prototypes/dml_iv/dr_iv.py [232:253]:
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                                   opt_reweighted=opt_reweighted)
        return

    def _check_inputs(self, y, T, X, Z):
        if len(Z.shape) > 1 and Z.shape[1] > 1:
            raise AssertionError(
                "Can only accept single dimensional instrument")
        if len(T.shape) > 1 and T.shape[1] > 1:
            raise AssertionError(
                "Can only accept single dimensional treatment")
        if len(y.shape) > 1 and y.shape[1] > 1:
            raise AssertionError("Can only accept single dimensional outcome")
        Z = Z.flatten()
        T = T.flatten()
        y = y.flatten()
        return y, T, X, Z

    def _nuisance_estimates(self, y, T, X, Z):
        n_samples = y.shape[0]
        prel_theta = np.zeros(n_samples)
        res_t = np.zeros(n_samples)
        res_y = np.zeros(n_samples)
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prototypes/dml_iv/dr_iv.py [419:440]:
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                                                 opt_reweighted=opt_reweighted)
        return

    def _check_inputs(self, y, T, X, Z):
        if len(Z.shape) > 1 and Z.shape[1] > 1:
            raise AssertionError(
                "Can only accept single dimensional instrument")
        if len(T.shape) > 1 and T.shape[1] > 1:
            raise AssertionError(
                "Can only accept single dimensional treatment")
        if len(y.shape) > 1 and y.shape[1] > 1:
            raise AssertionError("Can only accept single dimensional outcome")
        Z = Z.flatten()
        T = T.flatten()
        y = y.flatten()
        return y, T, X, Z

    def _nuisance_estimates(self, y, T, X, Z):
        n_samples = y.shape[0]
        prel_theta = np.zeros(n_samples)
        res_t = np.zeros(n_samples)
        res_y = np.zeros(n_samples)
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