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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 diff_aff(self):
 """Symmetric diffusion affinity matrix Return or calculate the symmetric diffusion affinity matrix .. math:: A(x,y) = K(x,y) (d(x) d(y))^{-1/2} where :math:`d` is the degrees (row sums of the kernel.) Returns ------- diff_aff : array-like, shape=[n_samples, n_samples] symmetric diffusion affinity matrix defined as a doubly-stochastic form of the kernel matrix """

        row_degrees = np.array(self.kernel.sum(axis=1)).reshape(-1, 1)
        col_degrees = np.array(self.kernel.sum(axis=0)).reshape(1, -1)
        if sparse.issparse(self.kernel):
            return self.kernel.multiply(1 / np.sqrt(row_degrees)).multiply(
                1 / np.sqrt(col_degrees))
        else:
            return (self.kernel / np.sqrt(row_degrees)) / np.sqrt(col_degrees)