class_docstr: Abstract base class for objectives that transform the posterior.
  
  :meta private:
class_docstr_tok: ["Abstract","base","class","for","objectives","that","transform","the","posterior",".",":","meta","private",":"]
docstr: Evaluate the transform on a set of outcomes.
  
  Args:
      Y: A `batch_shape x q x m`-dim tensor of outcomes.
  
  Returns:
      A `batch_shape x q' [x m']`-dim tensor of transformed outcomes.
code: def evaluate(self, Y: Tensor) -> Tensor:
      r"""Evaluate the transform on a set of outcomes.
  
      Args:
          Y: A `batch_shape x q x m`-dim tensor of outcomes.
  
      Returns:
          A `batch_shape x q' [x m']`-dim tensor of transformed outcomes.
      """
      pass
  
imports: from __future__ import annotations
  import inspect
  import warnings
  from abc import ABC, abstractmethod
  from typing import Callable, List, Optional, TYPE_CHECKING, Union
  import torch
  from botorch.exceptions.errors import BotorchTensorDimensionError, UnsupportedError
  from botorch.exceptions.warnings import InputDataWarning
  from botorch.models.model import Model
  from botorch.models.transforms.outcome import Standardize
  from botorch.posteriors.gpytorch import GPyTorchPosterior, scalarize_posterior
  from botorch.sampling import IIDNormalSampler
  from botorch.utils import apply_constraints
  from gpytorch.distributions import MultitaskMultivariateNormal, MultivariateNormal
  from linear_operator.operators.dense_linear_operator import to_linear_operator
  from torch import Tensor
  from torch.nn import Module
  from botorch.models.deterministic import DeterministicModel
label_desc: Use image node_id 1 for calling the PosteriorTransform obj's underlying member method code with example usage: obj.evaluate(Y) without return types