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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, t, yerr=1.123e-12, check_sorted=True, A=None, U=None, V=None):
 """ Compute the extended form of the covariance matrix and factorize Args: x (array[n]):
 The independent coordinates of the data points. This array must be _sorted_ in ascending order. yerr (Optional[float or array[n]]):
 The measurement uncertainties for the data points at coordinates ``x``. These values will be added in quadrature to the diagonal of the covariance matrix. (default: ``1.123e-12``) check_sorted (bool):
 If ``True``, ``x`` will be checked to make sure that it is properly sorted. If ``False``, the coordinates will be assumed to be in the correct order. Raises: ValueError: For un-sorted data or mismatched dimensions. solver.LinAlgError: For non-positive definite matrices. """

        t = np.atleast_1d(t)
        if check_sorted and np.any(np.diff(t) < 0.0):
            raise ValueError("the input coordinates must be sorted")
        if check_sorted and len(t.shape) > 1:
            raise ValueError("dimension mismatch")
        self._t = t
        self._yerr = np.empty_like(self._t)
        self._yerr[:] = yerr
        (alpha_real, beta_real, alpha_complex_real, alpha_complex_imag,
         beta_complex_real, beta_complex_imag) = self.kernel.coefficients
        self._A = np.empty(0) if A is None else A
        self._U = np.empty((0, 0)) if U is None else U
        self._V = np.empty((0, 0)) if V is None else V
        self.solver.compute(
            self.kernel.jitter,
            alpha_real, beta_real,
            alpha_complex_real, alpha_complex_imag,
            beta_complex_real, beta_complex_imag,
            self._A, self._U, self._V,
            t, self._yerr**2
        )
        self.dirty = False