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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 compute(self, x, yerr):
 """ Compute and factorize the covariance matrix. Args: x (ndarray[nsamples, ndim]):
 The independent coordinates of the data points. yerr (ndarray[nsamples] or float):
 The Gaussian uncertainties on the data points at coordinates ``x``. These values will be added in quadrature to the diagonal of the covariance matrix. """

        # Compute the kernel matrix.
        K = self.kernel.get_value(x)
        K[np.diag_indices_from(K)] += yerr ** 2

        # Factor the matrix and compute the log-determinant.
        self._factor = (cholesky(K, overwrite_a=True, lower=False), False)
        self.log_determinant = 2 * np.sum(np.log(np.diag(self._factor[0])))
        self.computed = True