ax/models/torch/cbo_lcea.py [135:161]:
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    def get_and_fit_model(
        self,
        Xs: List[Tensor],
        Ys: List[Tensor],
        Yvars: List[Tensor],
        task_features: List[int],
        fidelity_features: List[int],
        metric_names: List[str],
        state_dict: Optional[Dict[str, Tensor]] = None,
        fidelity_model_id: Optional[int] = None,
        **kwargs: Any,
    ) -> GPyTorchModel:
        """Get a fitted LCEAGP model for each outcome.
        Args:
            Xs: X for each outcome.
            Ys: Y for each outcome.
            Yvars: Noise variance of Y for each outcome.
        Returns: Fitted LCEAGP model.
        """
        # generate model space decomposition dict
        decomp_index = generate_model_space_decomposition(
            decomposition=self.decomposition, feature_names=self.feature_names
        )

        models = []
        for i, X in enumerate(Xs):
            Yvar = Yvars[i].clamp_min_(MIN_OBSERVED_NOISE_LEVEL)
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ax/models/torch/cbo_sac.py [68:94]:
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    def get_and_fit_model(
        self,
        Xs: List[Tensor],
        Ys: List[Tensor],
        Yvars: List[Tensor],
        task_features: List[int],
        fidelity_features: List[int],
        metric_names: List[str],
        state_dict: Optional[Dict[str, Tensor]] = None,
        fidelity_model_id: Optional[int] = None,
        **kwargs: Any,
    ) -> GPyTorchModel:
        """Get a fitted StructuralAdditiveContextualGP model for each outcome.
        Args:
            Xs: X for each outcome.
            Ys: Y for each outcome.
            Yvars: Noise variance of Y for each outcome.
        Returns: Fitted StructuralAdditiveContextualGP model.
        """
        # generate model space decomposition dict
        decomp_index = generate_model_space_decomposition(
            decomposition=self.decomposition, feature_names=self.feature_names
        )

        models = []
        for i, X in enumerate(Xs):
            Yvar = Yvars[i].clamp_min_(MIN_OBSERVED_NOISE_LEVEL)
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