pplbench/ppls/stan/logistic_regression.py [28:43]:
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        attrs["X"] = data.X.values
        attrs["Y"] = data.Y.values
        return attrs

    def extract_data_from_stan(self, samples: Dict) -> xr.Dataset:
        """
        Takes the output of Stan and converts into a format expected
        by PPLBench.
        :param samples: samples dictionary from Stan
        :returns: a dataset over inferred parameters
        """
        # dim 1 is the chains dimension so we squeeze it out
        return xr.Dataset(
            {
                "alpha": (["draw"], samples["alpha"].squeeze(1)),
                "beta": (["draw", "feature"], samples["beta"].squeeze(1)),
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pplbench/ppls/stan/robust_regression.py [28:43]:
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        attrs["X"] = data.X.values
        attrs["Y"] = data.Y.values
        return attrs

    def extract_data_from_stan(self, samples: Dict) -> xr.Dataset:
        """
        Takes the output of Stan and converts into a format expected
        by PPLBench.
        :param samples: samples dictionary from Stan
        :returns: a dataset over inferred parameters
        """
        # dim 1 is the chains dimension so we squeeze it out
        return xr.Dataset(
            {
                "alpha": (["draw"], samples["alpha"].squeeze(1)),
                "beta": (["draw", "feature"], samples["beta"].squeeze(1)),
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