class_docstr: Abstract base class for outcome transforms.
  
  :meta private:
docstr: Un-transform a posterior.
  
  Posteriors with `_is_linear=True` should return a `GPyTorchPosterior` when
  `posterior` is a `GPyTorchPosterior`. Posteriors with `_is_linear=False`
  likely return a `TransformedPosterior` instead.
  
  Args:
      posterior: A posterior in the transformed space.
  
  Returns:
      The un-transformed posterior.
docstr_tok: ["Un-transform","a","posterior",".","Posteriors","with","`","_is_linear=True","`","should","return","a","`","GPyTorchPosterior","`","when","`","posterior","`","is","a","`","GPyTorchPosterior","`",".","Posteriors","with","`","_is_linear=False","`","likely","return","a","`","TransformedPosterior","`","instead",".","Args",":","posterior",":","A","posterior","in","the","transformed","space",".","Returns",":","The","un-transformed","posterior","."]
code: def untransform_posterior(self, posterior: Posterior) -> Posterior:
      r"""Un-transform a posterior.
  
      Posteriors with `_is_linear=True` should return a `GPyTorchPosterior` when
      `posterior` is a `GPyTorchPosterior`. Posteriors with `_is_linear=False`
      likely return a `TransformedPosterior` instead.
  
      Args:
          posterior: A posterior in the transformed space.
  
      Returns:
          The un-transformed posterior.
      """
      raise NotImplementedError(
          f"{self.__class__.__name__} does not implement the "
          "`untransform_posterior` method"
      )
  
imports: from __future__ import annotations
  import warnings
  from abc import ABC, abstractmethod
  from collections import OrderedDict
  from typing import Any, List, Mapping, Optional, Tuple, Union
  import torch
  from botorch.models.transforms.utils import norm_to_lognorm_mean, norm_to_lognorm_variance
  from botorch.posteriors import GPyTorchPosterior, Posterior, TransformedPosterior
  from botorch.utils.transforms import normalize_indices
  from linear_operator.operators import CholLinearOperator, DiagLinearOperator
  from torch import Tensor
  from torch.nn import Module, ModuleDict
label_desc: Use image node_id 5 for calling the OutcomeTransform obj's underlying member method code with example usage: obj.untransform_posterior(posterior) without return types