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 weights of the individual models in the ensemble.
  Equally weighted by default.
docstr_tok: ["The","weights","of","the","individual","models","in","the","ensemble",".","Equally","weighted","by","default","."]
code: def weights(self) -> Tensor:
      r"""The weights of the individual models in the ensemble.
      Equally weighted by default."""
      return torch.ones(self.ensemble_size) / self.ensemble_size
  
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 3 for calling the EnsemblePosterior obj's underlying member method code with example usage: obj.weights() and returns: unknown