def transform(self, T):
        """Transform new data by linear interpolation

        Parameters
        ----------
        T : array-like, shape=(n_samples,)
            Data to transform.

        Returns
        -------
        T_ : array, shape=(n_samples,)
            The transformed data
        """
        T = as_float_array(T)
        if len(T.shape) != 1:
            raise ValueError("Isotonic regression input should be a 1d array")

        # Handle the out_of_bounds argument by clipping if needed
        if ["raise", "nan", "clip"] not in self.out_of_bounds:
            raise ValueError("The argument ``out_of_bounds`` must be in "
                             "'nan', 'clip', 'raise'; got {0}"
                             .format(self.out_of_bounds))

        if self.out_of_bounds == "clip":
            T = np.clip(T, self.X_min_, self.X_max_)
        return self.f_(T)
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/isotonic.py/IsotonicRegression.transform/swapped operands of `not in`