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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 _pseudo_data(self, y, lp, mu):
 """ compute the pseudo data for a PIRLS iterations Parameters --------- y : array-like of shape (n,) containing target data lp : array-like of shape (n,) containing linear predictions by the model mu : array-like of shape (n_samples,) expected value of the targets given the model and inputs Returns ------- pseudo_data : np.array of shape (n,) """

        return lp + (y - mu) * self.link.gradient(mu, self.distribution)