class_docstr: A generic transformation of a posterior (implicitly represented).
class_docstr_tok: ["A","generic","transformation","of","a","posterior","(","implicitly","represented",")","."]
docstr: The variance of the posterior as a `batch_shape x n x m`-dim Tensor.
docstr_tok: ["The","variance","of","the","posterior","as","a","`","batch_shape","x","n","x","m","`","-dim","Tensor","."]
code: def variance(self) -> Tensor:
      r"""The variance of the posterior as a `batch_shape x n x m`-dim Tensor."""
      if self._variance_transform is None:
          raise NotImplementedError("No variance transform provided.")
      return self._variance_transform(
          self._posterior.mean, self._posterior.variance
      )
  
file_path: botorch/botorch/posteriors/transformed.py
imports: from __future__ import annotations
  from typing import Callable, Optional, Tuple
  import torch
  from botorch.posteriors.posterior import Posterior
  from torch import Tensor
label_desc: Use image node_id 8 for calling the TransformedPosterior obj's underlying member method code with example usage: obj.variance() and returns: self