class_docstr: A dummy deterministic model that uses transforms.
docstr: Args:
      outcome_transform: An outcome transform that is applied to the
          training data during instantiation and to the posterior during
          inference (that is, the `Posterior` obtained by calling
          `.posterior` on the model will be on the original scale).
      input_transform: An input transform that is applied in the model's
          forward pass. Only input transforms are allowed which do not
          transform the categorical dimensions. This can be achieved
          by using the `indices` argument when constructing the transform.
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code: def __init__(self, outcome_transform, input_transform):
      r"""
      Args:
          outcome_transform: An outcome transform that is applied to the
              training data during instantiation and to the posterior during
              inference (that is, the `Posterior` obtained by calling
              `.posterior` on the model will be on the original scale).
          input_transform: An input transform that is applied in the model's
              forward pass. Only input transforms are allowed which do not
              transform the categorical dimensions. This can be achieved
              by using the `indices` argument when constructing the transform.
      """
      super().__init__()
      self.input_transform = input_transform
      self.outcome_transform = outcome_transform
  
imports: import warnings
  import torch
  from botorch.acquisition.objective import ScalarizedPosteriorTransform
  from botorch.exceptions.errors import UnsupportedError
  from botorch.models.deterministic import AffineDeterministicModel, DeterministicModel, FixedSingleSampleModel, GenericDeterministicModel, PosteriorMeanModel
  from botorch.models.gp_regression import SingleTaskGP
  from botorch.models.transforms.input import Normalize
  from botorch.models.transforms.outcome import Standardize
  from botorch.posteriors.ensemble import EnsemblePosterior
  from botorch.utils.testing import BotorchTestCase
label_desc: Use image node_id 1 to create a new DummyDeterministicModel object from inherited base classes: DeterministicModel with example: obj = DummyDeterministicModel(outcome_transform, input_transform)