class_docstr: Abstract base class for outcome transforms.
  
  :meta private:
docstr: True for transformations such as `Standardize`; these should be able to apply
  `untransform_posterior` to a GPyTorchPosterior and return a GPyTorchPosterior,
  because a multivariate normal distribution should remain multivariate normal
  after applying the transform.
docstr_tok: ["True","for","transformations","such","as","`","Standardize","`",";","these","should","be","able","to","apply","`","untransform_posterior","`","to","a","GPyTorchPosterior","and","return","a","GPyTorchPosterior",",","because","a","multivariate","normal","distribution","should","remain","multivariate","normal","after","applying","the","transform","."]
code: def _is_linear(self) -> bool:
      """
      True for transformations such as `Standardize`; these should be able to apply
      `untransform_posterior` to a GPyTorchPosterior and return a GPyTorchPosterior,
      because a multivariate normal distribution should remain multivariate normal
      after applying the transform.
      """
      return False
  
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 4 for calling the OutcomeTransform obj's underlying member method code with example usage: obj._is_linear() and returns: False