class_docstr: Bilog-transform outcomes.
  
  The Bilog transform [eriksson2021scalable]_ is useful for modeling outcome
  constraints as it magnifies values near zero and flattens extreme values.
docstr: Subset the transform along the output dimension.
  
  Args:
      idcs: The output indices to subset the transform to.
  
  Returns:
      The current outcome transform, subset to the specified output indices.
docstr_tok: ["Subset","the","transform","along","the","output","dimension",".","Args",":","idcs",":","The","output","indices","to","subset","the","transform","to",".","Returns",":","The","current","outcome","transform",",","subset","to","the","specified","output","indices","."]
code: def subset_output(self, idcs: List[int]) -> OutcomeTransform:
      r"""Subset the transform along the output dimension.
  
      Args:
          idcs: The output indices to subset the transform to.
  
      Returns:
          The current outcome transform, subset to the specified output indices.
      """
      new_outputs = None
      if self._outputs is not None:
          if min(self._outputs + idcs) < 0:
              raise NotImplementedError(
                  f"Negative indexing not supported for {self.__class__.__name__} "
                  "when subsetting outputs and only transforming some outputs."
              )
          new_outputs = [i for i in self._outputs if i in idcs]
      new_tf = self.__class__(outputs=new_outputs)
      if not self.training:
          new_tf.eval()
      return new_tf
  
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 2 for calling the Bilog obj's underlying member method code with example usage: obj.subset_output(idcs) and returns: new_tf