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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def predict_ipcw(self, y):
 """Return inverse probability of censoring weights at given time points. :math:`\\omega_i = \\delta_i / \\hat{G}(y_i)` Parameters y : structured array, shape = (n_samples,) A structured array containing the binary event indicator as first field, and time of event or time of censoring as second field. Returns ------- ipcw : array, shape = (n_samples,) Inverse probability of censoring weights. """

        event, time = check_y_survival(y)
        Ghat = self.predict_proba(time[event])

        if (Ghat == 0.0).any():
            raise ValueError("censoring survival function is zero at one or more time points")

        weights = numpy.zeros(time.shape[0])
        weights[event] = 1.0 / Ghat

        return weights