botorch/models/gp_regression_fidelity.py [116:140]:
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            covar_module=covar_module,
            outcome_transform=outcome_transform,
            input_transform=input_transform,
        )
        self._subset_batch_dict = {
            "likelihood.noise_covar.raw_noise": -2,
            "mean_module.constant": -2,
            "covar_module.raw_outputscale": -1,
            **subset_batch_dict,
        }
        self.to(train_X)

    @classmethod
    def construct_inputs(cls, training_data: TrainingData, **kwargs) -> Dict[str, Any]:
        r"""Construct kwargs for the `Model` from `TrainingData` and other options.

        Args:
            training_data: `TrainingData` container with data for single outcome
                or for multiple outcomes for batched multi-output case.
            **kwargs: Options, expected for this class:
                - fidelity_features: List of columns of X that are fidelity parameters.
        """
        fidelity_features = kwargs.get("fidelity_features")
        if fidelity_features is None:
            raise ValueError(f"Fidelity features required for {cls.__name__}.")
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botorch/models/gp_regression_fidelity.py [225:249]:
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            covar_module=covar_module,
            outcome_transform=outcome_transform,
            input_transform=input_transform,
        )
        self._subset_batch_dict = {
            "likelihood.noise_covar.raw_noise": -2,
            "mean_module.constant": -2,
            "covar_module.raw_outputscale": -1,
            **subset_batch_dict,
        }
        self.to(train_X)

    @classmethod
    def construct_inputs(cls, training_data: TrainingData, **kwargs) -> Dict[str, Any]:
        r"""Construct kwargs for the `Model` from `TrainingData` and other options.

        Args:
            training_data: `TrainingData` container with data for single outcome
                or for multiple outcomes for batched multi-output case.
            **kwargs: Options, expected for this class:
                - fidelity_features: List of columns of X that are fidelity parameters.
        """
        fidelity_features = kwargs.get("fidelity_features")
        if fidelity_features is None:
            raise ValueError(f"Fidelity features required for {cls.__name__}.")
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