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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_cumulative_hazard(self, X):
 """ Returns the hazard rates for the individuals Parameters X: a (n,d) covariate numpy array or DataFrame. If a DataFrame, columns can be in any order. If a numpy array, columns must be in the same order as the training data. """

        n, _ = X.shape

        cols = _get_index(X)
        if isinstance(X, pd.DataFrame):
            order = self.cumulative_hazards_.columns
            order = order.drop("_intercept") if self.fit_intercept else order
            X_ = X[order].values
        else:
            X_ = X

        X_ = X_ if not self.fit_intercept else np.c_[X_, np.ones((n, 1))]

        timeline = self._index
        individual_cumulative_hazards_ = pd.DataFrame(
            np.dot(self.cumulative_hazards_, X_.T), index=timeline, columns=cols
        )

        return individual_cumulative_hazards_