class_docstr: Power-transform outcomes.
  
  Useful if the targets are modeled using a (multivariate) power transform of
  a Normal distribution. This means that we can use a standard GP model on the
  power-transformed outcomes and un-transform the model posterior of that GP.
docstr: Power-transform outcomes.
  
  Args:
      outputs: Which of the outputs to power-transform. If omitted, all
          outputs will be standardized.
docstr_tok: ["Power-transform","outcomes",".","Args",":","outputs",":","Which","of","the","outputs","to","power-transform",".","If","omitted",",","all","outputs","will","be","standardized","."]
code: def __init__(
      self, power: float, outputs: Optional[List[int]] = None
  ) -> None:
      r"""Power-transform outcomes.
  
      Args:
          outputs: Which of the outputs to power-transform. If omitted, all
              outputs will be standardized.
      """
      super().__init__()
      self._outputs = outputs
      self.power = power
  
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 1 to create a new Power object from inherited base classes: OutcomeTransform with example: obj = Power(power, outputs)