class_docstr: A generic transformation of a posterior (implicitly represented).
class_docstr_tok: ["A","generic","transformation","of","a","posterior","(","implicitly","represented",")","."]
docstr: The mean of the posterior as a `batch_shape x n x m`-dim Tensor.
docstr_tok: ["The","mean","of","the","posterior","as","a","`","batch_shape","x","n","x","m","`","-dim","Tensor","."]
code: def mean(self) -> Tensor:
      r"""The mean of the posterior as a `batch_shape x n x m`-dim Tensor."""
      if self._mean_transform is None:
          raise NotImplementedError("No mean transform provided.")
      try:
          variance = self._posterior.variance
      except (NotImplementedError, AttributeError):
          variance = None
      return self._mean_transform(self._posterior.mean, 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 7 for calling the TransformedPosterior obj's underlying member method code with example usage: obj.mean() and returns: self