/botorch/posteriors/ensemble.py
imports: from __future__ import annotations
  from typing import Optional
  import torch
  from botorch.posteriors.posterior import Posterior
  from torch import Tensor
label_desc: Use image node_id 5 for calling the EnsemblePosterior obj's underlying member method code with example usage: obj.dtype() and returns: self