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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 grad_log_likelihood(self, y, quiet=False):
 """ Compute the gradient of the marginalized likelihood The factorized matrix from the previous call to :func:`GP.compute` is used so ``compute`` must be called first. The gradient is taken with respect to the parameters returned by :func:`GP.get_parameter_vector`. This function requires the `autograd <https://github.com/HIPS/autograd>`_ package. Args: y (array[n]):
 The observations at coordinates ``x`` from :func:`GP.compute`. quiet (bool):
 If true, return ``-numpy.inf`` and a gradient vector of zeros for non-positive definite matrices instead of throwing an error. Returns: The gradient of marginalized likelihood with respect to the parameter vector. Raises: ValueError: For mismatched dimensions. solver.LinAlgError: For non-positive definite matrices. """

        if not solver.has_autodiff():
            raise RuntimeError("celerite must be compiled with autodiff "
                               "support to use the gradient methods")

        if not self.kernel.vector_size:
            return self.log_likelihood(y, quiet=quiet), np.empty(0)

        y = self._process_input(y)
        if len(y.shape) > 1:
            raise ValueError("dimension mismatch")
        resid = y - self.mean.get_value(self._t)

        (alpha_real, beta_real, alpha_complex_real, alpha_complex_imag,
         beta_complex_real, beta_complex_imag) = self.kernel.coefficients
        try:
            val, grad = self.solver.grad_log_likelihood(
                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,
                self._t, resid, self._yerr**2
            )
        except solver.LinAlgError:
            if quiet:
                return -np.inf, np.zeros(self.vector_size)
            raise

        if self.kernel._has_coeffs:
            coeffs_jac = self.kernel.get_coeffs_jacobian()
            full_grad = np.dot(coeffs_jac, grad[1:])
        else:
            full_grad = np.zeros(self.kernel.vector_size)
        if self.kernel._has_jitter:
            jitter_jac = self.kernel.get_jitter_jacobian()
            full_grad += jitter_jac * grad[0]

        if self.mean.vector_size:
            self._recompute()
            alpha = self.solver.solve(resid)
            g = self.mean.get_gradient(self._t)
            full_grad = np.append(full_grad, np.dot(g, alpha))

        return val, full_grad