class_docstr: An outcome transform representing the chaining of individual transforms
docstr: Chaining of outcome transforms.
  
  Args:
      transforms: The transforms to chain. Internally, the names of the
          kwargs are used as the keys for accessing the individual
          transforms on the module.
docstr_tok: ["Chaining","of","outcome","transforms",".","Args",":","transforms",":","The","transforms","to","chain",".","Internally",",","the","names","of","the","kwargs","are","used","as","the","keys","for","accessing","the","individual","transforms","on","the","module","."]
code: def __init__(self, **transforms: OutcomeTransform) -> None:
      r"""Chaining of outcome transforms.
  
      Args:
          transforms: The transforms to chain. Internally, the names of the
              kwargs are used as the keys for accessing the individual
              transforms on the module.
      """
      super().__init__(OrderedDict(transforms))
  
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 ChainedOutcomeTransform object from inherited base classes: OutcomeTransform, ModuleDict with example: obj = ChainedOutcomeTransform()